Coronary artery disease remains a major public health problem worldwide. It causes approximately 1 of every 6 deaths in the United States. Imaging of myocardial perfusion (delivery of blood to the heart muscle) by myocardial perfusion single photon emission tomography (MPS) allows physicians to detect disease before a heart attack, and predict risk in millions of patients annually. This is currently limited by the need fo visual interpretation, which is highly variable and depends on the physician's experience. The long-term objective of this program is to improve the interpretation of this widely used heart imaging technique-achieving higher accuracy for disease detection than it is possible by the best attainable visual analysis. This proposal builds on our prior work in conventional myocardial MPS, and focuses on fast, low-radiation MPS imaging (fast-MPS) obtained by new high-efficiency scanners. Specifically, we aim to: 1) develop new image processing algorithms for a fully automated analysis of fast-MPS. The algorithms will include better heart muscle detection by training with correlated anatomical data and a novel approach for mapping the probability of abnormal perfusion for each location of the heart muscle;2) enhance the diagnosis of heart disease from fast-MPS by machine- learning algorithms that integrate clinical data, stress test parameters, and quantitative image features;3) demonstrate the clinical utility of the new algorithms applied to automatic canceling of the rest portion of the MPS scan, when not needed. The new system will be more accurate than the clinical expert analysis in the detection of obstructive coronary disease. By immediately indicating whether a stress scan is normal, the system will allow for the automatic cancellation of the rest imaging portion when it is not needed (estimated in over 60% of all MPS studies). Our research will demonstrate that the computer decision regarding rest-scan cancellation is safe for the patient, both from a diagnostic and prognostic standpoint. This will lead to a wide adoption of low-dose stress-only imaging for MPS studies, which would reduce the amount of radiation that patients are exposed to, and allow for significant healthcare savings. It will additionally lead to a paradigm shift in the practice of nuclear cardiology, which will ultimately result in better selection of patients who need intervention, and reduce the number of deaths due coronary artery disease.

Public Health Relevance

Imaging of myocardial perfusion (heart muscle blood flow) at rest and stress allows physicians to detect disease and predict risk in millions of patients in the US each year, but it is currently limited by the need of visual interpretation, which is dependent on doctor's experience. The investigators propose to develop and validate an automated, highly-accurate and objective computer system which will outperform even experienced physicians in interpreting these images using latest generation scanners and novel machine learning computer tools. The computer will be able to better select patients needing treatment and automatically indicate the normal stress-scan immediately, and more accurately than physicians allowing automatic cancellation of the rest imaging, when not needed;this will result in large healthcare savings and reduced radiation to the patients.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
Research Project (R01)
Project #
2R01HL089765-06A1
Application #
8762268
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Buxton, Denis B
Project Start
2007-07-18
Project End
2018-05-31
Budget Start
2014-08-06
Budget End
2015-05-31
Support Year
6
Fiscal Year
2014
Total Cost
$653,934
Indirect Cost
$269,267
Name
Cedars-Sinai Medical Center
Department
Type
DUNS #
075307785
City
Los Angeles
State
CA
Country
United States
Zip Code
90048
Betancur, Julian A; Hu, Lien-Hsin; Commandeur, Frederic et al. (2018) Deep Learning Analysis of Upright-Supine High-Efficiency SPECT Myocardial Perfusion Imaging for Prediction of Obstructive Coronary Artery Disease: A Multicenter Study. J Nucl Med :
Haro Alonso, David; Wernick, Miles N; Yang, Yongyi et al. (2018) Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. J Nucl Cardiol :
Slomka, Piotr J; Betancur, Julian; Liang, Joanna X et al. (2018) Rationale and design of the REgistry of Fast Myocardial Perfusion Imaging with NExt generation SPECT (REFINE SPECT). J Nucl Cardiol :
Sharif, Behzad; Motwani, Manish; Arsanjani, Reza et al. (2018) Impact of incomplete ventricular coverage on diagnostic performance of myocardial perfusion imaging. Int J Cardiovasc Imaging 34:661-669
Betancur, Julian; Otaki, Yuka; Motwani, Manish et al. (2018) Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning. JACC Cardiovasc Imaging 11:1000-1009
Motwani, Manish; Leslie, William D; Goertzen, Andrew L et al. (2018) Fully automated analysis of attenuation-corrected SPECT for the long-term prediction of acute myocardial infarction. J Nucl Cardiol 25:1353-1360
Gomez, Javier; Doukky, Rami; Germano, Guido et al. (2018) New Trends in Quantitative Nuclear Cardiology Methods. Curr Cardiovasc Imaging Rep 11:
Betancur, Julian; Commandeur, Frederic; Motlagh, Mahsaw et al. (2018) Deep Learning for Prediction of Obstructive Disease From Fast Myocardial Perfusion SPECT: A Multicenter Study. JACC Cardiovasc Imaging 11:1654-1663
Išgum, Ivana; de Vos, Bob D; Wolterink, Jelmer M et al. (2018) Automatic determination of cardiovascular risk by CT attenuation correction maps in Rb-82 PET/CT. J Nucl Cardiol 25:2133-2142
Diaz-Zamudio, Mariana; Fuchs, Tobias A; Slomka, Piotr et al. (2017) Quantitative plaque features from coronary computed tomography angiography to identify regional ischemia by myocardial perfusion imaging. Eur Heart J Cardiovasc Imaging 18:499-507

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