PURPOSE: To employ four-dimensional (4D) flow MRI to investigate associations between hemodynamic parameters with systolic anterior motion (SAM), mitral regurgitation (MR), stroke volume, and cardiac mass in patients with hypertrophic cardiomyopathy ...
Purpose To develop and evaluate a free-breathing, highly accelerated, multisection, single-beat cine sequence for cardiac MRI. Materials and Methods This prospective study, conducted from July 2022 to December 2023, included participants with various...
Purpose To measure the benefit of single-phase CT, inspiratory-expiratory CT, and clinical data for convolutional neural network (CNN)-based chronic obstructive pulmonary disease (COPD) staging. Materials and Methods This retrospective study included...
Purpose To assess the influence of deep learning (DL)-based image reconstruction on acquisition time, volumetric results, and image quality of cine sequences in cardiac MRI. Materials and Methods This prospective study (performed from January 2023 to...
Purpose To assess the diagnostic performance of a coronary CT angiography (CCTA) artificial intelligence (AI)-enabled tool (AI-QCPA; HeartFlow) to quantify plaque volume, as compared with intravascular US (IVUS). Materials and Methods A retrospective...
Purpose To use unsupervised machine learning to identify phenotypic clusters with increased risk of arrhythmic mitral valve prolapse (MVP). Materials and Methods This retrospective study included patients with MVP without hemodynamically significant ...
Purpose To develop a deep learning model for increasing cardiac cine frame rate while maintaining spatial resolution and scan time. Materials and Methods A transformer-based model was trained and tested on a retrospective sample of cine images from 5...