Coronary computed tomography angiography (CCTA) is inferior to intravascular imaging in detecting plaque morphology and quantifying plaque burden. We aim to, for the first time, train a deep-learning (DL) methodology for accurate plaque quantification and characterization in CCTA using near-infrared spectroscopy-intravascular ultrasound (NIRS-IVUS).

Even with intracoronary imaging-guided stent optimisation, suboptimal haemodynamic outcomes post-percutaneous coronary intervention (PCI) can be related to residual lesions in non-stented segments. Preprocedural assessment of pathophysiological coronary artery disease (CAD) patterns could help predict the physiological response to PCI.

This systematic review aims to identify, evaluate, and summarize the findings of the literature on existing computational models for radiofrequency and microwave thermal liver ablation planning and compare their accuracy.

The use of serial coronary computed tomography angiography (CCTA) allows for the early assessment of coronary plaque progression, a crucial factor in averting major adverse cardiac events (MACEs). Traditionally, serial CCTA is assessed using anatomical landmarks to match baseline and follow-up scans. Recently, a tool has been developed that allows for the automatic quantification of local plaque thickness differences in serial CCTA utilizing plaque contour delineation. The aim of this study was to determine thresholds of plaque thickness differences that define whether there is plaque progression and/or regression. These thresholds depend on the contrast-to-noise ratio (CNR).