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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Vydané v:	Magma (New York, N.Y.) Ročník 38; číslo 5; s. 859 - 871
Hlavní autori:	Sultan, Muhammad Ahmad, Chen, Chong, Liu, Yingmin, Gil, Katarzyna, Zareba, Karolina, Ahmad, Rizwan
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Cham Springer International Publishing 01.10.2025 Springer Nature B.V
Predmet:	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
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Popis
Shrnutí:	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-specific code vectors, enabling the discovery of a low-dimensional manifold for capturing temporal variations. DISCUS was validated using four studies: (I) simulation of a dynamic Shepp–Logan phantom to demonstrate its manifold discovery capabilities, (II) comparison with compressed sensing and DIP-based methods using simulated single-shot late gadolinium enhancement (LGE) image series from six distinct digital cardiac phantoms in terms of normalized mean square error (NMSE) and structural similarity index measure (SSIM), (III) evaluation on retrospectively undersampled single-shot LGE data from eight patients, and (IV) evaluation on prospectively undersampled single-shot LGE data from eight patients, assessed via blind scoring from two expert readers. Results DISCUS outperformed competing methods, demonstrating superior reconstruction quality in terms of NMSE and SSIM (Studies I–III) and expert reader scoring (Study IV). Discussion An unsupervised image reconstruction method is presented and validated on simulated and measured data. These developments can benefit applications where acquiring fully sampled data is challenging.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1352-8661 0968-5243 1352-8661
DOI:	10.1007/s10334-025-01257-z