Abstract: Systems and methods are disclosed for applying attenuation correction to single photon emission computed tomography (SPECT) imaging data for myocardial perfusion imaging (MPI) studies. SPECT-MPI imaging data can be provided to a deep-learning model to automatically generate simulated computed tomography attenuation correction (CT-AC) images from the non-corrected (NC) SPECT-MPI imaging data. These simulated CT-AC images can then be used to perform attenuation correction on the SPECT-MPI imaging data to generate corrected SPECT-MPI imaging data. The deep-learning model can be trained using corresponding pairs of non-corrected SPECT-MPI imaging data and traditional CT-AC imaging data. The deep-learning model can be a conditional generative adversarial neural network (cGAN).

Abstract: Systems and methods are disclosed for automatically performing motion correction in dynamic positron emission tomography scans, such as dynamic positron emission tomography myocardial perfusion imaging studies. An automated algorithm can be used. The algorithm can use simplex iterative optimization of a count-based cost-function customized to different dynamic phases for performing frame-by-frame motion correction.

Abstract: A deep learning model for the detection of obstructive coronary artery disease (CAD) can take a set of polar maps and patient information as input, then output obstructive CAD scoring data, such as probabilities of obstructive CAD associated with various cardiac territories, as well as an attention map and a CAD scoring map. The model can operate agnostic of camera type used to capture the set of polar maps. The attention map indicates regions of the polar maps important to the deep learning process for that particular set of polar maps. The attention map and obstructive CAD scoring data can be used to generate a CAD scoring map showing CAD probability by segment on a standard 17-segment model of a left ventricle. The attention map and/or CAD scoring map can act as easily explainable tools for interpreting the results of a myocardial perfusion imaging study.

Abstract: Calcific and noncalcific aortic tissue components can be quantified. Pre-intervention planning computed tomography angiography imaging data is received. A region of interest is defined between the lower coronary ostium and the virtual basal ring. Cross-sectional images of the region of interest are rendered and calcific and noncalcific tissue components are identified based on Hounsfield unit thresholds. The volumes of the identified calcific and noncalcific tissue components are calculated and used to determine a total tissue volume (e.g., fibrocalcific volume) for the valve, as well as component percentages of the total tissue volume for the calcific and noncalcific components. These volumes and/or component percentages can be leveraged to predict severe AS, identify prognosis of post-TAVI outcomes, or otherwise facilitate planning of medical intervention.

Abstract: Assessment of transthyretin amyloid cardiomyopathy (ATTR-CM) can be performed by calculating a cardiac pyrophosphate activity (CPA) measurement from single-photon emission computed tomography (SPECT) images. SPECT images obtained using 99mTechnetium-pyrophosphate as a radiotracer can be analyzed to calculate CPA. Scan-specific thresholds for abnormal myocardial activity are identified based on left ventricular blood pool (LVBP) radiotracer counts, then radiotracer activity in regions of the myocardium with abnormal myocardial activity is determined. Finally, a CPA measurement can be calculated as a function of the mean radiotracer counts in such regions over the maximal LVBP radiotracer activity multiplied by the volume of involvement (e.g., the volume of abnormal activity). This CPA measurement can then be used as an assessment of ATTR-CM.