Fig. 1.

Illustration of our proposed multi-contrast MRI reconstruction framework. The low-quality k-space data $k_{T1}^{(0)}$ and $k_{T2}^{(0)}$ are first refined using the (A) K-space Low-to-High Frequency Progressive (LHFP) learning network, resulting in $k_{T1}^{\prime }$ and $k_{T2}^{\prime }$. The refined k-space data is then transformed into images $I_{T1}^{(k)}$ and $I_{T2}^{(k)}$, while the original k-space data is converted into images $I_{T1}^{(in)}$ and $I_{T2}^{(in)}$. These multi-contrast images $I_{T1}^{(k)}$, $I_{T2}^{(k)}$, $I_{T1}^{(in)}$, and $I_{T2}^{(in)}$ are refined by the (B) Image space Hybrid Attention Fusion Network (HAFNet) to produce high-quality images $I_{T1}^{\prime }$ and $I_{T2}^{\prime }$.