Research Areas

Current research in the Slomka Laboratory focuses on developing innovative methods for fully automated analysis of nuclear cardiology data using novel algorithms and machine learning techniques, and on the development of integrated motion-corrected analysis of positron emission tomography (PET)/computed tomography (CT) angiography imaging.

This research was originally published in the Journal of Nuclear Medicine. Rubeaux M, Joshi NV, Dweck MR, Fletcher A, Motwani M, Thomson LE, Dey D, Li D, Berman DS, Newby DE, Slomka PJ. Motion correction of 18F-NaF PET for imaging coronary atherosclerotic plaques. J Nucl Med. 2016;57(1):54-59. © by the Society of Nuclear Medicine and Molecular Imaging, Inc.

The figure above shows noise decrease and target-to-background ratio improvement in 18F-sodium fluoride PET images displaying a 3-D rendering of end-diastolic image (left) and a motion-corrected image (right) superimposed on CT angiography 3-D rendering. Increased uptake is seen in all coronary arteries but it is difficult to differentiate from the noise in the end-diastolic image and becomes clearly visible in the motion-corrected image.

With permission from Betancur J, Commandeur F, Motlagh M, et al, Prognostic Value of Combined Clinical and Myocardial Perfusion Imaging Data Using Machine Learning, JACC Cardiovasc Imaging, 11 (17), 1000-1009, Elsevier, 2018. 

High Performance Automated System for Analysis of Fast Cardiac Single-Photon Emission Computed Tomography

Coronary artery disease remains a major public health problem worldwide. It causes approximately one of every six deaths in the United States. Imaging of myocardial perfusion (delivery of blood to the heart muscle) by myocardial perfusion single-photon emission tomography—a myocardial perfusion scan (MPS)—allows physicians to detect disease before a heart attack and predict risk in millions of patients annually. MPS is currently limited by the need for visual interpretation, which is highly variable and depends on the physician’s experience. The long-term objective of our research is to improve the interpretation of this widely used heart imaging technique—achieving higher accuracy for disease detection than is possible by the best attainable visual analysis. This work focuses on fast, low-radiation MPS imaging (fast-MPS) obtained by new high-efficiency scanners.

Specifically, the Slomka Laboratory aims to

  • Develop new image-processing algorithms for 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.
  • Enhance the diagnosis of heart disease from fast-MPS by machine-learning algorithms that integrate clinical data, stress test parameters and quantitative image features.
  • Demonstrate the clinical utility of the new algorithms applied to automatic canceling of the rest portion of the MPS scan when not needed.

Research in the Slomka Lab will demonstrate that the computer decision regarding rest-scan cancellation is safe for the patient, from both the diagnostic and prognostic standpoint. This will lead to widespread 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.

In the course of this research, the Slomka Lab has formed an international multisite registry of next-generation myocardial perfusion single-photon emission computed tomography (SPECT) imaging (REFINE SPECT) with all imaging data, diagnostic correlations and prognostic outcomes of more than 20,000 scans.

Integrated Analysis of Coronary Anatomy and Biology using 18F-Fluoride PET and CT Angiography
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