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

ABSTRACT: 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. This work shows that transfer learning (TL) combined with an ensembling strategy can help alleviate this key challenge. We evaluate four approaches: pre-trained models from the source domain, models directly trained from scratch on target domain data, models fine-tuned from a pre-trained model and an

SUBMITTER: Gulamhussene G 

PROVIDER: S-EPMC10336014 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature