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.

How to access this content:

For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.

After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.

After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.

Click here for more information on becoming a member.