Accelerated MR Elastography Using Learned Neural Network Representation

• URL: http://arxiv.org/abs/2601.11878v1
• Date: Sat, 17 Jan 2026 02:14:24 GMT
• Title: Accelerated MR Elastography Using Learned Neural Network Representation
• Authors: Xi Peng,
• Abstract summary: deep neural network representation as a nonlinear extension of the linear subspace model.<n>Network weights were learned using a multi-level k-space consistent loss in a self-supervised manner.<n>Phase-contrast specific magnitude and phase priors were incorporated, including the similarity of anatomical structures.
• Score: 16.308864289098086
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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