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Abstract #4323

Improving Tissue Segmentation of Brain MRI through Sparsity-guided Super-resolution Imaging

Jean-Christophe Brisset1, Louise E Pape1, Ricardo Otazo1, and Yulin Ge1

1Radiology, New York University School of Medicine, New York, NY, United States

Since human gray matter cortex is a relatively thin structure and has a complex folding pattern blended with white matter and cerebrospinal fluid (CSF), partial volume effect is always considered a challenging issue for precise tissue segmentation. Super-resolution (SR) is a common method that is often used in the picture world to recover a high-resolution image from low-resolution images. This study was performed to test whether a newly developed sparsity-guided SR algorithm can be adapted on standard clinical MRI images to improve brain tissue segmentation by decreasing partial volume effect.

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