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

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

doi:10.48550/arxiv.2502.10580

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

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

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

ArXiV.org

Cornell University

02/14/2025

DOI: 10.48550/arxiv.2502.10580

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https://doi.org/10.48550/arxiv.2502.10580View

Preprint (Author's original)This preprint has not been evaluated by subject experts through peer review. Preprints may undergo extensive changes and/or become peer-reviewed journal articles. Open Access

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.

Physics - Medical Physics

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
Steven R Kecskemeti - University of Wisconsin–Madison
James H Holmes - University of Iowa
Mathews Jacob - University of Virginia
Resource Type: Preprint
Publication Details: ArXiV.org
DOI: 10.48550/arxiv.2502.10580
ISSN: 2331-8422
Publisher: Cornell University; Ithaca, New York
Language: English
Date posted: 02/14/2025
Academic Unit: Roy J. Carver Department of Biomedical Engineering; Radiology; Electrical and Computer Engineering
Record Identifier: 9984792375402771

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