CMRxRecon: A publicly available k-space dataset and benchmark to advance deep learning for cardiac MRI.

ABSTRACT: Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a significant drawback of CMR is its slow imaging speed, resulting in low patient throughput and compromised clinical diagnostic quality. The limited temporal resolution also causes patient discomfort and introduces artifacts in the images, further diminishing their overall quality and diagnostic value. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have so far not been made publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi

SUBMITTER: Wang C 

PROVIDER: S-EPMC11199635 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature