Transfer-learning is a key ingredient to fast deep learning-based 4D liver MRI reconstruction.

Time-resolved volumetric magnetic resonance imaging (4D MRI) could be used to address organ motion in image-guided interventions like tumor ablation. Current 4D reconstruction techniques are unsuitable for most interventional settings because they are limited to specific breathing phases, lack temporal/spatial resolution, and have long prior acquisitions or reconstruction times. Deep learning-based (DL) 4D MRI approaches promise to overcome these shortcomings but are sensitive to domain shift.

Predicting 4D liver MRI for MR-guided interventions.

Organ motion poses an unresolved challenge in image-guided interventions like radiation therapy, biopsies or tumor ablation. In the pursuit of solving this problem, the research field of time-resolved volumetric magnetic resonance imaging (4D MRI) has evolved. However, current techniques are unsuitable for most interventional settings because they lack sufficient temporal and/or spatial resolution or have long acquisition times.

4D MRI: Robust sorting of free breathing MRI slices for use in interventional settings.

Purpose: We aim to develop a robust 4D MRI method for large FOVs enabling the extraction of irregular respiratory motion that is readily usable with all MRI machines and thus applicable to support a wide range of interventional settings.

Method: We propose a 4D MRI reconstruction method to capture an arbitrary number of breathing states. It uses template updates in navigator slices and search regions for fast and robust vessel cross-section tracking.