Keywords: Microstructure, Diagnosis/Prediction, Paediatric
Motivation: Focal cortical dysplasia (FCD) lesions are small in size and subtle in feature, making radiological detection a challenge. Moreover, while automated machine learning tools are promising, they often predict false positives.
Goal(s): Here, we examine whether ‘neural soma’ parameter estimates can differentiate between true lesions and false positives in paediatric FCD cases, thus improving specificity.
Approach: We fitted a constrained three-compartment ‘neural soma’ model to paediatric patient data acquired using tensor-valued diffusion encoding on a clinical scanner.
Results: ‘Neural soma’ parameter maps are sensitive to signal changes due to FCD lesions and distributions in parameter estimates can be used to differentiate between regions-of-interest.
Impact: The ‘neural soma’ model could support FCD lesion detection in a clinical setting by improving specificity between true lesions and false positives. This can inform selection of surgical resection targets and ultimately improve post-surgical outcomes and rates of seizure freedom.
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