Deep Unfolding Network with Spatial Alignment for multi-modal MRI reconstruction

2023-12-28

arXiv.org

AI Summary

Clinical multi-modal MRI acquisitions often suffer from inter-modal spatial misalignment, degrading reconstruction accuracy in accelerated imaging. Method: We propose the first joint alignment-reconstruction optimization framework, featuring an interpretable deep-unfolding network that adaptively embeds cross-modal spatial alignment regularization into the reconstruction process. This enables end-to-end alignment compensation and prior-guided reconstruction, eliminating the conventional two-stage paradigm. A single reconstruction loss simultaneously drives precise registration and high-fidelity image recovery. Results: Evaluated on three real-world multi-modal MRI datasets, our method achieves significant improvements in PSNR and SSIM over state-of-the-art approaches. Quantitative and qualitative results demonstrate that joint modeling enhances both reconstruction accuracy and robustness to misalignment, establishing a new benchmark for aligned multi-modal MRI reconstruction.

Abstract

Multi-modal Magnetic Resonance Imaging (MRI) offers complementary diagnostic information, but some modalities are limited by the long scanning time. To accelerate the whole acquisition process, MRI reconstruction of one modality from highly undersampled k-space data with another fully-sampled reference modality is an efficient solution. However, the misalignment between modalities, which is common in clinic practice, can negatively affect reconstruction quality. Existing deep learning-based methods that account for inter-modality misalignment perform better, but still share two main common limitations: (1) The spatial alignment task is not adaptively integrated with the reconstruction process, resulting in insufficient complementarity between the two tasks; (2) the entire framework has weak interpretability. In this paper, we construct a novel Deep Unfolding Network with Spatial Alignment, termed DUN-SA, to appropriately embed the spatial alignment task into the reconstruction process. Concretely, we derive a novel joint alignment-reconstruction model with a specially designed cross-modal spatial alignment term. By relaxing the model into cross-modal spatial alignment and multi-modal reconstruction tasks, we propose an effective algorithm to solve this model alternatively. Then, we unfold the iterative steps of the proposed algorithm and design corresponding network modules to build DUN-SA with interpretability. Through end-to-end training, we effectively compensate for spatial misalignment using only reconstruction loss, and utilize the progressively aligned reference modality to provide inter-modality prior to improve the reconstruction of the target modality. Comprehensive experiments on three real datasets demonstrate that our method exhibits superior reconstruction performance compared to state-of-the-art methods.

Problem

• Multimodal MRI Reconstruction
• Data Misalignment
• Image Quality Degradation

Innovation

• DUN-SA
• Alternating Optimization
• Multi-modal MRI Alignment