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Abstract #1190

FlowMRI-Net: A generalizable self-supervised physics-driven 4D Flow MRI reconstruction network for aortic and cerebrovascular applications

Luuk Jacobs1, Marco Piccirelli2, Valery Vishnevskiy1, and Sebastian Kozerke1
1Institute of Biomedical Engineering, ETH and University Zurich, Zurich, Switzerland, 2Department of Neuroradiology, University Hospital Zurich, Zurich, Switzerland

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, 4D Flow MRI

Motivation: Improved reconstruction quality and speed is necessary to accelerate 4D flow MRI acquisition and promote clinical adaptation.

Goal(s): To develop a deep learning-based framework (FlowMRI-Net) for fast reconstruction of accelerated 4D flow MRI that can be used for applications where reference data are not available.

Approach: Training is performed in a self-supervised manner using healthy aortic and cerebrovascular acquisitions and results are compared to state-of-the-art compressed sensing and deep learning-based (FlowVN) methods.

Results: FlowMRI-Net outperforms CS-LLR and FlowVN for aortic 4D flow MRI reconstruction and CS-LLR for cerebrovascular 4D flow MRI reconstruction.

Impact: FlowMRI-Net facilitates higher undersampling factors than the current state-of-the-art for aortic and cerebrovascular 4D flow MRI within clinically feasible reconstruction times, improving clinical adaptation particularly for cerebrovascular applications which are otherwise too time-consuming.

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Keywords