Accelerated
MR
Elastography Using Learned Neural Network Representation
Xi Peng ABSTRACT
Purpose
To develop a deep‐learning method for achieving fast high‐resolution MR elastography from highly undersampled data without the need for high‐quality training datasets.
Methods
We first reframed 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. A multi‐level k‐space consistency loss was proposed to learn the network weights and mitigate overfitting. To further enhance reconstruction quality, phase‐contrast specific magnitude and phase priors were incorporated, namely the similarity of anatomical structures and smoothness of wave‐induced harmonic displacement. The network was trained in a self‐supervised manner with empirically fixed iterations. Experiments were conducted using one multi‐slice spin‐echo and two 3D gradient‐echo spiral MRE datasets.
Results
Compared to conventional linear subspace‐based approaches, the nonlinear network representation method produced superior image reconstruction with suppressed artifacts and noise from a single in‐plane spiral arm per MRE repetition, yielding stiffness estimation (e.g., from a 3D GRE sequence at 2 mm resolution within 1 min, R = 10) comparable to that obtained from fully sampled data.
Conclusion
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 approach. Future work will include validation on a larger and more diverse cohort of subjects to further assess its robustness and generalizability.