Abstract #4773

Accurate Peak and Mean Velocity with Deep Learning-Reconstructed highly Undersampled 4D Flow MRI using FlowVN

Sohaib Ayaz Qazi^1,2, Hussnain Khalid¹, Federica Viola^1,2, Tino Ebbers ^1,2, Farkas Vanky³, and Petter Dyverfeldt^1,2

¹Department of health, medicine and caring sciences, Linköping University Sweden, Linköping, Sweden, ²Center for medical image science and visualization (CMIV), Linköping University Sweden, Linköping, Sweden, ³Department of thoracic and vascular surgery, and Department of health, medicine and caring sciences, Linköping University Sweden, Linköping, Sweden

Synopsis

Keywords: Flow, Cardiovascular, Image Reconstruction

Motivation: 4D Flow MRI is limited by long scan times. Accelerated imaging by compressed sensing leads to long reconstruction times.

Goal(s): The goal of this work was to evaluate a deep learning-based method (FlowVN) for reconstruction of pseudo-randomly heavily undersampled Cartesian 4D Flow.

Approach: In this study, we explored FlowVN for the reconstruction of different acceleration factors and did velocity analysis of 4D Flow MRI.

Results: The results show that FlowVN rapidly reconstructs undersampled 4D Flow images with good accuracy for average and peak velocity in the ascending aorta even at high acceleration factors.

Impact: High-quality rapid reconstruction of highly undersampled 4D Flow MRI with deep learning has the potential to substantially facilitate the use of 4D Flow MRI in the clinical routine.

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