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Abstract #3471

Perivascular Space Quantification with Deep Learning synthesized T2 from T1w and FLAIR images

Jiehua Li1, Pan Su1,2, Rao Gullapalli1, and Jiachen Zhuo1
1Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, United States, 2Siemens Medical Solutions USA, Inc., Malvern, PA, United States


The perivascular space (PVS) plays a major role in brain waste clearance and brain metabolic homeostasis. Enlarged PVS (ePVS) is associated with many neurological disorders. ePVS is best depicted as hyper-intensities T2w images and can be reliable quantified with both the 3D T1w and T2w images. However many studies opt to acquire 3D FLAIR images instead of T2w due to its high specificity to white matter abnormalities (e.g. the ADNI study). Here we show that deep learning techniques can be used to synthesize T2w images from T1w & FLAIR images and improve the ePVS quantification in absence of T2w images.

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