May 8, 2024, 4:42 a.m. | Jinho Kim, Marcel Dominik Nickel, Florian Knoll

cs.LG updates on

arXiv:2405.03732v1 Announce Type: cross
Abstract: This study accelerates MR cholangiopancreatography (MRCP) acquisitions using deep learning-based (DL) reconstruction at 3T and 0.55T. Thirty healthy volunteers underwent conventional two-fold MRCP scans at field strengths of 3T or 0.55T. We trained a variational network (VN) using retrospectively six-fold undersampled data obtained at 3T. We then evaluated our method against standard techniques such as parallel imaging (PI) and compressed sensing (CS), focusing on peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) as metrics. Furthermore, …

abstract acquisitions arxiv cs.lg data deep learning eess.iv network scans six study type

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