From Compressed-Sensing to Deep Learning MR: Comparative Biventricular Cardiac Function Analysis in a Patient Cohort.

Background: Conventional segmented, retrospectively gated cine (Conv-cine) is challenged in patients with breath-hold difficulties. Compressed sensing (CS) has shown values in cine imaging but generally requires long reconstruction time. Recent artificial intelligence (AI) has demonstrated potential in fast cine imaging.

Dark blood T2-weighted imaging of the human heart with AI-assisted compressed sensing: a patient cohort study.

Background: Dark blood T2-weighted (DB-T2W) imaging is widely used to evaluate myocardial edema in myocarditis and inflammatory cardiomyopathy. However, this technique is sensitive to arrhythmia, tachycardia, and cardiac and respiratory motion due to the long scan time with multiple breath-holds. The application of artificial intelligence (AI)-assisted compressed sensing (ACS) has facilitated significant progress in accelerating medical imaging.