Cardiac MRI is limited by long acquisition times, which can lead to patient discomfort and motion artifacts. We aim to accelerate Cartesian dynamic cardiac MRI by learning efficient, scan-adaptive undersampling patterns that preserve diagnostic image quality. We develop a learning-based framework for designing scan- or slice-adaptive Cartesian undersampling masks tailored to dynamic cardiac MRI. Undersampling patterns are optimized using fully sampled training dynamic time-series data. At inference time, a nearest-neighbor search in low-frequency k-space selects an optimized mask from a dictionary of learned patterns. Our learned sampling approach improves reconstruction quality across multiple acceleration factors on public and in-house cardiac MRI datasets, including PSNR gains of 2-3 dB, reduced NMSE, improved SSIM, and higher radiologist ratings. The proposed scan-adaptive sampling framework enables faster and higher-quality dynamic cardiac MRI by adapting k-space sampling to individual scans.
@article{arxiv.2602.13984,
title = {Scan-Adaptive Dynamic MRI Undersampling Using a Dictionary of Efficiently Learned Patterns},
author = {Siddhant Gautam and Angqi Li and Prachi P. Agarwal and Anil K. Attili and Jeffrey A. Fessler and Nicole Seiberlich and Saiprasad Ravishankar},
journal= {arXiv preprint arXiv:2602.13984},
year = {2026}
}