  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.
