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