Preprint
Accelerated MR Elastography Using Learned Neural Network Representation
ArXiv.org
Cornell University
01/17/2026
DOI: 10.48550/arxiv.2601.11878
Abstract
To develop a deep-learning method for achieving fast high-resolution MR elastography from highly undersampled data without the need of high-quality training dataset. We first framed the deep neural network representation as a nonlinear extension of the linear subspace model, then used it to represent and reconstruct MRE image repetitions from undersampled k-space data. The network weights were learned using a multi-level k-space consistent loss in a self-supervised manner. To further enhance reconstruction quality, phase-contrast specific magnitude and phase priors were incorporated, including the similarity of anatomical structures and smoothness of wave-induced harmonic displacement. Experiments were conducted using both 3D gradient-echo spiral and multi-slice spin-echo spiral MRE datasets. Compared to the conventional linear subspace-based approaches, the nonlinear network representation method was able to produce superior image reconstruction with suppressed noise and artifacts from a single in-plane spiral arm per MRE repetition (e.g., total R=10), yielding comparable stiffness estimation to the fully sampled data. This work demonstrated the feasibility of using deep network representations to model and reconstruct MRE images from highly-undersampled data, a nonlinear extension of the subspace-based approaches.
Details
- Title: Subtitle
- Accelerated MR Elastography Using Learned Neural Network Representation
- Creators
- Xi Peng
- Resource Type
- Preprint
- Publication Details
- ArXiv.org
- DOI
- 10.48550/arxiv.2601.11878
- ISSN
- 2331-8422
- Publisher
- Cornell University; Ithaca, New York
- Number of pages
- 21 pages
- Language
- English
- Date posted
- 01/17/2026
- Academic Unit
- Roy J. Carver Department of Biomedical Engineering; Radiology; Mechanical Engineering
- Record Identifier
- 9985130055902771
Metrics
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