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Abstract #3698

Fast Reconstruction of Time-Resolved 4D MRA with Unrolled Neural Networks

Zhitao Li1, Tianrui Zhao1, Mahmut Yurt2, Shreyas Vasanawala3, and Lirong Yan1
1Department of Radiology, Northwestern University, Chicago, IL, United States, 2Department of Electrical Engineering, Stanford University, Stanford, CA, United States, 3Department of Radiology, Stanford University, Stanford, CA, United States

Synopsis

Keywords: Arterial Spin Labelling, AI/ML Image Reconstruction, 4D MRA; dMRA; dynamic MRA; image reconstruction; machine learning; AI; ML;

Motivation: Recently an ASL-based accelerated 4D MRA was demonstrated with great potential in delineating dynamic blood flow patterns at high spatiotemporal resolution. However, the time-consuming reconstruction presents a bottleneck for wider clinical translation.

Goal(s): To introduce DL-AngioNet, a ML-based framework that accelerates the reconstruction while providing improved SNR.

Approach: The network was trained using historical data via a data-driven method with a physical model. The unrolled structure of the network provided a data consistency term to ensure validity of the results.

Results: DL-AngioNet accelerated the reconstruction by ~30-fold while preserving good flow dynamic information. Results demonstrated superior SNR comparing to the conventional PICS method.

Impact: DL-AngioNet significantly accelerates 4D MRA reconstruction by ~30-fold, which not only preserves good 4D MRA flow dynamics, but also provides improved SNR in the results. DL-AngioNet could facilitate 4D MRA into a wider clinical use.

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Keywords