NIBIB Strategic Plan:
Focus on Imaging Science
Belinda Seto, Ph.D.
Deputy Director
National Institute of Biomedical
Imaging and Bioengineering
Goal One
 Improve human health through the
development of emerging biomedical
technologies at the interface of engineering
and the physical and life sciences.
 2 examples: represent the convergence of
bioengineering, physical sciences, biology and
clinical sciences
Molecularly-Targeted Fluorescent Cell Penetrating Peptides for
Tumor and Nerve
Quyen Nguyen et al. Nature Biotechnology (NIBIB K08 Awardee)
Schematic diagram of
activatable CPPs. Cellular
uptake induced by a cationic
peptide is blocked by a short
stretch of acidic residues
attached by a cleavable linker.
©2004 by National Academy of Sciences
Surgery Guided with
Molecularly-Targeted
Fluorescence of Both Tumor and
Nerve
Portion of nerve embedded within tumor visible
with fluorescently labeled probe. Tumor and nerve
both invisible in standard white light reflected
image
Quyen Nguyen et al. Nature
Biotechnology
(NIBIB K08 Awardee)
Deep red Cy5 fluorescence from tumor-targeting
ACPP pseudocolored green and overlaid on standard
white-light reflectance image
Buried nerve branch
Green FAM fluorescence from
nerve imaging peptide
pseudocolored aqua
Transcranial MRI–Guided Focused Ultrasound Surgery of
Brain Tumors: Initial Findings in 3 Patients
(Neurosurgery 66:323-332, 2010)
Diagram of the transcranial MRI–guided focused ultrasound surgery device for noninvasive brain
tumor ablation being developed under R01EB003268, (PI: K. Hynynen). This work evaluated the
clinical feasibility of transcranial magnetic resonance imaging–guided focused ultrasound surgery
(McDannold et al, ). This work was also based on earlier funding by NCI grants: CA76550;
CA089017; CA46627
NIBIB Translation Research: World’s First MRGuided HIFU Treatment for Essential Tremor
PI on this study: W.
Jeffrey Elias, MD.,
U.Va.
Funded by the
Focused Ultrasound
Foundation.
K.Hynynen, U. Toronto, funded
by NIBIB, collaborated with
InSightec in adapting the
design of the applicator and
US delivery, based on research
funded under R01EB 3268,
ExAblate MR-guided High-Intensity Focused Ultrasound (Insightec, Inc.)
 Helmet-like applicator developed with 512 phased-array HIFU transducers
 Can be precisely focused within the brain.
 Entering FDA clinical trials
Magnetic Resonance
Elastography
In principle, the mechanical properties of tissue could be
assessed if a method could be developed to visualize
propagation of applied mechanical waves
Hard
Soft
The challenge in visualizing such waves is that they are
only microns in amplitude. How can such small motions
be reliably imaged?
Magnetic resonance imaging is a remarkably versatile technology.
A 1995 publication by Mayo Clinic researchers reported the
discovery of a method to visualize propagating shear waves in
tissue with amplitudes smaller than the wavelength of light…
MR Elastography
1. Driver
(30-500 Hz)
2. MRE Sequence
3. Inversion
Tissue-simulating gel
phantom with stiff
inclusions
2.5cm
Conventional
MR Image
-10
0
+10
0
40
80
Displacement (mm)
Shear Stiffness (kPa)
Wave Images
Elastogram
Importance of Chronic Liver Disease,
Fibrosis, Cirrhosis
 A leading cause of death worldwide
 Increasing prevalence of conditions
that cause hepatic fibrosis
• Hepatitis C - 170 M people
globally
• Hepatitis B
• Obesity / Fatty liver disease
 Fibrosis can be reversed, if
diagnosed early and treated
Progression of Liver Disease
Normal
Fibrosis
Reversible
Silent
Cirrhosis
Irreversible
High Mortality
Liver Biopsy
Standard Diagnostic Procedure to rule-out
Fibrosis
 Risk of Complications
 Potential Sampling Errors
 Subjective Histology Grading
Technology Developed for Hepatic MRE
10
Displacement (mm)
0
8
6
4
2
Elastogram
-90
Acoustic waves at 60Hz
Imaging time: 16 sec
Active Driver
0
(EB001981)
Shear Stiffness (kPa)
+90
Passive Driver
Chronic Liver Disease
Healthy
7.0 kPa
2.0 kPa
Biopsy: Grade 3 Fibrosis
F, 30 yrs
-60
M, 21 yrs
0
Amplitude (mm)
+60
-60
0
Amplitude (mm)
+60
Goal Two
 Enable Patient-centered health care
through development of health
informatics and mobile and point-of-care
technologies
 Example: Image sharing contracts
Image Data Sharing: NIBIB Activities
 Contract awarded to Radiological Society of
North America: Image Sharing Network,
PI: Dr. David Mendelson
 Grant awarded to Wake Forest University
 Grant awarded to University of Alabama
Goal Three
 Transform advances in medicine at the molecular
and cellular levels into therapeutic and diagnostic
technologies that target an individual’s personal
state of health
 Example: Mehmet Toner’s project on circulating
tumor cells
Key dimensions of the CTC-chip
45 mm
Biotinylated
anti-EpCAM
Capture
Antibody
Avidin
OH
OH
OH
OH
OH
OH
OH
Clinical Application
EpCAM
Lung, Prostate, Colon,
Pancreas, Breast
EGFR
Lung, Brain, Colon
HER-2
Breast
CD133
Lung, Prostate
CD44,
CD44v6
Lymphangioleiomyomatosis
NG2
Angiomyolipoma,
Melanoma
Automated Image Processing
CTC
PSA
(x1, y1)
f (x,y)
(x2, y2)
DNA
Native Signal
 12-bit monochrome
 Filter-based images
Threshold
 Intensity-based
 Brightest signals
(x3, y3)
Quantify
Filter
Classify
 Center of mass (xi,yi)  |(x1,y1) – (x2,y2)| < 2.5µm  Enumerate
 Area
 Elongation < 3.6
 Report
 Elongation factor
 AreaPSA > AreaDNA
 Image database
Stott, et al. Science Translational Medicine, 2010
Taking Imaging Beyond Enumeration
Novel classification schema for CTCs using crosscorrelation image processing algorithms
Ki67: Cell proliferation marker
M30: Cell apoptosis marker
Questions to be addressed:
Numbers
1. Can CTCs be detected in the blood of
patients with EGFR- and ALK-mutant
lung cancer?
2. Do CTC numbers change with
treatment?
Genotype
3. Can tumor-specific mutations be
detected in CTCs?
Signaling
4. Can signaling be measured in CTCs
(and does it change with targeted
therapy)?
5. Do the signaling effects in CTCs match
those of the primary tumor?
6. Does this predict clinical outcome?
Dynamic
RangeTrack
of CTC Enumeration
CTC
Numbers
with Disease
Course
Lung Cancer Patient Responding
Nagrath et al, Nature 2007
Lung Cancer Patient Not Responding
Goal Four
 Develop medical technologies that are low-cost,
effective, and accessible to everyone.
 Example: GE Vcan
 Transformative low cost mobile clinical
Ultrasound system. A hand held portable $8K
device that can be carried in a coat pocket and
has the same functionality of conventional
systems which cost ~$200K . Has been embraced
by leading cardiologist as a major development
to help realize mHealth and is described as
tomorrow’s stethoscope.
GE V-scan: World’s smallest portable
ultrasound
V-scan offers a chance for physicians to make a
move from stethoscopes to portable imaging
devices, bringing advanced visualization to any
examination room. V-scan can offer an
immediate look beyond patient vital signs with
the potential to identify critical issues, like fluid
around the heart, which could be a sign of
congestive heart failure. And for cardiologists,
Vscan provides a dependable visual evaluation of
how well the heart is pumping at a glance, so
they can treat patients more efficiently.
The handheld ultrasound can reduce the need for specialist
referrals, which in turn can lower healthcare costs.
NIBIB Grant R01EB002485: Kai Thomenius, Ph.D.
GE’s Chairman and CEO Jeff Immelt demonstrates
the new V-scan
Goal Five
 Develop training programs to prepare a
new generation of interdisciplinary
engineers, scientists, and health care
providers.
 Examples: R25 program for residents
NIBIB Research Education Program for
Residents and Fellows (R25)
 Target groups: residents or fellows interested in
developing research experience in
interdisciplinary areas such as quantitative
biology, biologically-inspired engineering, and/or
imaging science .
 12 month initial support, additional 12 months
possible
 75% effort
 $70,000 direct costs including $10,000 research
supplies and $1000 travel
Goal Six
• Expand public knowledge about the medical,
social, and economic value of
bioengineering, biomedical imaging, and
biomedical informatics.
• Examples of start-ups