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.
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.
Quantitative Prediction of Disease and Outcomes from Next Generation SPECT and CT
Coronary artery disease remains a major public health problem worldwide. It causes approximately one of every six deaths in the U.S. 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 heart attacks occur, and is currently used to predict risk in millions of patients annually.
The Slomka Laboratory has established an international multisite registry (REFINE SPECT) with all imaging data, diagnostic correlations and prognostic outcomes of more than 23,000 scans. Using this registry, the Slomka Lab has demonstrated that a combination of MPS image analysis and artificial intelligence (AI) tools achieved superior predictive performance compared to visual assessment by experienced readers or current state-of-the-art quantitative techniques.
The overall objective of this research is to optimize the clinical capabilities of MPS in risk prediction and treatment guidance by integrating all available imaging and clinical data with state-of-the-art AI methods.
Specifically, the Slomka Laboratory aims to
- Expand and enhance REFINE SPECT including CT and MPS flow data
- Develop fully automated techniques for all MPS and CT image analysis
- Apply explainable deep-learning, time-to-event AI models for optimal prediction of MACE and benefit from revascularization from all image and clinical data.
Research in the Slomka Lab will result in an immediately deployable clinical tool, which will optimally predict risk of adverse events and establish the relative benefits from specific therapies, beyond what is possible by subjective visual analysis and mental integration of all imaging (MPS, CT, flow), and clinical data by physicians. Most importantly, this research will allow patients to benefit from increased precision and accuracy in risk assessment, thereby optimizing the use of imaging in guiding patient management decisions and ultimately improving outcomes.