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

• Published in: Journal of magnetic resonance imaging Vol. 59; no. 4; pp. 1231 - 1241
• Main Authors: Yan, Xianghu, Luo, Yi, Chen, Xiao, Chen, Eric Z., Liu, Qi, Zou, Lixian, Bao, Yuwei, Huang, Lu, Xia, Liming
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.04.2024; Wiley Subscription Services, Inc
• Subjects: Artificial intelligence; biventricle; compressed sensing; Confidence intervals; Correlation coefficient; Correlation coefficients; Deep learning; Diastole; Field strength; function; Function analysis; Heart; Image processing; Image quality; Image reconstruction; Machine learning; Parameters; Statistical analysis; Statistical significance; Statistical tests; Ventricle; deep learning; compressed sensing; biventricle; artificial intelligence; function

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...