Fig. 2.

Illustration of our proposed Low-to-High Frequency Progressive (LHFP) learning network, which consists of a Low-Frequency (LF) learning stage and a High-Frequency (HF) enhancement stage. In the LF learning stage, equal loss weights are applied to all frequency components. In the HF enhancement stage, the focus shifts to enhancing HF components learning, guided by the estimated low-high frequency boundaries ${\mathbf{M}}_{T1}^{(LF)}$ and ${\mathbf{M}}_{T2}^{(LF)}$. The refined k-space data $k_{T1}^{(3)}$ and $k_{T2}^{(3)}$ are reconstructed into MR images $I_{T1}^{(k)}$ and $I_{T2}^{(k)}$.