Deep Manifold Learning for Dynamic MR Imaging

TL;DR

This paper introduces Manifold-Net, a deep learning approach that models cardiac MRI data reconstruction on a nonlinear low-rank tensor manifold, improving image quality from highly undersampled measurements.

Contribution

It pioneers unrolling manifold optimization into neural networks for dynamic MRI reconstruction, leveraging low-rank tensor manifolds to enhance performance.

Findings

Outperforms CS-based k-t SLR in high acceleration scenarios

Surpasses state-of-the-art deep learning methods DC-CNN and CRNN

Demonstrates improved image reconstruction quality

Abstract

Purpose: To develop a deep learning method on a nonlinear manifold to explore the temporal redundancy of dynamic signals to reconstruct cardiac MRI data from highly undersampled measurements. Methods: Cardiac MR image reconstruction is modeled as general compressed sensing (CS) based optimization on a low-rank tensor manifold. The nonlinear manifold is designed to characterize the temporal correlation of dynamic signals. Iterative procedures can be obtained by solving the optimization model on the manifold, including gradient calculation, projection of the gradient to tangent space, and retraction of the tangent space to the manifold. The iterative procedures on the manifold are unrolled to a neural network, dubbed as Manifold-Net. The Manifold-Net is trained using in vivo data with a retrospective electrocardiogram (ECG)-gated segmented bSSFP sequence. Results: Experimental results at high accelerations demonstrate that the proposed method can obtain improved reconstruction compared with a compressed sensing (CS) method k-t SLR and two state-of-the-art deep learning-based methods, DC-CNN and CRNN. Conclusion: This work represents the first study unrolling the optimization on manifolds into neural networks. Specifically, the designed low-rank manifold provides a new technical route for applying low-rank priors in dynamic MR imaging.