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Abstract #4864

AI-based Image Reconstruction of kt-accelerated Intracranial Dual-Venc 4D Flow MRI

Haben Berhane1, Jackson E Moore1, Ann Ragin1, Eric Russell2, Fan Caprio3, Susanne Schnell4, Sameer A Ansari2, and Michael Markl1
1Biomedical Engineering, Northwestern University, Chicago, IL, United States, 2Radiology, Northwestern University, Chicago, IL, United States, 3Neurology, Northwestern University, Chicago, IL, United States, 4Radiology, University of Greifswald, Greifswald, Germany


4D flow MRI provides a comprehensive assessment of hemodynamics through the 3D visualization and quantification of blood flow. However, its current clinical usage is hindered by long scan times. Recent developments have shown deep learning to be highly effective in accelerating image reconstruction, but no study has shown the its effectiveness in reconstructing highly undersampled cerebrovascular 4D flow MRI data. As such, we developed a CNN for the reconstruction of kt-accelerated intracranial dual-venc 4D flow MRI (R=5). We found that the CNN showed excellent SSIM values (0.94[0.93-0.95]) and moderate-to-good net flow and peak velocity agreement with the conventionally reconstructed data.

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