Despite promising advances in deep learning-based MRI reconstruction methods, restoring high-frequency image details and textures remains a challenging problem for accelerated MRI. To tackle this challenge, we propose a novel consistency-aware multi-prior network (CAMP-Net) for MRI reconstruction. CAMP-Net leverages the complementary nature of multiple prior knowledge and explores data redundancy between adjacent slices in the hybrid domain to improve image quality. It incorporates three interleaved modules respectively for image enhancement, k-space restoration, and calibration consistency to jointly learn consistency-aware multiple priors in an end-to-end fashion. The image enhancement module learns a coil-combined image prior to suppress noise-like artifacts, while the k-space restoration module explores multi-coil k-space correlations to recover high-frequency details. The calibration consistency module embeds the known physical properties of MRI acquisition to ensure consistency of k-space correlations extracted from measurements and the artifact-free image intermediate. The resulting low- and high-frequency reconstructions are hierarchically aggregated in a frequency fusion module and iteratively refined to progressively reconstruct the final image. We evaluated the generalizability and robustness of our method on three large public datasets with various accelerations and sampling patterns. Comprehensive experiments demonstrate that CAMP-Net outperforms state-of-the-art methods in terms of reconstruction quality and quantitative $T_2$ mapping.

翻译：尽管基于深度学习的MRI重构方法取得了有前景的进展，但在加速MRI中恢复高频图像细节和纹理仍是一个具有挑战性的问题。为应对这一挑战，我们提出了一种新颖的一致性感知多先验网络（CAMP-Net）用于MRI重构。CAMP-Net利用多种先验知识的互补性质，并探索混合域中相邻切片间的数据冗余以提升图像质量。它包含三个交织模块，分别用于图像增强、k空间恢复和校准一致性，以端到端的方式联合学习一致性感知的多重先验。图像增强模块学习线圈组合图像先验以抑制噪声样伪影，而k空间恢复模块则探索多线圈k空间相关性以恢复高频细节。校准一致性模块嵌入MRI采集的已知物理特性，确保从测量数据中提取的k空间相关性与无伪影图像中间体之间的一致性。由此产生的低频和高频重构在频率融合模块中分层聚合，并迭代精炼以逐步重构最终图像。我们在三个大型公共数据集上评估了该方法在不同加速倍数和采样模式下的泛化性和鲁棒性。综合实验表明，CAMP-Net在重构质量和定量$T_2$映射方面均优于最先进的方法。