Adaptive k-space learning and high-dimensional subsets embedding for parallel MRI reconstruction

Magnetic resonance imaging (MRI) inherently requires considerable time for data acquisition, but obtaining multi-contrast MRI data further prolongs this process, thereby increasing susceptibility to motion artifacts. It is worth noting that the multi-contrast MR images have both structural similarities and unique contrast information. Therefore, to take advantage of their similarities while preserving their distinctive characteristics, we proposed a new method called high-dimensional subsets embedding (HDSE). This novel approach is based on the frame of low-rank modeling of local <i>k</i>-space neighborhoods with parallel imaging (P-LORAKS). Specifically, our approach utilizes the structural similarity of multi-contrast MR images to process different <i>k</i>-space data through two independent channels. In one channel, we individually separate the complementary <i>T</i> ₁-<i>T</i> ₂ <i>k</i>-space data and directly construct a new subset of local <i>k</i>-space, allowing the model to better capture structural correlations between multiple contrasts. In another channel, we provide global under-sampled <i>T</i> ₂-weighted <i>k</i>-space data further constrain image acquisition in high-dimensional space to maintain image consistency and reduce noise amplification. These two different channels information is fused together to form high-dimensional feature objects. Besides, we embed the constructed objects into P-LORAKS in various ways to enhance the reconstruction performance. Experimental results demonstrated that the aided reconstruction of local subsets fusion and the high-dimensional reconstruction of adaptive global constraints can improve the accuracy of image reconstruction and enhance the robustness of the model.