APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
APPENDIX
THE EVALUATED SOFTWARE PACKAGES
Carimas
In Carimas (Turku PET Centre, University of Turku and Turku University Hospital, Turku,
Finland), the myocardial segmentation is performed semi-automatically—long and short axes
being defined by a user. The input function from the LV cavity is defined as a reduced LV
volume. Therefore, the LV and the cavity volumes strongly depend on the user’s definition.
Due to the RUBY-10 project, Carimas retained only the 1TCM as in Lortie et al. (1). The
program calculates global MBF based on a myocardial global TAC; the same principle
applies to regional and segmental—17-segment AHA model (13)—MBF values. There are
two ways to control and assure quality of the image analysis: (a) visualization of segmentation
results with broad adjusting capabilities and (b) plotting the fitted TAC with corresponding
data and goodness of fit displayed in the modelling results table.
Corridor4DM
In Corridor4DM (INVIA Medical Imaging Solutions, Ann Arbor, Michigan, United States),
the myocardial segmentation is performed automatically. Corridor4DM obtains the input
blood TACs using one of three options: (a) Generalized Factor Analysis for Dynamic
Sequences (GFADS), (b) GFADS-Hybrid and (c) ROI. The GFADS algorithm automatically
finds the RV and LV input TACs with the expected advantage of reduced spillover and noise
influence (2). For the GFADS-Hybrid method, the RV and LV input TACs are automatically
defined from non-contiguous ROIs to estimate the K1 uptake (3). In both GFADS cases, the
MBF is computed using the Yoshida Renkin-Crone (R-C) relation (4). For the ROI method,
Corridor4DM obtains only an LV input function from an ROI centered between the left
atrium and ventricle and uses the Lortie R-C relation. Corridor4DM estimates the MBF using
a 1TCM for 82Rb and a 2TCM for 13N-ammonia and then the MFR is computed as a ratio of
the stress to rest MBF. Results are reported globally, for each vascular region, and for each
17-segment polar map region. Quality assurance is provided by the TAC plot and factor
images.
FlowQuantTM
In FlowQuant (National Cardiac PET Center, University of Ottawa Heart Institute, Ottawa,
Canada), the myocardial segmentation and the definition of the input function from the LV
Manuscript of 07.02.2014—Page 1 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
cavity are performed automatically, with optional user adjustments. FlowQuant is the original
implementation of the Lortie’s 1TCM for
82
Rb PET tracer kinetic analysis (1). MFR polar
maps of stress/rest MBF are computed, and statistics are generated for segmental and regional
levels. Global spillover and regional partial-volume corrections are performed using
geometric mixing models. FlowQuant’s quality assurance displays include LV orientation, 3D
polar-map and blood-pool sampling, dynamic motion assessment, partial-volume and
spillover corrections, and kinetic modeling goodness-of-fit metrics.
HOQUTO
In HOQUTO (Hokkaido University Graduate School of Medicine, Sapporo, Japan), the
myocardial segmentation and the definition of the input function from the LV cavity are
performed semi-automatically: the program automatically sets ROIs and produces regional
MBF values after an operator selects the landmark—the insertion of the RV into the septum.
HOQUTO uses 1TCM as well as the Lortie’s approach (1), but with a different tracer
extraction correction function. The characteristics of this approach include dual-spillover
model and blood-input weighted fitting. HOQUTO was designed for estimating
with 3D PET and was validated by 2D
15
82
Rb MBF
O-labeled water PET, which is considered as
standard MBF measurement (5). The prompt-gamma corrections (PGC) for
82
Rb vary
between 3D PET and PET-CT scanner manufacturers (6). The PET-CT scanner used to
perform the current study included a PGC different from the PET studies used originally to
develop HOQUTO for
82
Rb flow quantification. Thus, HOQUTO might have required the
adjustment its PGC and/or spillover correction factors for the current study.
ImagenQ
ImagenQ (Cardiovascular Imaging Technologies, Kansas City, Missouri, United States)
provides automatic absolute quantitative measurement of MBF for both list-mode and non-list
mode systems by using a rapid dynamic acquisition (8x12 sec, 2x27 sec). In ImagenQ the
myocardial segmentation is performed automatically using a wavelet based edge detection
algorithm (7). The definition of the input function from the LV cavity is performed
automatically, but can be refined manually by the operator. ImagenQ implements the Lortie’s
1TCM (1) and the Yoshida retention model (4, 7). Both of these models as applied in
ImagenQ utilize a shorter dynamic acquisition time to improve the robustness of the model
and extend applicability to non-list-mode systems.
Manuscript of 07.02.2014—Page 2 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
Calculation of the quantitative values can be displayed within the application as global,
regional or 17 segment—AHA model—values. The program’s quality control suite includes
visual and quantitative assessment of the infusion timing, blood pool placement, washout,
ROI placement and image noise.
MunichHeart
In MunichHeart (Department of Nuclear Medicine, Technical University, Munich, Germany)
the myocardial segmentation is performed automatically in a late summed image—when the
tracer sufficiently cleared from the blood pool—after an initial definition of the long axis in
the rest study. Motion correction is performed from the last frame preceding the one where
the blood signal equals the tissue signal. The volume of interest for the input function is
centered in basal slices of the motion corrected data to avoid spillover effects. For
82
Rb
MunichHeart uses a retention model with a validated correction to estimate absolute MBF (8).
The developers of MunichHeart prefer this
approaches: possibly noisy
82
82
Rb data analysis approach to 1TCM or 2TCM
Rb data and low tracer extraction will require amplifications
during the correction step, and this requirement as well as the preference to a high effective
spatial resolution would be served better with the retention approach. The MBF is estimated
for 460 polar map elements. After processing of the rest study, the stress scan is automatically
aligned and the MBF quantification for a stress image is repeated. MFR maps are created
elements-wise as the rest and stress polar maps describe spatially matching locations. MBF
and MFR maps can be averaged into the three standard vessel beds (the regional level),
personalized vessel territories, and the 17-segment AHA model. After the initial long axis
definition, the process is fully automated. The retention approach reduces the need for
extensive quality control.
PMOD
In PCARD—the cardiac tool of PMOD (PMOD Technologies, Zürich, Switzerland)—the
myocardial segmentation is performed automatically. The stepwise procedure automatically
performs heart reorientation into short-axis slices based on the early and late uptake,
definition of the myocardium centerline by fitting a model shape to the data, and polar
sampling to calculate the segmental time-activity curves according to the standard 17-segment
AHA model. The activity in the LV cavity is used for deriving the input function as well as
for the blood spillover correction in the model. For the septal segments, spillover of RV
activity is also accounted for. For
82
Rb, the Lortie’s 1TCM (1) is preferred, yet Herrero’s
Manuscript of 07.02.2014—Page 3 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
2TCM (9) exists to serve for historic comparisons. Two measures of global MBF can be
obtained, by modeling the summation curve of all segments, or by volume-weighted
averaging of the segmental perfusion estimates. Each step in the PCARD’s workflow can be
reviewed and adjusted by the user, and it assures the quality of the analysis.
QPET
In QPET (Cedars-Sinai Medical Center, Los Angeles, California, United States) the
myocardial segmentation is performed automatically with a recently described improved
algorithm (10). Briefly, the LV contour is determined from the summed dynamic image data
skipping the first two minutes—a method that is based on the original quantitative gated
SPECT contour detection principles (11) and the improved valve-plane definition of ACcorrected high-resolution PET. The 3D cylindrical region for the LV input function is
automatically placed in the middle of the valve plane, with a 1- by 2-cm length oriented along
the long axis of the heart. The Lortie’s 1TCM (1) is used, including regional uptake and
clearance parameters (K1 [mL/min/g] and k2[min−1]), blood-to-myocardium spillover fraction
fb, and myocardial partial-volume corrections (1 – fb). The dynamic myocardial samples are
obtained from the polar map by analyzing all time frames within the fixed LV contour
boundaries (12). To reduce noise in the time–activity curves, QPET computations are
performed in 70 myocardial regions with equal surface areas. The global values are computed
within the whole LV region bounded by the valve plane. Stress and rest MBF are computed
for each sample in the polar map. MFR is computed by dividing the stress polar map by the
rest values at each point. MBF in each vascular territory is obtained by averaging the polar
map segments according to the standard 17-segment AHA model. Manual correction of the
LV can be performed by modifying the initial mask for the LV region but it is not required in
the majority of cases.
syngo MBF
In syngo MBF (Siemens Medical Solutions, USA) the myocardial segmentation is performed
automatically. The arterial blood input function is obtained by averaging the activity in a
cylindrical region of interest placed automatically in the middle of the LV in the basal region.
The tool supports the Lortie’s 1TCM (1) where the rate constants K1 [mL/min/g] and k2
[min−1] and blood-to-myocardium spillover fraction fb are fitted independently to each other.
The MBF and MFR values are computed on 505 segments and reported for 17-segment
regions, three vascular regions, and globally, which can be compared with user-defined
Manuscript of 07.02.2014—Page 4 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
database. Each case is processed with a quality control step in which the operator confirms or
modifies the results of automatic reorientation (14) and invokes an additional option for
advanced motion correction (15) if needed.
UW-QPP
In UW-QPP (The University of Washington Quantitative PET Perfusion, Seattle, USA) the
myocardial segmentation is performed automatically by finding the mid-line of the
myocardial wall and then iteratively selecting the edge/boundary of the myocardium.
The tool supports conventional compartmental models (e.g. the Lortie’s 1TCM) as well as
axially distributed models, both of which have been extensively validated for
(16) and partially validated for
13
N-ammonia
82
Rb (17) against microspheres in a canine model. Axially
distributed models are more physiologically realistic than compartmental models, but the
relative benefit of this realism in the context of dynamic PET imaging is arguably small (16).
While other flow estimate spatial resolution is supported, for this work, the 17 segmental and
the three regional flows were estimated; the global flow was calculated as the average of the
17 flow estimates. Quality is assured because the user can manually review and adjust all of
the main processing steps—reorientation, input function selection, segmentation, modeling,
and visualization of flow estimates.
Manuscript of 07.02.2014—Page 5 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
PET IMAGE ACQUISITION
A brief CT-scout was acquired, followed by CT attenuation correction (AC) scan (120kV,
10mA); CT AC image alignment with PET was verified visually by an experienced
technologist and corrected if necessary by manual 3D translation, using the vendor’s ACQC
program. PET scans were acquired on a Discovery 690 PET/CT (GE Healthcare, Milwaukee,
WI, USA) using a 3D list-mode acquisition after a 30-s (constant-activity square-wave)
infusion of
82
Rb (10 MBq/kg, Jubilant DraxImage, Kirkland, Canada). An 8-minute rest
acquisition was started ~10-15 seconds after starting the intravenous 82Rb infusion. Following
the rest data acquisition, patients underwent the pharmacological stress study. The patient kept
the same position, while adenosine at 0.84 mg/kg was infused over 6 minutes. Two minutes
after the start of adenosine infusion,
82
Rb infusion was started. The PET acquisition for
pharmacological stress was performed in the same way as described for rest (18). Dynamic
images were reconstructed using the vendor VPFX time-of-flight algorithm (2 iterations and
24 subsets) into 24 time frames (12 x 8s, 5 x 12s, 1 x 30s, 1 x 60s, 2 x 120s), with 6.4 mm 3D
Gaussian post-filtering.
Manuscript of 07.02.2014—Page 6 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
THE STUDY DESIGN
The RUBY-10 project started in March 2012 with nine software packages included. Each of
these tools had one or more mathematical models for quantification of myocardial blood flow
using
82
Rb and all data were analyzed using all available models. In June 2012, the first
results were received and preliminary analysis was performed. Based on the initial analysis,
faulty implementation and consequent exclusion of three models from two packages was
agreed upon by the study team. At this point also the tenth member—CVIT—included their
package ImagenQ (4) into the RUBY project. One of the three earlier excluded models was
re-implemented in UW-QPP and its results were reintroduced into the pool. Later also CVIT
added 1TCM (1) into the ImagenQ and these results were also included. As a result, we
included 10 software programs in this study. Table 1 of the paper displays the software and
models included in the final analysis. Of note, none of the participants had access to the
individual results from other analyses before their own database was closed.
Manuscript of 07.02.2014—Page 7 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
APPENDIX REFERENCES
1. Lortie M, Beanlands RSB, Yoshinaga K, Klein R, Dasilva JN, DeKemp RA.
Quantification of myocardial blood flow with 82Rb dynamic PET imaging. Eur. J.
Nucl. Med. Mol. Imaging 2007;34(11):1765–74.
2. El Fakhri G, Sitek A, Guérin B, Kijewski MF, Di Carli MF, Moore SC. Quantitative
dynamic cardiac 82Rb PET using generalized factor and compartment analyses. J.
Nucl. Med. 2005;46(8):1264–71.
3. Sitek A, Di Bella EV, Gullberg GT. Factor analysis with a priori knowledge-application in dynamic cardiac SPECT. Phys Med Biol 2000;45(9):2619–38.
4. Yoshida K, Mullani N, Gould KL. Coronary flow and flow reserve by PET simplified
for clinical applications using rubidium-82 or nitrogen-13-ammonia. J. Nucl. Med.
1996;37(10):1701–12.
5. Katoh C, Yoshinaga K, Klein R, et al. Quantification of regional myocardial blood
flow estimation with three-dimensional dynamic rubidium-82 PET and modified
spillover correction model. J Nucl Cardiol 2012;19(4):763–74.
6. Renaud JM, Mylonas I, McArdle B, et al. Clinical Interpretation Standards and
Quality Assurance for the Multicenter PET/CT Trial Rubidium-ARMI. J. Nucl. Med.
2014;55(1):58–64.
7. Saha K, Case JA, Cullom SJ, et al. Automated detection of Myocardium Boundary in
Rb-82 PET Images using Wavelet based approach. IEEE NSS/MIC Conf. Record
2006;4(1):2068-2071.
8. Lautamäki R, George RT, Kitagawa K, et al. Rubidium-82 PET-CT for quantitative
assessment of myocardial blood flow: validation in a canine model of coronary artery
stenosis. Eur. J. Nucl. Med. Mol. Imaging 2009;36(4):576–86.
9. Herrero P, Markham J, Shelton ME, Bergmann SR. Implementation and evaluation of
a two-compartment model for quantification of myocardial perfusion with rubidium82 and positron emission tomography. Circ. Res. 1992;70(3):496–507.
10. Nakazato R, Berman DS, Dey D, et al. Automated quantitative Rb-82 3D PET/CT
myocardial perfusion imaging: normal limits and correlation with invasive coronary
angiography. J Nucl Cardiol 2012;19(2):265–76.
11. Germano G, Kavanagh PB, Chen J, et al. Operator-less processing of myocardial
perfusion SPECT studies. J. Nucl. Med. 1995;36(11):2127–32.
12. Slomka PJ, Alexanderson E, Jácome R, et al. Comparison of clinical tools for
measurements of regional stress and rest myocardial blood flow assessed with 13Nammonia PET/CT. J. Nucl. Med. 2012;53(2):171–81.
13. Cerqueira MD, Weissman NJ, Dilsizian V, et al. Standardized myocardial
segmentation and nomenclature for tomographic imaging of the heart. A statement for
healthcare professionals from the Cardiac Imaging Committee of the Council on
Clinical Cardiology of the American Heart Association. Circulation 2002;105(4):539–
42.
Manuscript of 07.02.2014—Page 8 of 9
APPENDIX to Nesterov et al. –”Cross-comparison of 82Rb MPI PET software”
14. Xiao-Bo Pan, Schindler T, Ratib O, Nekolla S, Declerck J. Effect of reorientation on
myocardial blood flow estimation from dynamic 13NH3 PET imaging. In: 2009 IEEE
Nuclear Science Symposium Conference Record (NSS/MIC): IEEE, 2009:3715–6.
15. Bond S, Pan XB, Declerck J. A study of consistency of myocardial blood flow
calculation using motion correction in dynamic PET [abstract]. J Nucl Med.
2010;51(suppl 2):1326.
16. Alessio AM, Bassingthwaighte JB, Glenny R, Caldwell JH. Validation of an axially
distributed model for quantification of myocardial blood flow using ¹³N-ammonia
PET. J Nucl Cardiol 2013;20(1):64–75.
17. Alessio AM, Patel A, Lautamäki R, et al. Validation of axially-distributed models for
myocardial blood flow estimation from N-13 ammonia and Rb-82 cardiac PET
imaging [abstract]. ICNC, 2011.
18. Prior JO, Allenbach G, Valenta I, et al. Quantification of myocardial blood flow with
82Rb positron emission tomography: clinical validation with 15O-water. Eur. J. Nucl.
Med. Mol. Imaging 2012;39(6):1037–47
Manuscript of 07.02.2014—Page 9 of 9