# MR Fingerprinting for Epilepsy

> **NIH NIH R01** · CASE WESTERN RESERVE UNIVERSITY · 2022 · $600,554

## Abstract

Abstract
Epilepsy affects 65 million people worldwide; approximately 30% of them do not respond to
medications but can be cured by surgery. Focal cortical dysplasia (FCD), a major pathology for
medically intractable epilepsies, is frequently missed by visual analysis of the conventional MRI,
making surgical treatment very difficult. We propose to develop a novel quantitative MRI
acquisition and analysis framework specific for epilepsy patients, which could provide more
sensitive and specific measures of brain structure, thereby improving FCD detection and subtype
prediction. To this end, the quantitative framework will be developed and validated in three steps:
(1) Develop high-resolution Magnetic Resonance Fingerprinting (MRF) scan that allows
simultaneous quantification of multiple tissue property maps efficiently, accurately and precisely.
These quantitative maps have shown to be more sensitive and specific on detecting and
characterizing subtle signal abnormalities. (2) Develop image post-processing methods to
analyze quantitative maps, which will provide quantitative measurements that highlight additional
morphological features, such as gray-white boundary blurring, abnormal cortical thickness and
folding. (3) Develop machine-learning-based feature screening and prediction tools to
characterize group-level features differentiating FCD subtypes, and predict individual-level FCD
location and subtyping. Because detection and subtype prediction of FCD are both associated
with seizure outcomes, epileptologists can use this tool to provide more personalized and
customized counseling. The result of our proposed work promises a paradigm shift by converting
the current standard-care of visual/qualitative MRI review to a quantitative framework, including
data acquisition, post-processing and decision support tool, that would eventually lead to better
treatment planning, reduction in unnecessary pre-surgical evaluation tests (especially invasive
evaluation), and improved post-operative seizure outcomes in patients with devastating and
disabling medically intractable epilepsy. The quantitative nature of our acquisition/analysis
methods also makes it possible to be uniformly adopted by other centers with high consistency.

## Key facts

- **NIH application ID:** 10312013
- **Project number:** 5R01NS109439-04
- **Recipient organization:** CASE WESTERN RESERVE UNIVERSITY
- **Principal Investigator:** Dan Ma
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $600,554
- **Award type:** 5
- **Project period:** 2019-01-15 → 2023-12-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10312013

## Citation

> US National Institutes of Health, RePORTER application 10312013, MR Fingerprinting for Epilepsy (5R01NS109439-04). Retrieved via AI Analytics 2026-06-11 from https://api.ai-analytics.org/grant/nih/10312013. Licensed CC0.

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