CAMP-Net: Consistency-Aware Multi-Prior Network for Accelerated MRI Reconstruction
This work addresses the problem of accelerated MRI reconstruction for medical imaging, offering improved detail restoration, but it is incremental as it builds on existing unrolling-based networks with multi-prior integration.
The paper tackles the challenge of reconstructing high-quality MRI images from highly undersampled k-space data to reduce scan time, proposing CAMP-Net, which outperforms state-of-the-art methods in reconstruction quality and T2 mapping estimation, especially at high acceleration factors.
Undersampling k-space data in MRI reduces scan time but pose challenges in image reconstruction. Considerable progress has been made in reconstructing accelerated MRI. However, restoration of high-frequency image details in highly undersampled data remains challenging. To address this issue, we propose CAMP-Net, an unrolling-based Consistency-Aware Multi-Prior Network for accelerated MRI reconstruction. CAMP-Net leverages complementary multi-prior knowledge and multi-slice information from various domains to enhance reconstruction quality. Specifically, CAMP-Net comprises three interleaved modules for image enhancement, k-space restoration, and calibration consistency, respectively. These modules jointly learn priors from data in image domain, k-domain, and calibration region, respectively, in data-driven manner during each unrolled iteration. Notably, the encoded calibration prior knowledge extracted from auto-calibrating signals implicitly guides the learning of consistency-aware k-space correlation for reliable interpolation of missing k-space data. To maximize the benefits of image domain and k-domain prior knowledge, the reconstructions are aggregated in a frequency fusion module, exploiting their complementary properties to optimize the trade-off between artifact removal and fine detail preservation. Additionally, we incorporate a surface data fidelity layer during the learning of k-domain and calibration domain priors to prevent degradation of the reconstruction caused by padding-induced data imperfections. We evaluate the generalizability and robustness of our method on three large public datasets with varying acceleration factors and sampling patterns. The experimental results demonstrate that our method outperforms state-of-the-art approaches in terms of both reconstruction quality and $T_2$ mapping estimation, particularly in scenarios with high acceleration factors.