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 context-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 context-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重建方法取得了显著进展，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上下文感知多先验网络（CAMP-Net）用于MRI重建。CAMP-Net利用多个先验知识的互补特性，并探索混合域中相邻切片间的数据冗余以提升图像质量。它包含三个交替模块，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上下文感知的多重先验。图像增强模块学习线圈组合图像先验以抑制类噪声伪影，而k空间恢复模块探索多线圈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，确保从测量中提取的k空间相关性与无伪影图像中间结果之间的一致性。所得的低频和高频重建结果在频率融合模块中分层聚合，并通过迭代优化逐步重建最终图像。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通用性和鲁棒性。综合实验表明，CAMP-Net在重建质量和定量$T_2$映射方面优于当前最先进方法。