Image-feature-understanding data consistency for under-sampled MRI reconstruction

Motivation: The optimal selection of data consistency (DC) weight is task-dependent and a challenge in current deep learning unrolled reconstruction network which may result in compromised image quality, and thus deserves further investigations. Goal(s): To propose a method to obtain adaptive data consistency weight which is superior to existing methods. Approach: An image-feature-understanding data consistency (IFUDC) modulator is integrated into network to obtain adaptive DC weight based on input images. Results: Image quality metrics (SSIM, PSNR) of proposed method are higher than those of existing method. Impact: IFUDC is effective to modulate DC weight adaptively and helps to mitigate the difficulty in optimal DC weight selection.