2020/08/29 by Elizabeth K. Cole, Cole, Elizabeth K., John M. Pauly +6
Computer Science · Engineering · Mathematics · Medicine · #Advanced MRI Techniques and Applications #Advanced Neuroimaging Techniques and Applications #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Medical Imaging Techniques and Applications #cs.LG #eess.IV #electronic engineering #information engineering #stat.ML
paper · pdf · doi:10.48550/arxiv.2008.13065
arxiv created 2020/08/29 · openalex publication_date 2020/08/29 · arxiv updated 2020/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Deep learning-based image reconstruction methods have achieved promising results across multiple MRI applications. However, most approaches require large-scale fully-sampled ground truth data for supervised training. Acquiring fully-sampled data is often either difficult or impossible, particularly for dynamic contrast enhancement (DCE), 3D cardiac cine, and 4D flow. We present a deep learning framework for MRI reconstruction without any fully-sampled data using generative adversarial networks. We test the proposed method in two scenarios: retrospectively undersampled fast spin echo knee exams and prospectively undersampled abdominal DCE. The method recovers more anatomical structure compared to conventional methods.