Zero-shot self-supervised deep learning reconstruction for abdominal DCE MR multitasking

Motivation: Deep learning (DL) MR multitasking reconstruction can reduce the reconstruction time, but previous methods are supervised learning, which may learn artifacts from the reference images. Goal(s): Our goal was to develop a DL reconstruction method that can improve image quality beyond supervised DL and conventional iterative reconstruction. Approach: We developed a zero-shot self-supervised deep learning method for DCE MR multitasking reconstruction. Results: With shorter reconstruction time than conventional iterative reconstruction, the proposed method obtained better image quality than both supervised DL and conventional iterative reconstruction methods. Impact: With the proposed method, DCE MR multitasking can have better image quality with shorter reconstruction time than previous iterative reconstruction, which is essential for the potential clinical application of the motion-resolved and high spatial-temporal-resolution abdominal DCE MR multitasking.