Keywords: Tractography, Tractography
Motivation: Tractography parcellation is crucial for quantitative analysis of white matter connectivity and microstructure related to brain health and disease. However, real-life dMRI scans often have incomplete Field of View (FOV), and robust tractography parcellation methods do not yet exist.
Goal(s): We aim to propose a robust framework for parcellation of tractograms with inferior FOV cutoff.
Approach: We propose a deep learning framework for effective tractogram parcellation with incomplete FOV by leveraging data augmentation with synthetically cut tractograms.
Results: Evaluations on datasets with simulated and real-world FOV limitations indicate that our method enables effective parcellation of tractography affected by incomplete FOV.
Impact: dMRI scans with incomplete FOV are common in real-life clinical and large-scale research datasets and pose great challenges to identification of anatomical tracts. We propose the first deep learning framework to achieve robust parcellation of tractography affected by incomplete FOV.
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.
Keywords