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Abstract #3921

Examining ‘neural soma’ compartments in paediatric epilepsy to support focal cortical dysplasia lesion detection

Yi Jie Li1, Enrico De Vita1,2,3, Felice D'Arco2, Tina Banks2, Kiran Seunarine1,2, Mathilde Ripart1, Konrad Wagstyl1,4, Sophie Adler1, Martin Tisdall5, Leevi Kerkelä6, Torsten Baldeweg1, and Chris A Clark1
1Great Ormond Street Institute of Child Health, University College London, London, United Kingdom, 2Radiology, Great Ormond Street Hospital, London, United Kingdom, 3MRI Physics group, Radiology, Great Ormond Street Hospital, London, United Kingdom, 4Department of Biomedical Computing and Early Life Imaging, King's College London, London, United Kingdom, 5Neurosurgery, Great Ormond Street Hospital, London, United Kingdom, 6Hawkes Institute and Department of Computer Science, University College London, London, United Kingdom

Synopsis

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.

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Keywords