An unsupervised method for MRI recovery: deep image prior with structured sparsity

Objective To propose and validate an unsupervised MRI reconstruction method that does not require fully sampled k-space data. Materials and methods The proposed method, deep image prior with structured sparsity (DISCUS), extends the deep image prior (DIP) by introducing group sparsity to frame-speci...

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Bibliographic Details
Published in:	Magma (New York, N.Y.) Vol. 38; no. 5; pp. 859 - 871
Main Authors:	Sultan, Muhammad Ahmad, Chen, Chong, Liu, Yingmin, Gil, Katarzyna, Zareba, Karolina, Ahmad, Rizwan
Format:	Journal Article
Language:	English
Published:	Cham Springer International Publishing 01.10.2025 Springer Nature B.V
Subjects:	Algorithms Basic Science - Reconstruction algorithms and artificial intelligence Biomedical Engineering and Bioengineering Computer Appl. in Life Sciences Computer Simulation Contrast Media Data acquisition Deep Learning Evaluation Fourier transforms Gadolinium Gadolinium - chemistry Health Informatics Heart - diagnostic imaging Humans Image Enhancement - methods Image Interpretation, Computer-Assisted - methods Image Processing, Computer-Assisted - methods Image reconstruction Imaging Magnetic resonance imaging Magnetic Resonance Imaging - methods Manifolds Medical imaging Medicine Medicine & Public Health Phantoms, Imaging Radiology Reproducibility of Results Research Article Retrospective Studies Solid State Physics Sparsity Temporal variations Unsupervised Machine Learning Vectors Reconstruction Unsupervised learning Cardiac MRI
ISSN:	1352-8661, 0968-5243, 1352-8661
Online Access:	Get full text
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