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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