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Abstract #3983

Boosting SNR and/or Resolution of Arterial Spin Label (ASL) imaging using Multi-contrast Approaches with Multi-lateral Guided Filter and Deep Networks

Enhao Gong1, John Pauly1, and Greg Zaharchuk2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

Arterial Spin Labeling (ASL) MRI is a powerful neuro imaging tool which provides quantitative perfusion maps. However, ASL perfusion maps typically suffer from low SNR and resolution. Averaging from multiple scans (high Nex value) can improve the SNR but at the cost of significantly increased acquisition time. In the work we proposed a technique for improved ASL image quality with boosted SNR and/or resolution by 1) incorporating the information of multi-contrast images 2) using nonlinear, non-local, spatial variant multi-lateral filtering, 3) training a deep network model to adaptively tune the final denoising level and further boost the SNR and improve image quality. Various in-vivo experiments demonstrate the superior performance of the proposed method which will significantly accelerate ASL acquisition and improve image quality.

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