Keywords: Neuro, Epilepsy, Drug-resistant epilepsy, seizure onset zone, clinical MRI
Motivation: To solve an incomplete sequence problem in localizing seizure onset zone (SOZ) using clinical MRI of children with drug-resistant epilepsy,
Goal(s): We develop a sequence-agnostic deep learning approach that can localize SOZ even using subsets of clinical MRI sequence data.
Approach: This consisted of 1) a sequence-agnostic model with cross-sequence distillation to train sequence specific and shared feature extractors for each sequence data and 2) a shared classification head to localize SOZ sites using a specific subset of multiple sequence data.
Results: Our approach provided a high accuracy of 96%/89%/86% in classifying SOZ sites using five/four/three MRI sequence data of a validation cohort.
Impact: Sequence-agnostic learning approach could accurately classify seizure onset zone (SOZ) using clinical MRI sequence data of pediatric drug-resistant epilepsy. It provided a new way to better generalize the SOZ classification in multi-center studies that suffer from missing sequence data problem
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