MRI recovery with self-calibrated denoisers without fully-sampled data

Objective Acquiring fully sampled training data is challenging for many MRI applications. We present a self-supervised image reconstruction method, termed ReSiDe, capable of recovering images solely from undersampled data. Materials and methods ReSiDe is inspired by plug-and-play (PnP) methods, but...

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Bibliographic Details
Published in:	Magma (New York, N.Y.) Vol. 38; no. 1; pp. 53 - 66
Main Authors:	Shafique, Muhammad, Liu, Sizhuo, Schniter, Philip, Ahmad, Rizwan
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.02.2025
Subjects:	Algorithms Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Brain - diagnostic imaging Calibration Computer Appl. in Life Sciences Health Informatics Heart - diagnostic imaging Humans Image Processing, Computer-Assisted - methods Imaging Magnetic Resonance Imaging - methods Medicine Medicine & Public Health Phantoms, Imaging Radiology Reproducibility of Results Research Article Signal-To-Noise Ratio Solid State Physics Reconstruction MRI Self-supervised
ISSN:	1352-8661, 0968-5243, 1352-8661
Online Access:	Get full text
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