Optimizing Interventions for the Treatment of
M.SSACe.SErs
Vascular Flow Disruptions
M
S INSTME
OF TECHNOLOGY
by
OCT 16 201
Brett Lawrence Boval
LIBRARIES
B.S., Massachusetts Institute of Technology (2012)
Submitted to the Department of Mechanical Engineering
in partial fulfillment of the requirements for the degree of
Master of Science in Mechanical Engineering
at the
MASSACHUSETTS INSTITUTE OF TECHNOLOGY
September 2014
@ Massachusetts Institute of Technology 2014. All rights reserved.
redacted ........
.D......................
A uthor AuthorSignature
Department of Mehanical Engineering
/, ,August 22, 2014
Certified by................
Signature redacted
-.
V7
Elazer R. Edelman
Thomas D. and Virginia W. Cabot Professor
of Health Sciences & Tchnology
Ip19s's pervisor
Certified by................................
Signature redacted
Robert Langer
Professor
aite
Signature redacted~s Reader
.............................
David E. Hardt
Chairman, Department Committee on Graduate Theses
Accepted by ..............................
2
Optimizing Interventions for the Treatment of Vascular Flow
Disruptions
by
Brett Lawrence Boval
Submitted to the Department of Mechanical Engineering
on August 22, 2014, in partial fulfillment of the
requirements for the degree of
Master of Science in Mechanical Engineering
Abstract
All tissues rely on perfusion and therefore intact blood flow. When flow is disrupted the coupled interaction between the functional and fluid domains of a tissue
is impeded and viability is lost. Aortic stenosis is a paradigmatic example of vascular
flow disruption. In health there is no gradient across the aortic valve and pressure
generated in the left ventricle is transmitted without loss to the aorta. Pressure gradients across the valve force the left ventricle pressure to rise accordingly, straining this
proximal pumping chamber, and when it can no longer compensate, affecting distal
blood flow and organ perfusion. Aortic stenosis is common and impactful, highly
morbid and when symptomatic almost inevitably fatal within a few years.
New interventions to address vascular flow disruptions, such as transcatheter aortic valve replacements (TAVR), have significantly improved quality of life and life
expectancy. However, these emerging technologies involve unique delivery modes
and anchoring mechanisms that require the prosthesis to intimately interact with the
aortic wall. As aortic stenosis is marked by asymmetric and inhomogeneous accumulations of calcium there is the potential for suboptimal alignment of the valve.
A framework is presented to account for valve-tissue interactions in the setting of
such asymmetric deposits. We compared paired pre and post procedural computed
tomographic (CT) scans of patients who had severe adverse events with TAVR. A
range of scenarios are considered and two patient cases are presented - with one normal deployment and the other with device buckling failure. The work highlights the
ongoing need for better understanding of how calcium distribution in the aortic root
environment affects a patient's susceptibility to clinical complications.
3
Thesis Supervisor: Elazer R. Edelman
Title: Thomas D. and Virginia W. Cabot Professor
of Health Sciences & Technology
Thesis Reader: Robert Langer
Title: Institute Professor
4
Acknowledgments
Professor Elazer Edelman was a constant source of support and wisdom.
He
willingness to always find the time to talk was exceptional. He allowed me to explore
broadly, improve academically, and grow personally all while making sure that I
focused on questions of merit. Professor Marco Costa was always willing to share
his clinical wisdom and it was greatly appreciated. I wish him safe sailing on his
adventures. I would also like to thank Professor Robert Langer for agreeing to be a
thesis reader.
I am lucky to have the company of so many interesting and intelligent coworkers.
Jon Brown helped make my time in the lab successful in myriad ways. We worked
together on many projects, and he was there to discuss anything that was needed.
Dr.
Claire Conway helped immensely with the creation and organization of this
document. Her ability to understand my impressionist thoughts and extract clarity is
an impressive feat. Dr. Kumaran Kolandaivelu was there to lend a critical eye to any
scientific question and recommend a plan of action. The dedication and love Denis
Kramarenko showed to his experimental setup set an expectation that was impossible
to meet. The humor and wit of Sina Omran and Dr. Shimon Unterman was always
appreciated and Ben Leiden, Or Gadish, Maria Pont Torres, Sivan Selliktar, and
Luccie Wo devoted great time and effort. Drs. Ramon Partida, Kay Everett, Caroline
O'Brien, and Celso Lopes always led by example and created a work environment that
was both productive and enjoyable. Melissa St-Pierre and Kim LePage managed the
administration of a busy multi-disciplinary lab with grace.
Finally, I would like to thank my parents and my brother for their support through
the years. Even after thousands of hours of schooling and swim meets, they are still
my biggest fans.
5
6
Contents
1
Clinical Motivation & Literature Review
13
1.1
Overview .......
13
1.2
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
14
1.2.1
16
1.3
1.4
2
Scope of Project .......
.........................
Clinical M otivation . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
1.3.1
Aortic Stenosis Patient Population and Natural History . . . .
17
1.3.2
Changing Patient Population
. . . . . . . . . . . . . . . . . .
21
1.3.3
The Energetic Cost of Aortic Stenosis . . . . . . . . . . . . . .
22
1.3.4
Current Challenges to Be Solved
. . . . . . . . . . . . . . . .
25
1.3.5
Value of Additional Studies
. . . . . . . . . . . . . . . . . . .
26
Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
28
1.4.1
M odes of Failure
. . . . . . . . . . . . . . . . . . . . . . . . .
28
1.4.2
Failure Mechanisms . . . . . . . . . . . . . . . . . . . . . . . .
34
1.4.3
Failure M odels
35
. . . . . . . . . . . . . . . . . . . . . . . . . .
Methodology
2.1
3
...................................
39
Overview - Medical Image Computing . . . . . . . . . . . . . . . . . .
39
2.1.1
Image Reconstruction . . . . . . . . . . . . . . . . . . . . . . .
41
2.1.2
Model Generation and Fiduciary Selection . . . . . . . . . . .
48
2.1.3
Project Files. . . . . . . . . . . . . . . . . . . . . . . . . . . .
53
55
Results
3.1
Clinical Data
-
Patient Dataset
. . . . . . . . . . . . . . . . . . . . .
7
55
3.1.2
Normal Deployment
61
3.1.3
Abnormal Deployment ...........................
.
57
.......................
65
Discussion
4.4
. . . . . . .
69
4.1.1
Wide Range of Stable Valve Deformation . . . .
. . . . . . .
69
4.1.2
Complex Boundary Conditions
. . . . . . .
70
4.1.3
Understanding Calcium is a Critical Unmet Need . . . . . . .
70
.
.
. . . . . . . . . . . . . . . . . . . . .
.
. . . . . . .
72
4.2.1
Large Variance in Follow Up Interval . . . . . .
. . . . . . .
72
4.2.2
Motion Artifacts - Older Scans
. . . . . . . . .
. . . . . . .
72
4.2.3
Small Sample Size
. . . . . . . . . . . . . . . .
. . . . . . .
72
Justification of Approach . . . . . . . . . . . . . . . . .
. . . . . . .
73
4.3.1
Need for Incorporating Unstructured Data . . .
. . . . . . .
73
4.3.2
Need for Understanding of Device Movement . .
. . . . . . .
74
4.3.3
Need for Immediate Clinical Applicability
. . .
. . . . . . .
76
Ongoing Work . . . . . . . . . . . . . . . . . . . . . . .
. . . . . . .
78
4.4.1
Improved Calcium Risk Score . . . . . . . . . .
. . . . . . .
78
4.4.2
Correlating Reaction Forces with Conduction Risk . . . . . . .
80
4.4.3
Transcatheter Mitral Valve Replacements . . . .
81
.
. . . . . . . . . . . . . . . . . . . . . . . .
.
.
.
.
.
.
Limitations
. . . . . . . . .
.
4.3
Findings of Note
.
4.2
69
.
4.1
5
Patient Population . . . . . . . . . . . . . . . . . . . . . . .
.
4
3.1.1
Conclusion
. . . . . . .
83
A
95
A. 1 Clinical Variables ....
.............................
8
95
List of Figures
Aortic Root . . . . . . . . . . . . . . .
. . . . . . . . .
18
1-2
Natural History of Aortic Stenosis . . .
. . . . . . . . .
19
1-3 Visual Comparison of Existing Valves .
. . . . . . . . .
20
1-4
Types of Aortic Valve Stenosis . . . . .
. . . . . . . . .
22
1-5
Calcific Aortic Valve
. . . . . . . . . .
. . . . . . . . .
23
1-6
Mitral Valve Replacements . . . . . . .
. . . . . . . . .
26
1-7
Conduction Pathway Near Aortic Valve
. . . . . . . . .
30
1-8
Conduction Pathway Near Aortic Valve
. . . . . . . . .
31
1-9
Lit Review Overview . . ... . . . . . .
. . . . . . . . .
38
2-1
Overall Methods Workflow . . . . . . . . . . .
. . . .
40
2-2
Image Segmentation General Outline . . . . .
. . . .
42
2-3
Aortic Cross Section View Highlighting Calcium Deposits
. . . .
43
2-4
Extracted Valve . . . . . . . . . . . . . . . . .
. . . .
43
2-5
Automated 3D Modeling Pre-Procedural Reconstruction
. . . .
45
2-6
Calcium Segmentation Workflow . . . . . . . . . . . . .
. . . .
46
2-7
Solid Body Model of 29mm Stented Valve Graft . . . .
. . . .
48
2-8
Selection of the Fiduciary Points
. . . . . . . . . . . .
. . . .
50
2-9
Patient Specific Analysis of Valve Deformation . . . . .
. . . .
51
2-10 Extracted Displacement Vectors for Proximal Plane . .
. . . .
53
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
1-1
Elapsed Time Between Imaging . . . . . . . . . . . . . . . . .
56
3-2
Age Distribution of Patients . . . . . . . . . . . . . . . . . . .
58
3-3
New York Heart Association (NYHA) Functional Classification.
59
.
.
3-1
9
3-4
Aortic Insufficiency . . . . . . . . . . . . . . . . . . . . . . . . . . . .
60
3-5
Segmentation of Valve and Inner Lumen . . . . . . . . . . . . . . . .
61
3-6
Rigid Body Rotation to Fit Patient Specific Normal Deployment . . .
63
3-7
A Normal Deployment . . . . . . . . . . . . . . . . . . . . . . . . . .
64
3-8
Segmentation of Calcium and Inner Lumen . . . . . . . . . . . . . . .
65
3-9
Rigid Body Rotation to Fit Patient Specific Abnormal Deformation .
67
3-10 An Abnormal Deployment . . . . . . . . . . . . . . . . . . . . . . . .
68
4-1
Stats Framework
. . . . . . . . . . . . . . . . . . . . . . . . . . . . .
75
4-2
Evaluating Clinical Intuition . . . . . . . . . . . . . . . . . . . . . . .
79
4-3
Preliminary FEA . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
80
A-1 Clinical Data Available . . . . . . . . . . . . . . . . . . . . . . . . . .
95
10
List of Tables
3.1
Complication Rates. . . ..
. . . . . . . . . . . . . . . . . . . . . . .
11
58
12
Chapter 1
Clinical Motivation
& Literature Review
1.1
Overview
Collaborations to investigate complex, chronic diseases require clinical, academic,
and industrial input. In support of this collaboration, a general framework is presented to investigate mechanical interventions for the treatment of valvular flow disruptions. In specific, the interaction between calcific aortic valves and valve replacement prostheses is examined. This thesis is focused on the image reconstruction,
feature extraction and data analysis of patient imaging modalities in support of the
analysis of device-tissue interactions.
A review of the literature is presented to contextualize the clinical and engineering
work that has been done to understand aortic stenosis. The methodology section explains the framework created to support this broad proposal. This framework is then
applied to two test cases of patient imaging and clinical data collected by Professor
Marco Costa of Case Western Reserve University from 2008 to 2011. The results of
this application and the progress made on the creation of long-term infrastructure
are presented. Finally, the study concludes with a discussion of the importance of the
framework, its limitations, and promising ongoing and future work.
The thesis overview, problem statement, and proposed solution are presented in
13
the first part of this chapter. The second half consists of the clinical motivation and
the review of the literature.
1.2
Introduction
The Harvard-MIT Biomedical Engineering Center is interested in creating new
paradigms to understand complex diseases. These undertakings require expertise in
many fields and an understanding of all aspects of the interactions between diverse
stakeholders. By understanding the mechanisms of disease and disease progression,
we can understand when to treat, how to treat, and where to expect improvements
in future care.
For chronic diseases, the scope and scale of combinations and permutations available for treatment makes it impossible to intervene intuitively. A patient's response
to treatment is dependent not only on their current clinical presentation, but also on
the past states and the rate of progression. Since the patient's current state is not
independent of the path that lead to the state, the "memory" of disease progression
must be captured. This memory requirement creates a much larger state space than
can be easily grasped by a single clinician. Even when summing up the experiences
of all clinicians, there are still too many possible states to empirically sample with
adequate coverage. There is therefore value in being able to understand these complex environments and reduce the feature space by understanding what mechanisms
dominate the progression of the disease. Dominant motifs in pathophysiology enable
clinicians to apply general knowledge to the treatment of new specific patients.
Work in the field of coronary atherosclerosis has shown the benefits of an integrated
approach. Understanding the biology, hemodynamics, and mechanical properties of
atherosclerotic plaques has opened new medical management, and new interventions.
Percutaneous coronary interventions (PCI) to treat plaques have taught us the benefit
of understanding the coupled interactions between the disease environment and device
specific characteristics.
Forces, fluids and flows determine the progression of the
disease. It is therefore important to understand these interventions in those terms.
14
The approaches taken to understand PCI can be applied to similar interventions to
treat obstructions of heart valves.
Aortic stenosis is an important example of complex chronic disease that is both
common in the population and highly variable in its presentation. Fundamental research into the mechanisms behind obstruction creation, growth, and acute incidence
have improved preventative care, interventional efficacy, and long term outcomes.
More improvements can, and should, be realized.
The overarching goal is to understand what to assess when optimizing interventions for the replacement of cardiac components. Investigating the aortic valve, and
particularly aortic stenosis, is important for three reasons. First, the clinical need
is high.
Second, technological improvements have allowed for new approaches to
treatment that have improved care but are still fluid. Third, clinical experience is
insufficient to have created a large enough clinical database. To summarize, the disease is complicated enough, and the treatment suboptimal enough, to justify further
study.
General Proposal
A multidisciplinary team is required to understand a problem
such as aortic stenosis. Bringing together a consortium of interested stakeholders
including clinical, academic and industry leaders is a difficult, but necessary task in
order to create solutions that have an impact on patient care. Understanding aortic
stenosis and the interactions between devices and stenotic valves is a question of merit
that provides value to all three parties. Clinicians need guidance in understanding who
to treat, when to treat, and what level of procedural risk to expect. Industry needs
to better understand the new design constraints that come with deploying devices
non-invasively in patients that previously were not surgical candidates. Finally, the
clinician-researchers can understand how to fundamentally characterize the variability
in patients who have stenosis of heart valves and understand the progression of health
and disease in the general population. A significant part of the broad project will be
working to bring all these diverse interests into a mutually beneficially structure.
15
1.2.1
Scope of Project
General:
For chronic diseases, the scale and scope of combinations and permuta-
tions available for treatment makes it impossible to intervene intuitively and there
exist too many possible states to empirically sample with adequate coverage.
Specific:
When dealing with impediments and obstructions to vascular flow, in-
tervention cannot be optimized through patient-specific anatomy alone nor through
device-specific properties alone. Models incorporating forces, fluids, and flows are
necessary to understand knowledge gained from preclinical and clinical experience.
Test Case:
Aortic stenosis provides the optimal testbed for this paradigm. Aor-
tic stenosis is a dynamic disease involving many tightly coupled elements. TAVR
intervention is still in early stage where the designs are still fluid and the clinical experience is insufficient to have a large enough clinical database to know how to treat
and which approach to select. The disease is complicated enough, and the treatment
suboptimal enough, to justify further study.
Focus:
The following methods and results address the engineering need to collect,
organize, annotate, and extract relevant features from patient clinical imaging. The
ability to extract meaning from unstructured data, such as CT imaging, is an important aspect of intervention analysis. The interaction between replacement valve and
native tissue is significantly different in TAVR than traditional surgery. The work
presented attempts two distinct goals. The first is to provide a framework for incorporating CT image analysis into the study of TAVR and the device-anatomy interaction
in a way that can support further in-depth structural and fluid analysis as warranted.
The second is to use this framework on a small dataset of patient imaging to generate
testable hypotheses that can be tested, validated and applied retrospectively to the
large US randomized control trials of TAVR and prospectively to new patients.
16
1.3
Clinical Motivation
Heart failure is the inability of the cardiovascular system to meet the metabolic
demands of the body. This project focuses on aortic stenosis, a type of structural
heart failure where the narrowing of the aortic valve by calcific plaques requires the
heart to expend significantly more energy to maintain the same end organ perfusion
pressure. TAVR attempts to correct this structural defect in a minimally invasive
way. Promising results have been shown, and two products are approved for human
use in the United States." These interventions have shown improvements in both
quality of life and mortality with a cost effectiveness similar to other cardiovascular
technologies.4-7
However, significant procedural complications still occur and the mechanisms underlying these severe adverse events require more study. This thesis presents a framework to analyze patients who have had severe adverse events after undergoing TAVR.
It combines image analysis of cardiac CT scans in conjunction with knowledge of device design and mechanics. The aim is to probe the mechanical interactions between
the prosthesis and surrounding tissue to better understand how the device impacts
the performance of the heart, and how the heart impacts the performance of the
device.
1.3.1
Aortic Stenosis Patient Population and Natural History
Aortic stenosis is the narrowing of the valve that connects the principal pumping
chamber of the heart (the left ventricle) with the principal artery to the body (the
aorta). The narrowed orifice leads to a larger blood pressure drop across the valve. As
a consequence, the heart must expend more energy to maintain adequate perfusion
pressure to the brain, kidneys and other end organs . Historical studies of patients
with asymptomatic aortic stenosis show very low mortality as the heart compensates
for the constriction (Figure 1-2). Longitudinal studies such as the Framingham Heart
Study and the Helsinki Ageing Study have looked at the prevalence of valve disorders
in the general population. Lindroos et al. found in 1993 that more than 13% of the
17
500 randomly sampled patients over the age of 75 had severe calcification of the aortic
valve and 40% had mild calcification. 8 They concluded that many of these patients
should be candidates for surgery, but primary care physicians were not recommending
the patients for cardiac screening.
(b) Aortogram showing the aortic root.
(a) Drawing of the aortic root.
Figure 1-1: The aortic root with the right and left coronary arteries. Taken from
Cheung (2012).9
More recent work has shown that one third of patients with severe symptomatic
aortic stenosis do not undergo valve replacement.
10
The majority of the untreated
group was no even evaluated by a cardiac surgeon and retrospective analysis showed
that they did not have prohibitively high operative risk. These patients are seen
by primary care physicians as coping with the disease. However, in the long run
these changes become maladaptive and lead to worsening symptoms including angina
(chest pain), syncope (fainting), edema (the release of water from the vasculature
to the surrounding tissue), and eventually death. The onset of severe symptoms
represents a relatively late time point in the progression of the disease, but the drop
off in survival is steep. Median prognosis from onset of heart failure is dire without
treatment, with median life expectancy of one year.
Work has been done to understand the pathobiology of valvular tissue in order
18
Onset of severe symptoms
100-
Angina
80
0Latent period
(increasing obstruction,
6o-
3
Syncope
-
Failure
6
4
2
0
Average survival (years)
overload)
Smyocardial
-
40Average death
(age)
20 _
0
0
10
20
30
40
50
60
70
80
Age (years)
Figure 1-2: Ross and Braunwald tracked the natural history of patients with Aortic
Stenosis before any treatments were available. The chart shows a simplified survival
function. The percent survival of a cohort is plotted as a function of the age in years
of the cohort. The survival function is always monotonically decreasing as patients
in the tracked cohort die. that captures the two key characteristics of patients with
aortic stenosis. Taken from Carabello (2009)." Originally from Ross and Braunwald
(1968).
to prevent or retard the calcification of the aortic tissue, but no current therapies
exist to treat the tissue." Therefore, open heart surgical replacement has been the
gold standard of treatment for symptomatic aortic stenosis. However, many patients
are too frail or have co-morbidities that make them high risk candidates for surgery.
Across the field of medicine, less invasive medical interventions have found favor in
recent years as a way to reach previously untreatable patients and to improve recovery
time. For example, the use of stents to open blocked arteries can delay or negate the
need to circumvent the whole artery, as is done in coronary artery bypass graft surgery
(CABG). In a similar way, collapsable valves can be delivered via catheters traveling
retrograde through the cardiovascular system. The new valves is expanded directly
across the old valve and uses the tissue from the original valve to anchor itself in
place. An introductory video highlighting the transcatheter aortic valve procedure
can be found here (http://youtu.be/KArINj4TFwQ).
Animal studies were conducted in the 1990's and in 2002 the first percutaneous
prosthetic heart valve procedure was performed as a compassionate use case for a
19
a
Figure 1-3: The SAPIEN (a-c) and the Corevalve (d-f) are class III FDA approved
medical devices. (b) and (d) show how both use radial expansion to anchor the valve
in place. (c) and (f) show real fluoroscopic images of valves deployed in the aortic
valve. Taken from Van der Boon (2012).4
patient with no other options.1 5 16-18 This early stage proof of concept was successfully
implanted, but the patient died of other co-morbidities 17 weeks later. Many valves
have been placed since then. There are two valves approved for human use in the
United States as of August 2014 (Figure 1-3). Both are Class III medical devices that
went through lengthly pre-market approval processes for certification.
The SAPIEN valve (panels 1-3(a)-(c)) was commercialized by Edwards Lifesciences
and was the first to be available in the US in November 2011. It is a balloon expandable valve prosthesis where the height to width aspect ratio is low and the valve is
placed only across the annulus. The metal frame is made of 316L biomedical grade
stainless steel with a de-cellularized tissue valve made of bovine pericardium. The
valve is wrapped around a catheter balloon and that balloon is inflated in vivo to
expand the valve to the correct diameter.
The Corevalve (panels 1-3(d)-(f)) was commercialized by Medtronic and gained
FDA approval in January 2014. It features a much larger height to width ratio than
the balloon expandable valve. Another difference is the location of the replacement
leaflets. The longer graft valves allow the leaflets to reside above the original valve
in the aortic root space. This device is an example of a stented valve graft that is
20
manufactured with a cage made of a nickel titanium alloy and a tissue valve made
of porcine pericardium. The metal cage is made of super elastic shape memory alloy
that is shape set into a naturally opened state. Before the procedure, the valve is
radially crimped and placed inside a constraining sheath. As the sheath is slowly
released, the valve tries to re-expand to the original geometry. Clinical work has been
done recently to compare the efficacy of the two valve types head to head, but no
19 2 0
valve has been shown to be clearly superior. ,
1.3.2
Changing Patient Population
The three main etiologies of aortic stenosis are rheumatic, calcific, and bicuspid
(Figure 1-4). Rheumatic stenosis is a long term sequela of rheumatic fever. Rheumatic
fever is an inflammatory disease that occurs when a strep throat infection goes untreated. Rheumatic stenosis was once the dominant form of aortic stenosis in the
United States, but incidence of rheumatic fever has dropped dramatically with the
introduction of antibiotics for the treatment of streptococcus infections.
It is still
common in the developing world. Calcific aortic stenosis involves the ossification of
leaflet tissue and the subsequent reduction in leaflet mobility and ability to coapt.
Finally, bicuspid aortic valves are a common genetic abnormality where two of the
leaflets are fused together. Patients with bicuspid valves show a much higher propensity to acquire a stenosed valve and at a much younger age.
The original Ross and Braunwald curves were created from natural history studies
of people who were younger and predominantly had a different type of aortic stenosis
than the current patient population of TAVR. The study was predominantly following
patients that had rheumatic stenosis or a bicuspid leaflet. On the other hand, the
TAVR patient population has been much older and predominately calcific stenosis of
normal tricuspid leaflets. In fact, the TAVR devices rely on the presence of calcium
to firmly anchoring the device in the correct location across the valve. If there is
no calcium, the devices can not currently be properly secured.
The difference in
these two patient populations means that comparisons to historical precedence may
no longer be appropriate in the understanding of aortic stenosis.
21
Normal
Calcific
Rheumatic
Bicuspid
Figure 1-4: Types of aortic valve stenosis. The two small gray ellipses represent the
coronary artery ostia and the central gray shape represents the valve orifice. The top
row valves in diastole (closed) and the bottom row shows valves in systole (open).
Taken from Baumgartner (2009).
1.3.3
The Energetic Cost of Aortic Stenosis
The narrowing of the aortic valve has a dramatic effect on the energy demands and
efficiency of the cardiovascular system. One way to understand the demand is to look
at the transvalvular pressure gradient (TPG) across the valve in the normal direction
of flow. Ideally there is negligible pressure drop as the blood immediately leaves the
left ventricle. However, a narrowed orifice increases this pressure drop. Since the
aortic valve is upstream of all other arterial components, this pressure drop affects
the perfusion pressure for all the rest of the arterial tree. Mechanisms to compensate
for decreased end organ perfusion pressure are more energetically wasteful. As the
heart struggles to meet demand it becomes more fragile with respect to additional
insults. Therefore, estimating the transvalvular pressure gradient is an important
step in understanding the pathophysiology of aortic stenosis.
A back of the envelope approach would be to look at Bernoulli's equations. The
simple Bernoulli approximation yields TPGet
=
p
where p is the density of
the fluid, Q is the flow rate, and EOA is the effective orifice area of the central jet
of the flow. Garcia et al. has looked at ways to improve upon those approximations
with a combination analytic and empirical solutions.2 2 More complicated analytic
22
solutions yield a systolic mean TPG as TPGet = EL = jpQ 2 (OA
instantaneous TPG as TPGet = 27rpj
Et
- +A 2(EOA
2where
-
)2 and an
A is
the cross-sectional area of the inlet and outlet (which for simplicity are considered
equal).
FRom the previous equations it is clear that TPG oc
1 2.
EQA
Therefore cutting the
valve orifice in half leads to a four time increase in pressure drop. Since perfusion
pressure to end organs must be maintained, a small decrease in orifice area leads to
a large increase in the required pressure that must be generated in the left ventricle
to compensate for the pressure drop across the valve. This strong dependence helps
to explain the high morbidity and mortality costs of aortic stenosis.
Figure 1-5: A calcific aortic valve. The calcium distribution is highly asymmetric and
inter-patient variability is high.
An Assumption of Symmetry
The empirical and analytic simplifications presented above treat the problem as
an axially symmetric orifice. Clinicians also retain this assumption of symmetry.
Normal cardiovascular flow and even rheumatic fever and bicuspid valves maintain
a strong regularity to the shape of the flow orifice. However, the calcification of the
aortic valve imposes a strong asymmetry to the orifice shape and flow (Figure 1-5).
More importantly, the pattern of this asymmetry is highly variable across patients.
The location of the calcium and the inhomogeneity of the distribution are crucial
23
in understanding how the patient will respond to treatment.
Therefore, while an
assumption of symmetry is fine with simple gradient analysis, it is not sufficient for
more complicated modeling.
The calcification of cardiovascular tissue was originally perceived as a passive
degeneration of the valve. However, research into the pathophysiology of vascular
calcium ties it closely to active biological processes, not just a passive degeneration
of the tissue.2 3
24
1.3.4
Current Challenges to Be Solved
The anatomy of the aortic root is crucial to the success of TAVR and the relative
importance of features changes with percutaneous approaches. 2 4 Open heart surgery
allows direct, extended access to the valve environment. This allows the surgeon to
stitch in the replacement valve and make sure that the valve is correctly placed and
that there are no leaks around the valve. In exchange for a much less traumatic access
route, transcatheter delivery systems currently do not have the capability to place
sutures around the valve. Therefore, doctors have less control in locating the valve
and the only anchoring is provided by the radial force of the constrained valve trying
to expand to the full shape. Inadequate anchoring can lead to device migration into
the left ventricle or the aorta. The calcium buildup that originally caused the valve
to fail is still present and can lead to asymmetrical under-expansion of the valve with
gaps between the valve and the wall. These sealing problems have been shown to
"
significantly decrease life expectancy.2
Many variables go into the pre-procedural assessment of risk and benefit for the
patient. Doctors are interested in these cases in order to improve their ability to
predict the difficulty of a given patient based on anatomical and procedural variables.
Both the balloon expandable and self expanding valves have been shown in clinical
trials to be as efficacious as surgery for high-risk patients. Nevertheless, severe adverse
events continue to occur at significant rates. In the key US trials for both devices,
more than 10% of patients experienced moderate or severe leakage around the valve,
called aortic regurgitation (AR).1 In addition, the Corevalve Pivotal US trial had a
pacemaker implantation rate of 22.4%.26 Room for optimization include more appropriate patient selection, better guidance for clinicians regarding optimal delivery
location, and design improvements in sealing for next generation devices that do not
solely depend on radial forces. The ability to understand the mechanisms behind
these complications will allow clinicians and engineers to create countermeasures to
mitigate those risk.
In addition, multiple companies are in early stage development of products that
25
replace the mitral valve in a similar way. The geometry of the mitral valve is significantly different and the asymmetry present in calcific aortic stenosis is even more
important when dealing with the mitral valve (Figure 1-6). It is larger, more asymmetric, has fewer anchoring places and requires the precise interaction of multiple
parts to maintain valve function. The cyclic dynamics of the valve are also much
more complicated and there exists much smaller clinical knowledge about the procedure. At the time of this study, fewer than 10 transcatheter mitral valve replacement
procedures have been publicly documented. For these new products, the ability to
predict future complications is crucial for the continued development of the devices
and eventual submission to regulatory bodies.
Future Mitral Valve Replacements
Aortic Valve
Mitral Valve
Figure 1-6: At least four companies (Edwards Lifesciences, Neovasc, CardiAQ,
Medtronic) are developing transcatheter valves to be placed in the mitral valve. These
valves have taken much longer to be developed due to more complex geometry, less
areas for anchoring, and higher cyclic deformation.
1.3.5
Value of Additional Studies
Many retrospective studies have been completed analyzing the associative links
between patient baseline data, procedural measurements, and long term clinical outcomes. These studies have greatly assisted the field in finding the strongest correlations between inputs and outcomes.
However, these are ensemble averages and
inherently correlative. They work well for studies with large sample sizes and com26
mon events.2 7 By mechanistically studying the device-environment iterations, guiding
principles can be extracted to support both clinicians and designers to improve current
and future procedures.2 8 The move towards integrated finite element analysis (FEA)
early in the medical device design pipeline is being strongly encouraged by the FDA
and beginning to see adoption by the field at large. Methods have been described
applying FEA on patient specific imaging from TAVR, but the understanding of how
these valves interact with their environment in a real life patient setting is lacking.
The analysis of pre and post CT with contrast of patients with severe adverse events
is different from currently reported work and can provide valuable information about
the valve-environment interactions.
27
1.4
1.4.1
Literature Review
Modes of Failure
The Valve Academic Research Consortium (VARC) has standardized the definitions of TAVR clinical endpoints and definitions of complications. These definition
have been implemented to allow for comparisons between different clinical trials and
registries. 29 The major severe adverse events in the procedure axe stroke, ischemia,
bleeding/vascular access problems, aortic root rupture, acute kidney injury (AKI),
conduction disturbances, and paravalvular leakage.3 0
Acute Kidney Injury
AKI has long been associated with cardiac surgery, and can be instigated by
ischemic events, increased inflammatory state, and nephrotoxic chemicals such as CT
contrast.3 1 AKI occurred in 11.8% of the patients in the US PIVOTAL trial.2 In
a Columbia University Medical Center study, significant AKI occurred in 8.3% of
patients and correlated with very poor prognosis. The severe AKI group experienced
a one year mortality rate of 55.6% compared to the non-AKI rate of 16.0%.32
Stroke
Cerebrovascular events can be caused by the dislodgment of aortic calcium and
subsequent embolization to the brain. Rates of stroke in clinical trials range from
7-10%.1,2 This number may underestimate the actual incidence of stroke by missing
more subtle, but still important, manifestations.
Recent work has accessed stroke risk in surgical valve patients by having patients
evaluated by a neurologist before and after the procedure and then subsequently given
post operative magnetic resonance imaging (MRI). They showed higher rates of stroke
than normally reported and found that 54% of patients had clinically silent strokes.
While the surgical and transcatheter approaches have different stroke risk, there is
a need for better cerebrovascular protection in both procedures and highlights the
risk of missed, but significant, cognitive impairment.33 Work is ongoing to design
28
methods and devices to reduce the amount of dislodged calcium and to prevent its
delivery to the brain. In addition, new arrhythmias can promote emboli formation and
subsequent embolization making conduction disturbances an independent predictor
of cerebrovascular events 1-60 days post-procedure. 34
Myocardial Infarction
TAVR may disrupt the flow of blood to the coronary arteries by obstructing the
coronary ostia in the sinus of Valsalva. This obstruction can lead to transient ischemia
or develop into full myocardial infarction. Infarctions are procedurally rare (~2% at
12 months) but are more prevalent with the self expanding valve than the balloon
expandable valve.1' 2 The stented valve graft projects much higher into the aortic
bulb, and therefore has more opportunity to interrupt coronary inlet flow.
Vascular Access and Bleeding
The transfemoral approach requires an arterial system large enough to fit the
catheter and free of obstructions that could cause the catheter to catch and perforate
the artery. However, patients that have calcific valves also have a higher incidence
of calcified arteries. In the PARTNERS trial 15.3% of patients had major vascular
complications using the VARC definitions."
Sicker patients tend to have higher
vascular access problems, and the use of larger catheters increases complications. As
procedures move to 2nd and 3rd generation delivery systems with smaller catheter
profiles and as lower risk patients are treated, the incidence of vascular complications
is expected to drop significantly.
Aortic Root Rupture
The valve can also perforate the cardiac tissue. The incidence is low (1.3% and
1.8% in US PIVOTAL trials), but the consequences of aortic root rupture are severe.2, 36 This failure mode is present in both self expanding and balloon expandable
valves.
29
Conduction Disturbances
The aortic valve is centrally located in the heart. The tissue surrounding the
valve is heavily enervated with conducting fibers that synchronize the contractions of
the upper half of the heart (right and left atrium) with the lower half of the heart
(right and left ventricle) (Figure 1-7 and 1-8).
The introduction of a rigid metal
tube into this environment can lead to conduction blocks and arrhythmias. These
conduction disturbances can be transient or permanent and can be masked by an
imposed pacemaker waveform. It is important to note that some patients have preexisting conduction disturbances and may already have pacemakers.
aortic leallet
Left bundle
branch
Figure 1-7: Conduction pathway near the aortic valve. The prosthesis landing zone
interacts with the membraneous septum and the left bundle branch. Taken from
Piazza (2008).2 Originally from Tawara (1906).
Arrhythmias are common in the pre-TAVR patient population with 41.0% of the
intended to treat population having prior atrial fibrillation or atrial flutter and 23.4%
having a preexisting pacemaker or defibrillator.
6
These conduction disturbances
may be detected via routine electrocardiogram before the patient is symptomatic,
or a decline in functional state may signal to the physician to run a cardiac study.
During the TAVR procedure conduction disturbances can be assessed via realtime
electrocardiographic monitoring, via visual inspection of fluoroscopic images, and via
the effect on global vitals such as heart rate and blood pressure.
High rates of conduction blocks, such as new onset left bundle branch blocks
30
A
STJ --
RfT
B
L
R
N
L
VA4..
SMV
C
Figure 1-8: Conduction pathway near the aortic valve. Figure A shows a healthy
aortic root. Figure B shows a diseased valve where the tissue has contracted and
the leaflet coaptation length has been reduced. Figure C shows a stented valve graft
deployed in the aortic root. The distal (bottom) portion of the device interacts with
the membraneous septum (MS) and the left bundle branch (LBB) of the conduction
pathway. The prosthesis landing zone interacts with the membraneous septum and
the conduction pathway. STJ is the sinotubular junction. MV is the mitral valve.
VS is the ventricular septum. RFT and LFT are the right and left fibrous trigones.
Taken from Khawaja (2011).4
31
(LBBB), have been seen in both self expanding and balloon expandable valves."
After excluding patients with previous pacemaker or procedurally placed pacemaker,
wide ranges of persistent LBBB have been reported (10.5%, 27.4%, 34.3%).38-40 There
is also variability in the amount of patients who recover from procedural LBBB. In
one trial more than 29% of patients had LBBB and of those patients, half of the disturbances persisted for more than one month.4 ' This variance shows the importance
of device type and clinical procedure on the incidence of conduction blocks. The perceived clinical effect of LBBB depends on the procedural methodology and exclusion
criteria of the study, with most showing an increased need for later pacemaker implantation, some reports showing poorer left ventricle ejection fraction recovery, and
mixed reports about whether LBBB increases risk of death.
While current balloon expandable valves and self expanding valves are similar in
most clinical outcome parameters, pacemaker implantation rate was much higher for
the self expanding valve than the balloon expandle valve (22% compared to 3.8%).
The pacemaker rate is highly dependent on how aggressively conduction disturbances
are treated during procedures. Aggressive treatment of conduction disturbances lead
to pacemaker implants as high as 33%.44 Monitoring the patient and waiting longer
to implant the pacemaker has reduced the pacemaker implantation rate in valve graft
procedures significantly.
Paravalvular Leakage
Paravalvular leakage is the flow around the prosthesis that occurs when the metal
frame of the device is not well apposed with the surrounding tissue. It is fundamentally an inability of the device to coapt to a complex boundary that is strongly
asymmetric (geometry) and strongly anisotropic (mechanics). Paravalvular leakage is
detected via Doppler echocardiography before, during, and after procedures. 4 Estimates of retrograde flow across the valve can be estimated by integrating the velocity
flow field through the surface defined by the aortic annulus. This type of leakage is
by definition not central, but the number of jets, size of jets, and orientation of jets
are characterized.
32
Paravalvular leakage is a common occurrence after procedures. 10.5% of patients
had moderate or sever paravalvular leakage at one year in the balloon expandable
prosthesis PARTNERS A trial.' In the 2014 US PIVOTAL trial, patients given the
self-expanding stented valve graft prosthesis had a lower rate of moderate or severe
leakage at 4.3% This decrease represents a new appreciation for the importance of
minimizing paravalvular leakage. The US PIVOTAL clinical trial report mentions a
new focus on actively reducing paravalvular leakage at discharge by more liberally
employing post-dilation to improve device-annulus sealing.2 Much clinical work has
been done to understand the morbidity and mortality effects of paravalvular leakage
on TAVR patients.45 ,46 The effect on mortality is quite pronounced. Patients with
moderate to severe paravalvular leakage are ten times more likely to respond poorly
to TAVR (either death or New York Heart Association function score of 2+).25
33
1.4.2
Failure Mechanisms
Implantation Depth
There axe limited degrees of freedom when deploying a valve via a catheter. The
length of catheter deployed into the femoral artery sets the implantation depth. During the early use of the stented valve graft, the clinicians were worried about device
migration and supra-annular deployment. Therefore, clinicians erred on the side of
deeper implantation for better anchoring.
The deeper implantation does prevent
valve embolization, but has also been strongly associated with increased rates of conduction disturbances and increased paravalvular leakage. The proposed mechanism
linking conduction block with implantation depth is the mechanical impingement of
the conduction pathway running through the membraneous septum. 41,47 Deeper implantation leads to a greater contact force between the device and conduction network.
Greater implant depth also increases interaction with subvalvular calcium, which has
been showing to increase asymmetric deployment and paravalvular leakage. 34
Calcium Location
The extent and distribution of calcium is believed to be a key factor in many
TAVR related complications.3 4
45 ' 46
Bulk calcium burden and the amount of asym-
metry have been shown to increase the risk of stroke, vascular access complications,
aortic root rupture, conduction blocks, and paravalvular leakage. The calcium interacts through many mechanisms. Calcium dislodged during the procedure provides
embolization material and increases stroke risk. Aortic calcium induces vascular tortuosity and complicates catheter access. Subvalvular calcium in the left ventricular
outflow track creates more pressure on the membraneous septum, creates asymmetry
in valve deployment, and promotes paravalvular leakage. The necessity of dealing with
the asymmetric and inhomogeneous native calcium environment is one of the major
differences between surgical and transcatheter valves. The amount and distribution
of valvular calcium has been shown to predict location and severity of paravalvular
leakage. 48 ,49 The location, not just the quantity, of the valvular calcium (on the leaflet
34
edge compared to the wall) also effects the risk of aortic rupture, with calcium in the
left ventricular outflow track correlated with aortic root rupture, but calcium on the
leaflet showing no effect. 50
Balloon Valvuloplasty
Balloon predilation of the native valve has been shown to correlate with the need
for a permanent pacemaker. 4 2 Clinicians using a smaller balloon have shown lower
'
pacemaker rates.5
Prosthesis Sizing
The sizing of the valve sets the radial resistive force of the deployed valve. Mismatch between the annulus size and the device size has been shown to increase paravalvular leakage. 52 Specifically, valve undersizing has been associated with moderate and severe paravalvular leakage.53 Selecting an oversized valve (5-10% larger)
has been shown to reduced paravalvular leakage.54 This suggests that the magnitude
of the radial force affects the amount of leakage. 55
Greater than 5-10% oversiz-
ing has been connected with increased aortic root rupture and increased conduction
blocks.3 4 , 4 5, 46 ,5 6 There exists an optimal radial resistive force for each patient where
enough contact force is applied to anchor and seal the device without damaging the
surrounding tissue. The inter-patient variability in this optimal radial force remains
to be characterized. CT sizing of the annulus has recently become standard practice
because it has been shown to more accurately assess annulus diameter than echocardiogram alone, and the improved sizing has improved clinical outcomes.
1.4.3
Failure Models
Clinical and academic work has been done to understand severe adverse events
and to be able to better predict future complications.2 8 Clinical risk scores are being
created that take into account TAVR specific knowledge gained from clinical trials and
ongoing registries. Computational methods such as finite element analysis (FEA) and
35
computational fluid dynamics (CFD) have been implemented to probe how the valve,
fluid, and native tissue interact. Benchtop testing has provided new insights into
the fundamentals behind complex clinical phenomena while grounding computational
models in physical reality.
Clinical Correlates
The correlation of clinical/procedural data with clinical outcomes has a long history in medicine. Clinical trials provide a well controlled experiment to confidently
understand the effects of intervention. However, they are by necessity very narrow
in scope in order to gain an approved indication. The PARTNERS B trial showed
the inadequacy of medical management for severe aortic stenosis and gave a clear
mandate for intervention. The US PIVOTAL trials showed the efficacy of the stented
valve graft and the overall improvement in the field over the intervening four years.
Both trials clearly show the benefit of TAVR for non-surgical and high-risk surgical
patients.
Registries allow for tracking the real world implementation of technologies that
are applied in much more varied conditions than seen in clinical trials. Registries in
Europe and more recently the US have allowed for improvements in care as greater
numbers of patients allow for greater statistical power to look at smaller effects with
more certainty. The TVT Registry in the US tracks the expansion of TAVR and the
rates of complications. Studies of these registries have led to improved treatment
guidance.
Risk scores originally created for surgical aortic valve replacement have been applied to TAVR with questionable efficacy. The applicability of the Society of Thoracic
Surgeons (STS) score and the logistic European System for Cardiac Operative Risk
Evaluation (LES) to the prediction of TAVR outcomes is unclear and large studies have shown that both risk scores overestimate both short term (in-hospital and
30 day) and long term TAVR mortality. "Calcium risk scores have also been implemented that modify metrics originally created for coronary vascular stent risk with
some success predicting paravalvular leakage and conduction disturbances (Agatston,
36
ANCVS).58 Procedural predictors of pacemaker implantation risk have also been created that incorporate clinical data, electrocardiographic data and anatomic data from
echocardiograms and CT scans. 59
Virtual Deployment
Virtual deployment of valves into general and patient-specific anatomies has been
attempted with the goal of understanding interventional efficacy and device response
to patient heterogeneity. These models can be relatively simple, such as beam bending
approximations, or they can be more detailed FEA.60 These models have been used to
predict aortic root rupture, paravalvular leakage, leaflet stresses and leaflet coaptation
for both self expanding and balloon expandable valves. 61 64
Fluid Modeling
Models from lumped parameter models to 3-D CFD have been performed to understand the effect of the aortic valve on the thrombosis formation, hemolytic potential,
and paravalvular leakage. These models aim to connect the structure of the system
65 66
with observed physiologic response. ,
Benchtop Testing
Benchtop testing has also been used to understand the effect of asymmetric deployment on valve function. 67 ,6 8 These studies incorporate particle image velocimetry
to understand the flow fields created by the replacement valves with the goal of understanding valve function. In vitro measurements of hemodynamics through prosthetic
valves and through bicuspid aortic valves have provided relevant information for de69 70
vice design and the explanation of physiologic response. ,
37
Modes
Failure____
Failure
Mechanisms
Failure
Models
10
Clinical
"
SUnderexpansion
Annulus-Device
Sealing
ConductionIn
Inepateda
First
Principles
Correlative
Associations
Probability
(Incidence)
---
How common?
By Valve Type
By Devices, etc.
How are SAE
detected in clinic?
How often do
SAE occur?-----b
. .....
. ........................
..........................................
a
Effeco~ty
Imaging
(Echo, Angio & CT)
auSveriy
Detection
Angular Seeding
Mismatchh
Analyze
Known Pose
Full
Beam Bending
Approx
What research
links SEA w\
failure mechanisms?
Predict
Deployment
Clinical
Da
Extract Tissue
Future Pose]
Heavy
Calcification
Asymmetric
IpatLO-o
DepatAge
Inflammation
tate
Stress Applied to
Membraneous Septum
FEA
Clustering
Image
Analysisj
Figure 1-9: Lit Review Overview
OWW WW W W"
Trials (PARTNERS)
Registries (EUROPE)
Reviews
ACC/AHA
Guidelines
What engineering
simplifications have
been applied?
How has full
deployment
been modelled?
How have people
analyzed clinical
& imaging data?
00
Chapter 2
Methodology
2.1
Overview - Medical Image Computing
The methodology focuses on processing of patient imaging data, the reconstruction
of patient specific models, and the extraction and comparison of valve specific features.
Extraction of patient specific and valve specific geometries are necessary to understand
the effect of the valve on the surrounding environment, and to understand the effect
of the environment on the valve. The generation of a model of the implanted device
and the extraction of patient specific fiduciary marks are necessary precursors for
more detailed mechanics based assessment of valve behavior (Figure 2-1). While
preliminary FEA work has been done to understand the stress/strain state of the
valves in their deployed state, the focus of this methodology will be on the image
reconstruction and model generation.
39
Image Reconstruction
Take 2D Slices
Create 3D Reconstruction
Segment Region of Interest
(Automatic Script)
Model Generation and
Fiduciary Mark Selection
Manually Check
Create Solid Body Geometry
Extract Patient Specific
Fiduciary Marks
Select and Extract
Ideal Fiduciaries
,
-~ --
Finite Element Analysis
Compare Fiduciary Marks
Create Boundary Conditions
Create Mesh in ANSYS
Finite Element Analysis in ANSYS
Generate Deformed Valve
Figure 2-1: Overall Methods Workflow
40
2.1.1
Image Reconstruction
Conceptual
The top left section of the workflow represents the image analysis section (Figure
2-1). The primary goal of this section was to extract patient specific fiduciary marks
from CT scans in a semi-automated way. The conceptual steps are the following:
1. Import 2D Slices
2. Create 3D Reconstruction
3. Segment Region of Interest
4. Manually Check Segmentation and Interject if Necessary
5. Extract Patient Specific Fiduciary Marks
Import 2D slices
The individual slices were stored in the standard Digital Imaging
and Communications in Medicine (DICOM) format. Since the acquisition time for
a whole heart scan was greater than the length of a cardiac cycle, the heart moves
substantially relative to the CT scanner during each beat. Therefore, the signal to
noise ratio was improved by subsampling the images and selecting only the images
that happen at the same part of the cardiac cycle. Electrocardiogram-gated CT scans
accomplishes this by simultaneously measuring the patients electrocardiogram (ECG)
while scanning. In post processing with the scanner software, the images would then
be coregistered based on their ECG value.
Create 3D reconstruction
The images were binned into 10% increments of a
heart beat. Each subset was visually inspected for motion artifacts. The binning
brings together image slices that were spatially close in the scanner's reference frame,
but temporality separated between heartbeats.
If the patient moves with respect
to the scanner between heartbeats there will be a distortion of the reconstructed
image. These errors present as a very regular and ordered blurring of the valve in one
41
principal direction. This represents either incorrect binning of the cardiac cycle or
movement of the patient relative to the scanner during the acquisition window. The
motion artifacts are not equally distributed, but tend to be most pronounced during
systole. Therefore the diastolic phase with the least motion artifacts was selected for
analysis. The analysis in this thesis focused on single-phase measurements, and did
not take into account deformation across the cardiac cycle.
Min/Max
Threshold
Window
& Level
Mi/Max
Threshold
Calcim
Window
& Level
Ioad
Radial Di;=jbri
Amnulus
plane
RAnl
ofC
TikesOf
Mmrets
Lunen
Mask
LVOT-AscAo
spline
etfDpoynt
Seeding Angle
& Level
Min/Max
Threshold
Isolate IAft Side
of the Heart
Timm
Tilt
Plane
Window
Slice Index
Cropping Info
Maskmask
Calcium
MliMax
Threshold
Dphi
Window
& Level
Device
Mask
Device Distid
Struts Ph=n
pre/o" Camg
in Spline
Underexpmnsion
& Eccentricity
of Deployment
Figure 2-2: Image Segmentation General Workflow. Plain text represents inputs.
Single-walled boxes processes and internal outputs. Double boxes represent final
outputs.
Segment Regions of Interest
The image segmentation workflow was accom-
plished with various programs and included automated, semi-automated, and fully
manual steps (Figure 2-2). This thesis focused only on two segments of this workflow,
the "Calcium Load" and "Radial Underexpansion & Eccentricity of Deployment".
Traditional 2D Slices.
In aortic stenosis, the narrowing of the valve occurs when
calcium infiltrates the valve and creates stiff, non-compliant tissue. It is both a main
driver of cardiovascular disease and a hinderance to minimally invasive access through
42
Figure 2-3: Aortic Cross Section View Highlighting Calcium Deposits. The image
on the left is a 2D slice taken from a CT scan. Each slice is separated by .75 mm.
The image on the right is a maximum intensity projection of the same patient. The
highest intensity value over a 22mm thick slice is presented.
Figure 2-4: Stented valve graft extracted from CT imagery.
43
the arterial tree. A traditional two dimensional view of the heart can be used to look
at the uneven calcium distribution across the three leaflets of the valve (Figure 23). The total calcium distribution around the valve can be more easily visualized
by taking a maximum intensity projection over a thicker region (22mm instead of
.75mm). This projection sacrifices information about the relative depth of features.
It should be noted that the distribution of calcium in the cardiovascular system varies
significantly from patient to patient.
Radial Expansion and Eccentricity
The injected contrast circulating in the blood creates a large difference in X-ray
opacity between the blood and the surrounding tissue. 4This allows for the relatively
straightforward segmentation of the inner lining of the vascular system (the inner
lumen). The DICOM stack was imported and filtered to emphasize the X-ray opacity
gradient between tissue and contrast. A mask was then created from a thresholding
criteria on a voxel by voxel basis.
In the body, the left side and the right side of the heart connect through the lungs
(when the blood is being oxygenated) and the end organs (when the blood is being
deoxygenated). However, the microvascular network connecting the sides of the heart
through the lungs and organs is much smaller than the resolution of the CT. This
allows the left and right side to be treated as disjoint sets. Since the cardiac CT only
incorporates the area around the heart, the left side (left atrium, left ventricle, and
aorta) makes up the largest (by volume) contiguous feature in a patient's scan. Nontarget regions were therefore removed by pruning smaller, non-contiguous regions
without having to incorporate a priori knowledge about the geometric dimensions
or orientation of the heart. This was accomplished by running successive rounds of
morphological openings followed by removing small islands of the mask. This loop
was repeated until only one connected region remained. The morphological open
removed tenuous connections between connected regions while attempting to keep
the remaining mask topology unchanged.
The island removal process deleted all
disjoint sets whose size was under a given threshold voxel count. The minimum voxel
44
count was raised each round to remove smaller and smaller outlying regions.
The segmentation of the pre-procedural CT scan was readily able to isolate the
cardiovascular system (Figure 2-5). The left and right sides of the heart were extracted
from the rest of the body. The left and right sides of the heart are tightly nestled.
While anatomically disjoint, the tissue separation is on the order of 1mm thick. Many
times, completely automated segmentation bridged that small gap creating spurious
connections. During the inspection stage, the spurious connections were removed and
miscellaneous vessels returning from the lungs were removed for clarity.
Figure 2-5: Automated 3D modeling pre-procedural reconstruction. The light blue
is the segmented left side of the heart that pumps oxygenated blood from the lungs
to the rest of the body. The dark blue is the segmented right side of the hearth that
pumps deoxygenated blood from the rest of the body to the lungs. A section of the
right ventricle has been removed to allow visualization of the aortic valve.
45
Calcium Segmentation
Cropping Index
Ranges
Calcium Mi/Max
Threshold
Calcium
Window & Level
Import icom
Threshold Fill
Dilated Lumen ROI
Keep Voxels Near Lumen Region
Find Interwetion of
Cim
and True LMe
Mask
True Lumen Mask
Figure 2-6: Calcium Segmentation&Workflow. Plain text represent inputs. Single wall
boxes represent individual processes. Double wail boxes represent outputs.
3D Reconstruction.
CT scans allow much higher resolution reconstructions of pa--
tient anatomy than echocardiograms(ultrasound). The implementation of the calcium
segmentation involved the mask of the inner lumen generated in an earlier process
(Figure 2-6). The image stack was re-filtered with a new Hounsfield window and level
filter to isolate the calcium now instead of the blood. A calcium mask was created
by selecting all voxels whose intensity value falls within that calcium threshold.
This thresholding included any highly electron dense regions such as pacemakers
and bones, like the spine and ribs. To remove these other artifacts, this high electron
density mask was compared against the mask of the left side of the heart. Only
calcium near the aortic valve or the aorta was of interest in this study. Therefore,
46
a dilated copy of the aortic mask was created by including adjacent voxels in all
directions from the original aortic mask. A boolean intersection was then performed
on the calcium mask and the dilated aortic mask with only the intersection of the
calcium mask and the dilated aorta mask retained. This new mask was now treated
as calcium associated with the cardiovascular system. The updated calcium mask was
compared with the original vessel mask and any intersection of the two were removed
from the vessel mask. The end product was two disjoint masks, one containing the
calcium, and one containing the inner lining of the blood vessels.
47
2.1.2
Model Generation and Fiduciary Selection
Conceptual
Figure 2-7: Reverse Engineered Solid Body Model of 29mm Stented Valve Graft
The solid body model was reverse engineered from a tissue-less undeployed stented
valve graft. Only the Nitinol cage in the zero external forces state is modeled. Reference points for the device were created from the solid model and stored as (x,y,z)
coordinates. These fiduciary points were used for coregistration of the deformed and
undeformed valve.
Technical Implementation
A solid body model was created in Solidworks (Version 2013-2014. Dassault Systemes, Waltham, MA) from the combination of point cloud data and scanning imagery. Edges were manually delineated and splines were fit to each edge. The splines
were then used to create non-uniform rational B-spline (NURBS) surfaces representing individual strut faces. Those faces were subsequently knitted at the edges to
create a watertight solid body. This form was saved as a single part file (.iges) and
exported to ANSYS Mechanical (Version 15. ANSYS inc., Cecil Township, PA) for
further analysis.
Coordinate System
There are many ways to create fiduciary marks and extract
features from the implanted stented valve graph. However, the principle orientation of
48
the device is along the direction of flow. The rotational symmetry of the device naturally lends itself to being described by parallel planes normal to the centerline. Three
planes were selected to capture the radial and longitudinal deformations expected in
the deployment.
The three parallel planes were created normal to the device centerline. The first
intersects the most proximal (top) part of the device. The second intersects the waist
of the device where the cross-sectional area is the smallest. The third intersects the
most distal (bottom) part of the device. A fiduciary mark was created for each planestrut crossing by selecting the midpoint of the interior edge of the strut. The distal
left ventricular outflow tract section is important as the primary anchoring region
for the device. The waist of the device interacts with the native calcified tissue and
marks the beginning of the supra-annular replacement valve. Finally, the proximal
aortic root plane captures the torsional deformation and under expansion experienced
throughout the length of the device when it is impinged by annular and sub-valvular
calcium.
The marks are represented with green circles for the proximal plane, yellow circles
for the constriction plane, and red circles for the distal plane (Figure 2-8a). The
stented valve graft has 15-fold rotational symmetry and therefore each plane intersects
the valve fifteen times. The 3 pairs of 15 points create a set of 45 points that represent
the pose of the undeformed valve. This 45 x 3 matrix was exported from Solidworks
to a .csv file for later use.
49
C.O.M at Origin Pt 001
Centered Fiduciaries W
20-
20
0.
10
0
0
-10
-20
-30
0
**
*
30
30"
10"
.
[mmb
-1
0*
-2
0
0
-3
X
j.
Annulus
I*[i.I
0 LVOT
(b)
(a)
Figure 2-8: Selection of the Fiduciary Points and the creation of those points in
the ANSYS software. (a) shows a representation of the 45 points. The green dots
represent the top of the valve, the yellow dots represent the point of most constriction,
and the red dots represent the bottom of the device. (b) shows those points inputted
into ANSYS.
50
Valve Orientation Extraction
In a similar method as described in section 2.1.1,
the Nitinol valve was extracted from the CT scan. The same three planes and 45
fiduciary points were extracted from the deployed device (Figure 2-8). All 45 fiduciary
points were plotted as color-coded x's based on their location on the device (green
top, yellow
middle, red
bottom) (Figure 2-9).
(a) Register Patient-Specific Fiduciaries
Fiduciaries Extracted CT Images Pt_001
650
640
630
610
600
590
580
-210 .,n
x
0
X Extracted LVOT
x
0
10
20[mm]
Extracted Ao Bulb 90
Extracted Annulus
-20
-10
~180
[mm]
40
170
-160-1o
30
50
(b) Extracted Fiduciaries for Comparison
Figure 2-9: Patient Specific Analysis of Valve Deformation. Each plane is imaged
and the centroid of each strut is chosen as the center point.
51
Technical Implementation
The fiduciaries are saved as two matrices. The points extracted from the CT scans
are defined as {Pdeformed,1,Pdeformed,2, ...
Pdeformed,45}
and the fiduciary points ex-
tracted from the idealized valve are defined as {Punconstrained,1,Punconstraine,2,...
Each point p is a {x, y, z} tuple representing a cartesian point.
, Punconstrained,45}.
Since the valve
is oversized for the annulus and therefore deployed in an constrained environment,
there is expected to be a significant difference between the zero external stress state
Punconstrained of the
valve and the actual deformed state
Pdeformed
of the valve. For
this application, the best fit was defined as the rigid body motion that minimized
the sum of the squared errors of the displacement vectors. The function is defined in
equation 2.1.
45
(R Pdeformed,i - T)
min
-
Punconstrained,i112
(2.1)
t=1
Where T is the translation such that the centroid of the extracted fiduciary points
is equal to the centroid of the undeformed fiduciary points and R is a orthogonal
rotation vector.
Solving for R is the Orthogonal Procrustes problem and can be
solved using singular valve decomposition to extract the rotation matrix with the
added constraint of allowing only rotations and forbidding reflections.
Once the rigid body transformation was completed, the displacement vectors were
calculated and exported in a .csv file for use by ANSYS (Figure 2-10). A unique
notebook was created for each patient in order to maintain working coherent copies
of data, code, and visualizations for each patient.
Overview
In the first step, the solid body model generation created a set of 45
fiduciary marks from the idealized, unconstrained valve. In the second step, those
same fiduciary marks were extracted from the segmentation of the in vivo valve
taken from follow up CT scans of each patient. The next step was to model the most
parsimonious way that the valve could be deformed from the idealized, unconstrained
state to the deformed state seen in vivo. Once that transformation was applied, the
52
30
30
9se
xxx
Zero External Force State
Physiologic Deformation
oee Zero External Force State
xxX Physiologic Deformation
20
20
10
10
-10
-20
-20
-301
-30
-301
-30
-
-10-
Proximal Deformation Vectors
-20
-10
0
[mmI
10
20
30
-20
-10
0
[mm]
10
20
30
Figure 2-10: Fiduciary points for the proximal plane. (a) shows a simply connected
schematic of the deformed valve (x's) and the undeformed valve (o's). (b) shows the
displacement vectors connecting each undeformed-deformed pair. The displacement
vectors are 3D, but only the planar (x and y) components are plotted. The out of plane
movement in the z direction is usually small compared to the in plane movements.
displacement vectors were calculated for the 45 ideal-deformed point pairs. Those
deformation vectors represent the most parsimonious way to deform the opened state
to the deployed state.
2.1.3
Python.
Project Files
A unique IPython notebook (version 1.1.0) running python (version 2.7)
was created for each patient. The singular value decomposition was implemented
in the Numpy module (a free python library designed to support MATLAB like
computing). All plots were created and embedded in a IPython notebook using the
matplotlib module. The IPython notebooks were placed it the Koch Institute file
server rowley for storage.
ANSYS
The solid body model, the mesh, the boundary conditions, and output files
for the patient-specific finite element models were stored in local ANSYS Workbench
files. Verification work for the FEA is still ongoing.
53
Images
The clinical image database was stored locally with backups made on exter-
nal hard drives. Identifying patient information has been scrubbed. The database was
managed using OsiriX (version 5.8.5 32 bit), a free DICOM management platform for
Mac OS X. Image segmentation and registration was completed using ScanIP(version
6.0. Simpleware Ltd., Exeter, UK) . ScanIP is a commercially available product used
to segment and mesh complicated geometries for analysis. It largely implements the
Insight Segmentation and Registration ToolKit of open-source algorithms which can
be found at http://www.itk.org/ItkSoftwareGuide.pdf.
The GUI, additional docu-
mentation, and API support for scripting make ScanIP a good tool for completing
exploratory analysis and then automating that analysis pipeline. ScanIP supports
many scripting languages, but python was used exclusively for this thesis. ScanIP
runs its own stock python interpreter, but most common libraries are supported and
full extensibility is allowed.
54
Chapter 3
Results
In this chapter the methodology is applied to a dataset acquired from the Case
Western Reserve University. Preliminary findings are presented and progress on the
overall project is reported.
3.1
Clinical Data - Patient Dataset
The patients in this dataset represent normal cases, unexpectedly difficult deployments, and cases with severe adverse events. Pre-procedural ECG-gated CT with
contrast, perioperative angiography, and follow up CT with contrast were collected
for each patient. The ECG-gated CT scans were taken with .6mm slice thickness and
binned for every 10% of the cardiac cycle. The Siemens B30f reconstruction kernel
was used. Pre-procedural CT scans with contrast were collected between August 2007
and September 2010. The paired follow up CT with contrast were collected between
November 2008 and March 2012.
All clinical variables collected before, during, and after the procedure were provided and the clusters can be found in the Appendix (Figures A-1, A-2, A-3, A-4, and
A-5). The paired clinical variables and imagery were scrubbed of all their identifying
data before analysis began and given generic identification numbers.
All patients had normal clinical follow up meetings to assess changes in valve performance and quality of life. In addition, the patients in Professor Costa's cohort
55
received a follow up CT scan with contrast to assess valve location and performance.
There are 33 patients with pre-procedural and follow up imaging. The interval between pre and post CT acquisition was not standardized and the large variance in
follow up imaging is a limitation of this dataset.
This dataset of 33 cases represents a very small subset of patients that have
received stented valve grafts. In this set of patients, the post procedural CT with
contrast provides extra information about the long term position and performance of
the valve. This post-procedural CT with contrast will be used to create the models,
but will not be collected prospectively for additional patients. Therefore, this initial
study is aimed at the small subset of patients with extremes of clinical presentation
with the expectation that any preliminary findings and modeling approaches will be
a proof of concept that allows for potential expansion.
Interval Between Pre and
Post CT
----
10
None
count 33.U
mean 19.2
std
10.3
min
2.0
25%
12.4
8
6
C
U
bu%
1/0%
00
max
1b.9
2(.2
4
4
2
0
0
5
10
15
20
25
30
35
40
Months Between Imaging Studies
Figure 3-1: Elapsed time between pre and post CT scans in the Case Western Dataset.
The high standard deviation of 10.3 months is a limitation of the current dataset.
Normal protocol does not include post-procedural CT with contrast.
56
3.1.1
Patient Population
Age
The patient age in the current dataset does not differ much from the distribution
in the larger US PIVOTAL Trial (Figure 3-2). The mean age of 79.4
6.8 was similar
to the US Pivotal trial mean age of 83.2 t 8.7. It is worth noting that patients are significantly older than the original patients followed in the Ross and Braunwald natural
history studies of the 1960s where the mortality cliff is represented as occurring for
patients in their 50s (Figure 1-2). Those studies were conducted with a heterogenous
patient population including patients with bicuspid leaflets, tricuspid leaflets, and
rheumatic fever induced stenosis. Echocardiograms had not been developed and no
medical management was available at the time. In the 21st century, the TAVR target
population consists mostly of non-rheumatic stenosis patients with tricuspid leaflets
who are on optimal medical management and still not thriving. These disparate situations warrant a reappraisal of the value of the five decade old Braunwald curves
and are an important motivator for this work.
Concomitant Aortic Regurgitation
AS is the failure of the aortic valve to open fully during forward flow. Aortic
regurgitation is the failure of the aortic valve to fully close during the filling of the
heart to prevent back flow of blood. While there are etiologies where only one type
of failure is present, it is common for both to be present.
In the US PIVOTAL
trial all patients had severe aortic stenosis. In addition, 43% of the patients had
mild regurgitation and 8.6% had moderate regurgitation. This high incidence of preprocedural aortic regurgitation is also seen in this dataset (Figure 3-4). Data in the US
PIVOTAL trial suggests that paravalvular leakage decreases over time. This dataset
also exhibits more patients with no regurgitation at follow up, but the question of
long term sealing around the valve warrants more investigation.
57
Histogram of Age
5
4
I.
3
0
U 2
1
0
65
80
75
70
85
90
Age
Figure 3-2: Age distribution of patients in the Case Western Dataset. The mean age
of 79.4 + 6.8 was similar to the US Pivotal trial mean age of 83.2 8.7. It is worth
noting that patients are significantly older than the original patients followed in the
Ross and Braunwald natural history studies of the 1960s.
Table 3.1: Complication Rates
Incidence (PIVOTAL)
Incidence (Case Western)
Paravalvular Leakage
46.6% (>trivial 0 6 Months)
52.6% (>trivial @ Post)
Arrhythmias
21.6%
30 Days
58
18.9% New Onset AF
40
NYHA Class Pre and Post Procedure
4
3
35
30
4-,
25
1
20
0
C-) 15
10
5
0
40
Figure 3-3: NYHA classification in the Case Western Database. All patients at
discharge were categorized with asymptomatic (Class I) or mild heart failure (Class
II) in the Case Western Reserve dataset. Class III represents moderate heart failure
where mild activities cause symptoms. Class IV patients have severe symptoms at rest
and are unable to do activities with difficulty. Thirty-seven patients are represented
here, but four were excluded because of incomplete imaging data.
59
40
TTE Aortic Insufficiency
3.0
2.0
1.0
0.0
35
30
25
0
20
U 15
10
5
0
(D
04
0
Figure 3-4: Aortic Insufficiency (Al) in the Case Western Dataset. Most patients had
concomitant aortic stenosis and aortic insufficiency as seen by the high incidence of
AI in TTE Pre-operative imaging. An Al of 4 is severe. After the procedure, the
severity of AI was reduced, but many continued to have significant Al. At the follow
up, the number of patients showing no Al increased, while the number of patients
with mild and moderate Al decreased.
60
3.1.2
Normal Deployment
The following is the application of the framework with patient imaging data from a
normal deployment. The outputs of the lumen and valve segmentation are presented.
The fiduciaries are extracted from the CT image and the rigid body fit is applied.
Finally, the deformation vectors mapping the ideal and deployed valve are presented.
Lumen And Device Segmentation
The automated segmentation returned a fused mask that incorrectly bridged the
left and right sides of the heart (Figure 3-5a). The spurious connection was removed
and only the left atrium, left ventricle, aortic root, and the valve remain (Figure
3-5b).
(a) Output of Automated Segmentation
(b) Isolated Left Ventricle with Valve
Figure 3-5: Segmentation of Valve and Inner Lumen for a Normal Deployment. The
valve is represented in blue while the lumen is represented in red.
Extracting Valve Deformation
Although the deployment was mostly symmetric, there still exists multi-modal
deformation (Figure 3-6). The valve exhibits expected under expansion and expected
61
low levels of ellipticity. However, It is worth noting that the planes defined by the
proximal, middle, and distal sections are no longer parallel. The planes defined by the
original fiduciary points are coaxial and parallel. For the deformed valve, the center of
gravity of each grouping of points is no longer coaxial and the normal vectors defined
by the three planes are no longer parallel. The oversizing of the device has led to an
elongation in the axial direction. The deformed valve has a larger aspect ratio than
the zero external stress state valve. It is both longer and narrower.
Deformation Vectors
It is also interesting to note that the level of ellipticity is
not constant at all three levels (Figure 3-7). The first and second rings show much
more ellipticity than the third. The main anchoring mechanism happens in the left
ventricle outflow track and that is where the greatest interaction between calcium
and the metal cage occurs.
In most patients the aortic bulb (in green) does not
provide significant anchoring force, and in some cases the device does not even make
sustained contact with the wall. It is therefore interesting to note that in this patient,
the aortic bulb section of the device experiences significant radial under expansion and
twist. During deployment, this section of the device is released last in the catheter.
Since the distal section of the device is already anchored into the left ventricle, any
torsion imparted by the deployment catheter will be transmitted through the valve
with residual rotational strain. The greater the oversizing of the valve, the greater
the expectation of the device engaging the tissue in the aortic bulb.
62
Rotated with Original Points Overlaid
Normal Deployment
0
3
30
<X
0
0,-/,o
0
MO
20
Xx
x
.
X
Xe
0
.
00
10[ n
Xe
x
-10
-20
-30
-20
-30
40
-10
,n
x
X
t
Extracted Ao Bulb
Extracted Annulus
Extracted LVOT
0
0
0
Undeformed Ao Bulb
Undeformed Annulus
Undeformed LVOT
10
0
.10
20
[mm]
(a) Oblique Viiew
Rotated with Original Pi ints Overlaid
Normal Deploy ment
X
30
30
o ,
xe
20
20
10
10
qmm]
10
[mm] 0
-10
x
X
Extracted Ao Bulb
Extracted Annulus
Extracted LVOT
0
0
0
Undeformed Ao Bulb
Undeformed Annulus
Undeformed LVOT
20
20
30
30
40
(b) Profile Vi
Figure 3-6: Rigid body rotation to fit patient specific normal deployment. In 3-6a
they under expansion can be seen most clearly in the aortic bulb. In 3-6b the under
expansion causes axial elongation compare to the unstressed valve.
63
E
30
20 -
10
10
10
01 1, ' /S
0
[mm]
E
IU 20
10
0
-30
20-
10
0
-10
-20
-30
-20
0
*
C,,
0
[mm)
0
C
S
9
10
20
*0. Zero External Force State
Physiologic Deformation
9
C
0
-10
0
[mm]
9
10
20
Seo Zero External Force State
Physiologic Deformation
-10
Middle Deformation Vectors
-20
-
E
E
-10
10
20
Zero External Force State
Physiologic Deformation
0
[mm]
[mm]
0
/
Proximal Deformation Vectors
-20
10
20
@eo Zero External Force State
x x x Physiologic Deformation
-10
xx x
oeo
..................
..............................................
..
................
.........................................
30
20
10
0
-10
-20 [
-.301
30
20
10-
0-10
-20-
-30
-20
Deformation Vectors Extracted From CT Scans of Pt_001
20
Oe9 Zero External Force State
Physiologic Deformation
xxx
0
-10
-10
20
ese Zero External Force State
x x x Physiologic Deformation
001
/
[mm]
-10
-10
-20
-20
-20
Distal Deformation Vectors
-
-20 -
3U
20
10
-10 [
-20
-30
Figure 3-7: Extracted deformation vectors from normal deployment. The arrows represent the deformation vectors connecting
the undeformed ideal valve with the deformed valve. The two figures on the left in red represent the distal portion of the device
in the left ventricle outflow tract. The center panels in yellow are the waist of the device and the green panels on the right are
the proximal ends of the device in the aortic root. A distinct curl in the vectors is visible.
3.1.3
Abnormal Deployment
An example of an abnormal deployment is now presented. This patient had much
more extensive calcification of both the aortic valve and the ascending aorta. The
output of the calcium segmentation is presented for this patient, along with the results
of the fitting algorithm and deformation vectors.
Calcium Segmentation
The calcium segmentation shows both valvular and aortic calcium (Figure 3-8).
This is extensive calcium in the device landing zone and along the catheter pathway.
(a) Lumen and Calcium
(b) Calcium
Figure 3-8: Pre-procedural segmentation of calcium and inner lumen for a patient
with abnormal device deployment.
Estimating Valve Deformation
The device failed to deploy fully (Figure 3-9). The reference planes remain closely
parallel with the center of gravity for each cluster remaining co-linear. In this deployment the valve failed to deploy properly and subsequently experienced radial buckling
65
in the inward direction. This failure mode has been attributed to suboptimal loading of the valve into the deployment catheter, but is exasperated by large verrucous
calcific nodules around the annular plane.
Deformation Vectors
The buckling failure mode introduces strong asymmetries
in the deformation of the valve (Figure 3-10). The under expansion in the aortic bulb
is still observed, but it is important to note that these 2 dimensional projections of
the vectors do not capture the movement of the fiduciaries into and out of the plane.
The z component of these vectors in the normal deployment are small compared to
the x and y components. However, in this deployment the z component of the vectors
are of the same order of magnitude as the x and y components.
66
Rotated with Original Points Overlaid
Abnormal Deployment
x
x*
30
x
X
0
X,
X
"
0
X X
X
0
0
e
X X
x
X
0
X
0
0
X1
x
10
[n0
x
00a
0
*x
1
-10
.
x
*,,
20
0
0
0)
-20
-30
-20
-30
-10
x
x
40
Extracted Ao Bulb
Extracted Annulus
Extracted LVOT
*
*
*
Undeformed Ao Bulb
Undeformed Annulus
Undeformed LVOT
499
-%
A
10
10
0
20
[mm]
(a) Oblique View
Rotated with Original Points Overlaid
Abnormal Deployment
goX
X
0,
OX
z
30
20
20
#
30
-10
10
qmm]
[mm]j
10
-10
X
X
Extracted Ao Bulb
Extracted Annulus
Extracted LVOT
S
S
S
20
Undeformed Ao Bulb
Undeformed Annulus
Undeformed LVOT
20
3
030
(b) Profile View
Figure 3-9: Rigid body rotation to fit patient specific abnormal deformation. The
patient had significant subvalvular, valvular and aortic calcification and the valve
buckled during deployment.
67
20 -
10-
E 0-
oe
xxx
20 -
100
eSe
0
-10
*Se
-10
0
20
Zero External Force State
Physiologic Deformation
0
[mm]
10
10
20
Zero External Force State
Physiologic Deformation
0
[mm]
Middle Deformation Vectors
-20
20
10-
0
20
10
0
-10
-20
-.IU
-20
se Zero Extemal Force State
10
20
Zero External Force State
[mm)
0
xxx Physiologic Deformation
-10
9o
0
[mm]
10
20
xxx Physiologic Deformation
-10
Proximal Deformation Vectors
-20
Deformation Vectors Extracted From CT Scans of Pt_002
Zero External Force State
Physiologic Deformation
0-0
-20
-10
20
-10 -
10
10-
-30
JU
-10
0
[mm]
Zero External Force State
-20
-10
00.
xxx Physiologic Deformation
20
20-
-20-
-20
-
-20-
10
20-
30
-301
E0
10
-10 -
0
[mm]
-10
-10
-20
Distal Deformation Vectors
-20
-20
-30
Figure 3-10: An abnormal deployment from a procedure on a patient with sever calcium deposits. The arrows represent the
deformation vectors connecting the undeformed ideal valve with the deformed valve. The two figures on the left in red represent
the distal portion of the device in the left ventricle outflow tract. The center panels in yellow are the waist of the device and the
green panels on the right are the proximal ends of the device in the aortic root. The z components of the deformation vectors
are much greater than seen in the normal deployment (Figure 3-7).
00
Chapter 4
Discussion
4.1
4.1.1
Findings of Note
Wide Range of Stable Valve Deformation
Two cases have been presented with wildly different deployment outcomes (Figures 3-7 and 3-10). The first had a normal deployment showing relatively symmetric
under expansion of the device. The second was on abnormal deployment where the
valve buckled inwards. This finding suggests a surprisingly wide region of valve deformations are possible that can support competent valves.
The stented valve graft must be evaluated on its ability to appropriately modulate
flow.
There are large and asymmetric deployments in the Case Western dataset
(Figure 3-10). It has been long known that circular, and low ellipticity deployments
have been successful, but the boundaries of what variations patients can tolerate
needs to be better characterized. This will result in better patient risk stratification
for treatment options and create better knowledge of the design and risk space for
industry and regulatory agencies. Evaluating what types of patients and what types
of valves are resistant to gross deformation warrants further investigation.
69
4.1.2
Complex Boundary Conditions
The two cases presented show the need for testing of replacement valves with
multi-modal loads. The abnormal deployment represents an extreme case where the
buckling failure mechanism masks the understanding of the normal device-tissue interaction. The incidence of this type of failure has decreased with improved ways
of packaging the prosthesis in the delivery catheter.
Even so, it is important to
understand how variations in physiology exacerbate procedural weaknesses.
The normally deployed valve exhibited torsional and bending deformation in addition to the radial under expansion and pulsatile load. Multimodal testing of devices
is beginning to be more widely implemented in the cardiovascular device regulatory
sphere, but understanding the range of these boundary conditions is important to
make sure that all relevant regions of the test space are being explored without inducing an unnecessary test burden. Valves in the Case Western dataset experience
significant torsion and bending that needs to be incorporated into bench top fatigue
testing to supplement pulsatile flow, but more work is needed to be done to accurately
characterize those boundary conditions.
Interestingly, physiologic torsion is known to be present during healthy cardiac
contraction and to decrease during heart failure. There is a distinct torsion rotation to
the valve as visualized by the curling vectors (Figure 3-7). Ongoing work is underway
to understand how these twists align and how well excess torsion correlates with
clinical outcomes.
4.1.3
Understanding Calcium is a Critical Unmet Need
The clinical literature clearly identifies calcium distribution as a crucial instigator
of TAVR complications. The extensive calcium present in the second patient undoubtably contributed to the buckling of the device. Work is ongoing to understand
the commonalities across patients in the Case Western dataset to understand calcium
patterns of influence that have less prominent effects than whole cage buckling. Evaluating the sensitivity of severe adverse events with respect to calcium distribution is a
70
pressing unmet need. Not all calcium is equally dangerous, and more work is needed
to understand how certain areas exacerbate problems in device design, delivery and
deployment and in doing so make for much smaller margins for procedural error.
71
4.2
4.2.1
Limitations
Large Variance in Follow Up Interval
The long interval between the initial and follow up scan is neither a negative or
positive factor. The two year gap may convolute short term and long term effects,
but if the goal is to understand long term success of devices, then long term data is
required. Of more concern is the high variance in the follow up interval. This high
variance is a function of the non-standardized nature of collecting post-procedural
CT with contrast. Care needs to be taken when comparing inter-patient results when
those follow up intervals vary greatly.
4.2.2
Motion Artifacts - Older Scans
Motion artifacts are a function of the relative motion between the subject and the
scanner and a function of the acquisition time. These ECG-gated scans were collected
from 2007-2010 and many phases of the cardiac cycle, especially during systole, show
blurring and step-off artifacts. These errors occur as the tomography is computed
and retrospectively gated to fit a specific cardiac cycle. Faster acquisition times lead
to lower artifact rates, and newer models have attenuated this noise sources by faster
gantry rotation or more detectors. Post-processing of the current dataset has been
attempted, but the artifacts make it difficult to extract a reliable strut center for
cardiac phases that have high relative motion.
4.2.3
Small Sample Size
Maintaining a database of paired clinical and imaging information that is both
relevant and complete is a difficult endeavor.
The collection of prospective data
has long lead times, making it difficult to accumulate many samples. Also, missing
patient data excluded 5 patients from the study. The missing data was not able to be
.retrieved, and there was no way to reasonably apply interpolation for these specific
data types.
72
4.3
Justification of Approach
A significant part of this work was to find academic questions that were of clinical
and industrial merit that were also feasible to investigate. Therefore this work has
focuses on two main parts. The first part involves the creation of a framework to
integrate clinical data with mechanics based modeling of underlying mechanisms.
The second question is can testable hypotheses be delineated and applied that hit the
trifecta of clinical need, academic opportunity and industrial ability to immediately
apply that knowledge. These mechanisms are explored in the Case Western dataset
and than can be probed on a much larger scale for validation when warranted. In the
end, the most important aspect turned out to be the need to incorporate unstructured
imaging data in the understanding of TAVR device-tissue interaction in a way that
has immediate clinical impact.
4.3.1
Need for Incorporating Unstructured Data
Risk scores validated for surgical valve replacement have been applied to TAVR.
However, these risk scores, created only with structured clinical data, have shown poor
applicability to the new procedure and new patient population.57 Work is needed to
incorporate unstructured data into these estimators.
Fusion of Different Information Modalities
Extracting features from alternative information streams is critical.
Clinician-
researchers have long sought for animal models that recapitulate the human experience to understand the human progression. However, animal models of complex
chronic diseases are difficult and time intensive to create. It is infeasible to query the
animal model in reasonable time to bound the solution sufficiently. The answer is to
use physical models incorporating human data in conjunction with as much animal
data as is available to create a model with which all the variables (flow, geometry,
etc) can be modulated to interrogate the effects of possible interventional benefits.
Creating discrete, quantifiable metrics from imaging data is a key step in creating
73
physiologically relevant features to incorporate into predictors (Figure 4-1).
Serial CT is an important contributor to the unstructured clinical data.
Paired pre and post procedural CT imagery is well structured to investigate questions of anatomy and intervention. The ubiquity of pre-procedural CT scans means
that any knowledge gained can be widely implemented without a large change in
the workflow of the clinician. Post-procedural CT provided need ground truth and
validation but the scans are not usually collected because of the additional burden
on the kidneys from additional intravenous contrast. In this way we have a valuable
dataset to evaluate how the device-tissue interactions and their clinical consequences.
4.3.2
Need for Understanding of Device Movement
Device-Anatomy Interaction
Investigating the interaction between the valve and tissue provides a good combination of technical feasibility and possibility for tangible improvements in care.
These TAVR specific problem of paravalvular leakage is a geometric and mechanical effect for which there has not been a sufficiently similar predicate device from
which to transfer sufficient institutional knowledge. The hemodynamic questions of
flow through replacement valves has been studied extensively in surgical valves, but
the questions of anchoring and sealing are intimately involved with the mechanics of
delivering and fixing the device in place.
Understanding the deformed and undeformed states.
We have the ability to create a high fidelity zero external force state from which it
was possible to extract fiduciary points. In addition, the final pose of the device can be
extracted from post-procedural CT images. This allows for an intuitive understanding
of the effects caused by deploying an oversized valve into a heterogeneous environment.
This approach simplifies the deployment steps, while still allowing for ways to estimate
the reaction forces between the device and surrounding tissue.
74
Sample
Pouation
Pre-Clinical
Multi-Scale
Anatomic Features
Multi-Scale
Physiologic Features
Clinical
Info
Data
Testing Data
Training Data
Outcomes
Variabls
Transform 4p
Iterative
Weights
Predict
Update
Outcomes
Compare with I fe large
Ground Truth
(ROC Curve)
Update Priors
Minimize Residual Error
Validate
Patient Specific
Calcium Risk Factor
Figure 4-1: Proposed Framework to Incorporate Multiple Data Streams into Predictive Analysis
75
4.3.3
Need for Immediate Clinical Applicability
The framework presented above supports the analysis of TAVR procedures in a
patient specific manner. These approaches are not limited to the aortic valve nor
stented valve grafts. There are multiple types of replacement valves, both investigational and approved, that could have been examined, but the study of an approved
stented valve graft for the treatment of aortic stenosis had the highest immediate
clinical applicability.
Study FDA Approved Stented Valve Graft
The self-expanding nitinol frame provides a different mechanism for device fixation than a balloon-expandable valve. In a self-expanding valve the radial resistive
force is set by the size of the device, and is not modifiable during deployment like a
balloon deployed device. The device will expand to a local energy minima, but being
able to predict those minima are difficult because they depend heavily on the initial
location of the sheathed device when deployment begins, the patient's anatomy, and
the movement of the device during expansion.
These uncertainties in prediction lead to uncertainties in discerning potentially
difficult procedures from easy procedures. The current stented valve graft on the
market has three attractive aspects. First, the self-expanding deployment requires
more pre-procedural planning of deployment location since device seating is more determine by location of release than force of the release. Secondly, the device has just
been approved for use in the United States and implantation rates are growing. Finally, significant procedural complications still remain which leaves room for positive
impact.
Relevance to Clinicians
Clinicians are forced to focus on the patient on hand and want patient specific
treatment guidance that is simple and unambiguous. They need to know what intervention would be most beneficial, and when to intervene. This work does not address
76
the question of when to intervene, but can be applied to help understand what intervention would be beneficial. Even within the TAVR indicated patient population,
the different valves involve different anchoring mechanisms and therefore a preferred
device type should be evident. Inter-device recommendations for treatment are currently only observational and correlative and can be improved with more research.
Relevance to Industry
Engineers designing the next iterations of devices want ways to evaluate the clinical
impact of changes in valve design. Large delivery catheter exclude patients with small
or tortuous anatomy and increase the risk of vascular complications. A limiting factor
in catheter diameter is the thickness of the struts of the metal frame. These extracted
deformations can act as more realistic boundary conditions to assess the effect of
reducing strut thickness. In this aspect, patient specific deformations are crucial in
understanding the physiologic stresses transferred between the native tissue and the
replacement valve.
77
4.4
4.4.1
Ongoing Work
Improved Calcium Risk Score
Improvements in risk stratification will provide clinicians more detailed assessments of the relative merits of TAVR compared to surgery compared to medical
management for individual patients. This will change the risk benefit calculus for the
procedure, and some new patients will be excepted while others will be newly denied.
The potential exists to apply the approach presented here to test clinical intuitions regarding relative benefits between procedures more mathematically rigorously (Figure
4-2).
Correctly incorporating anatomical information into risk scores will improve the
predictive power for complications such as paravalvular leakage and conduction disturbances.
However, most anatomic measurements are collected because they are
easily accessible and easily quantifiable. Work needs to be done to reduce this high
dimensional redundant data into a feature set that is manageable by clinicians while
still being useful. Ongoing work is being done to improve the anatomical signal to
noise ratio by mapping all the possible patient calcium orientations to a smaller space.
The goal is to create classes and types of calcium distributions that can be individually assessed and parametrically varied. Treating each calcium map as an unique
identifier hides the patterns that are present only at larger patient populations.
78
Clinical Workflow
Clinical
Problem
?*Clinica
Outcome
Clinical Intuitive
Explanation
Engineering
Transform
Procedure Implemented
for Each Intuition
Represent Intuition
Mathematically
Create Metric That Can
Be Applied to Dataset
Expected Threshold
for Importance
Predict Clinical
Outcome
Annotated Patient
Calcium Map
Known Clinical
Outcome
Compare
Create Receiver Operator
Characteristic (ROC) Curve
Clinical Decision
Support Tool
Create Function That
Combines Metrics
Maximize Predictive
Value of Function
Codify
ilAs
Clinical
Problem
Imkpndx
roved?
Risk Index
Clinical
Outcome
Figure 4-2: Proposal to Evaluate Clinical Intuitions
79
4.4.2
Correlating Reaction Forces with Conduction Risk
The physiological environment of replacement heart valves is quite complex. The
valves interact intimately with non-regular geometries, anisotropic tissues, and cyclic
strain (1Hz) for years in a challenging complex blood media. Therefore it is crucial to
understand the forces that the device imposes on the surrounding tissue as the device
anchors itself into place.
Static analysis can be used to estimate the reaction forces between tissue and
device. An ongoing part of this project is the application of the deformation vectors
found in the feature extraction and applying them as boundary conditions for FEA.
The working hypothesis is that the reaction force applied to the device in the area of
the membraneous septum is positively correlated with the incidence of new conduction
disturbances. The challenge is to apply physiologically reasonable constraints to allow
the solution to converge while still striving to maintain as much verisimilitude as
possible.
(a) Finite Element Mesh of the Device
(b) Deformed Mesh
Figure 4-3: Preliminary FEA
Simple beam bending approximations can be made to estimate the radial resistive
force based on the deformation vectors. However, many of the deformations seen in
vivo are large and asymmetric due to asymmetric locations of embedded calcium.
How those asymmetric deformation and forces affect outcomes remains to be investigated. This dataset contains known deployments (post-procedure CT) and known
80
adverse events (conduction disturbances) with the goal of understanding the relationship between conduction block and contact force.
4.4.3
Transcatheter Mitral Valve Replacements
Lessons learned in the study of the replacement of the aortic valve with a stented
tissue valve should be applied to replacements of the mitral valve. The pathophysiology of the mitral valve is different, but the interactions between the device and the
heart are fundamentally similar. An understand of how a metal cage interacts with
a complex geometry is readily applicable to both valve types and warrants further
investigation.
81
82
Chapter 5
Conclusion
TAVR is a growing treatment option for non-surgical and high risk patients with
aortic stenosis. Some clinical knowledge can be transferred from the use of surgical
valves and endovascular stents in understanding the vascular response of arteries to
the introduction of a mechanical scaffold. However, the combination of valve replacement with radial anchoring requires new approaches to understand the interactions
between device design, device placement, and procedural efficacy. The procedure has
shown significant improvements in quality of life and life expectancy for patients, but
serious complications still exist. As such, it is important for medical interventionists
and design engineers to better understand the interaction between the device and the
anatomic environment.
A framework has been presented that is capable of modeling patient specific
valve deformations in order to mechanistically investigate severe adverse events. This
framework has been applied to paired pre and post procedure CT data of patients
with severe adverse events. Two examples are shown, one with normal deployment
and one with a buckling type failure. In both cases, the valves experienced a wide
range of torsional, bending, and radial deformation. This analysis underscores the
pressing unmet need to better understand how calcium distribution impacts a patient's susceptibility to clinical complications. This question is of clinical, academic,
and industrial importance and is crucial to improving patient outcomes.
83
84
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Appendix A
1. Introductory Video Highlighting the Transcatheter Aortic Valve Procedure
9 http:,'/ youtu.be/KArINj4TFwQ
A.1
Clinical Variables
Body Mass Index
STS Mortality
STS Morbimortality
Gender
i s
Ag
(TricuspidBcuspid)
EuroScoreII
NH
Figure A-1: Clinical Data Available- Risk Scores
95
S Aortic
Pace Maker
DM
ValveNepsa
\NYHA
DVT
Co-Morbidities
rvouasy
Anemia
COPD
Previous
Carotid Artery
Disease
PIPrevious
Prvos
HTN
CVA
Figure A-2: Clinical Data Available- Comorbidities
Imeitely ate
Angiography
Vascular
Access
PrcdrlJProsthsis
Fluoroscopy
Contrast
Date of Study
I(mm/dd/yyyy)
Prosthiesis
Type
French
Introducer
Pre Implant
Balloon Valvuloplasty
Post Implant
Balloon Valvuloplasty
Figure A-3: Clinical Data Available- Procedural
96
Acute Renal
Failure
Intra-Hospital
Death
AF
Hospitalization
ClearanceCr
Pre TAVI
e C i
at hCatinine
Post TAViM eA
CK-M[B
Outcomes A
Days Ot tCCU
Definitive
Pacemaker
Cariogeneic
Respiratory
Infection
Access
Complicatios
post TAI
Creti ine
hk
Figure A-4: Clinical Data Available- Outcomes
97
TTE Max.
Gradient 6M
TTE Median
Gradient 6M
Insufficiency 6M ,
TTE Aortic
Insufficiency Type 6M
TTE Median
Gradient Dichrg
ETT_6MLVEDD
TTE LA
6M
TEE Follow Up
TTEAortic
Insufficiency 6M
Discharge
TEE
orceain Diameter
TEE Aortic
T EE Pre
|Root Diameter
TEE LVOTr~re
TEE Ascending
Aorta
Aora
t e n TTELVm
a ss
TTE EjecFrac
e
TTE Max.
TTELVEDV
TET
TTELA
TTE LA
Gradient Discharge
TTELVESV
TEE Discharge
TTEuffcAenc
IVS
TTE LVEDD
TTE Aortic
niscgerge
i o nEH M
yitra lp
TTE Pulmonary
TEE Sinotubular
Junction Diameter
V TE Mass
TT E IVS
6M
TTE EF
TTE Aortic
Insufficiency
arg
Insffi eny DschrgeInsufficiency Discharge
Inufi eD
Figure A-5: Clinical Data Available- Echo
TTE Pre TAVI
Aortic Insufficiency
TTExrd
TEVD
TTE LVEDD
Discharge
00