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Abstract #4406

Automatic Quantification of Enlarged Perivascular Space aided with Super-resolution 2D T2 images

Jiachen Zhuo1, Muhan Shao2, Steven Roys1, Xiao Liang1, Rosy Linda Njonkou Tchoquessi1, Prashant Raghavan1, Jerry Prince2, and Rao Gullapalli1
1Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, 2Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, United States


The perivascular space (PVS) is key to brain waste clearance and brain metabolic homeostasis. Enlarged PVS (ePVS) can be automatically quantified reliably by combining the 3D T1w and 3D T2w images to produce enhanced PVS contrast followed with frangi filtering and thresholding. However often times only 2D T2w images are available, especially in clinical exams. In this study, we investigate the feasibility of using an innovative deep learning based super-resolution technique (SMORE) to produce 3D T2w images (SR T2) for ePVS quantification. We show that the SR T2 volume provided comparable ePVS estimation as a 3D T2 volume.

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