MOTIF-CORD: Motion Integrated Forward Model with Co-Estimated Coil Sensitivity and Regularization by Denoiser for Free Breathing Liver DCE-MRI

Motivation: Dynamic contrast-enhanced (DCE) MRI faces challenges from respiratory motion and sub-optimal DCE contrast timing. Free-breathing DCE with high temporal resolution is desirable. Goal(s): We aim to reconstruct respiratory motion-free and high temporal resolution DCE-MRI. Approach: We proposed a Motion Integrated Forward model using motion vector fields and jointly estimated coil sensitivity to reconstruct severely under-sampled DCE data. Furthermore, we utilized a model-based deep learning framework to amalgamate the knowledge of the measurement model and the denoising prior. Results: The proposed method provided deformable motion vector fields, coil-sensitivity maps, and sharp motion-free DCE images without artifacts using highly under-sampled data. Impact: This method provides good quality free-breathing liver DCE MR images with high temporal resolution. It will eliminate the need for breath-holding. Moreover, continuous acquisition and high temporal resolution reconstruction mitigate the problem of sub-optimal DCE contrast in clinical diagnosis.