Accelerating Quantitative MRI Using Subspace Multiscale Energy Model (SS-MuSE)

Yan Chen; Jyothi Rikhab Chand; Steve R. Kecskemeti; James H. Holmes; Mathews Jacob

doi:10.1109/ISBI60581.2025.10980741

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Accelerating Quantitative MRI Using Subspace Multiscale Energy Model (SS-MuSE)

Conference proceeding

Open access

Accelerating Quantitative MRI Using Subspace Multiscale Energy Model (SS-MuSE)

Yan Chen, Jyothi Rikhab Chand, Steve R. Kecskemeti, James H. Holmes and Mathews Jacob

Proceedings (International Symposium on Biomedical Imaging), pp.1-5

04/14/2025

DOI: 10.1109/ISBI60581.2025.10980741

PMCID: PMC12381881

PMID: 40881623

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https://pmc.ncbi.nlm.nih.gov/articles/PMC12381881/View

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Abstract

Multi-contrast MRI methods acquire multiple images with different contrast weightings, which are used for the differentiation of the tissue types or quantitative mapping. However, the scan time needed to acquire multiple contrasts is prohibitively long for 3D acquisition schemes, which can offer isotropic image resolution. While deep learning-based methods have been extensively used to accelerate 2D and 2D + time problems, the high memory demand, computation time, and need for large training data sets make them challenging for large-scale volumes. To address these challenges, we generalize the plug-and-play multi-scale energy-based model (MuSE) to a regularized subspace recovery setting, where we jointly regularize the 3D multi-contrast spatial factors in a subspace formulation. The explicit energy-based formulation allows us to use variable splitting optimization methods for computationally efficient recovery.

Magnetic Resonance Imaging

Biological system modeling

Computational modeling

Data models

Energy-based Model

Iterative methods

Learning systems

MPnRAGE

Multi-contrast MRI

Optimization methods

Plug-and-Play

Solid modeling

Subspace

Three-dimensional displays

Training data

Details

Title: Subtitle: Accelerating Quantitative MRI Using Subspace Multiscale Energy Model (SS-MuSE)
Creators: Yan Chen - University of Virginia
Jyothi Rikhab Chand - University of Virginia
Steve R. Kecskemeti - University of Wisconsin–Madison
James H. Holmes - University of Iowa
Mathews Jacob - University of Virginia
Resource Type: Conference proceeding
Publication Details: Proceedings (International Symposium on Biomedical Imaging), pp.1-5
DOI: 10.1109/ISBI60581.2025.10980741
PMID: 40881623
PMCID: PMC12381881
eISSN: 1945-8452
Publisher: IEEE
Number of pages: 5
Grant note: R01AG067078,R01AG087159,R01HD108868,P50HD103556,R01EB019961 / NIH (10.13039/100000002)
Language: English
Date published: 04/14/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering
Record Identifier: 9984824228302771

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