Improved Visualization of Focal Cortical Dysplasia With Surface-Based Multiparametric Quantitative MRI

In the clinical routine, detection of focal cortical dysplasia (FCD) by visual inspection is challenging. Still, information about the presence and location of FCD is highly relevant for prognostication and treatment decisions. Therefore, this study aimed to develop, describe and test a method for the calculation of synthetic anatomies using multiparametric quantitative MRI (qMRI) data and surface-based analysis, which allows for an improved visualization of FCD.

Quantitative T1-, T2- and PD-maps and conventional clinical datasets of patients with FCD and epilepsy were acquired. Tissue segmentation and delineation of the border between white matter and cortex was performed. In order to detect blurring at this border, a surface-based calculation of the standard deviation of each quantitative parameter (T1, T2, and PD) was performed across the cortex and the neighboring white matter for each cortical vertex. The resulting standard deviations combined with measures of the cortical thickness were used to enhance the signal of conventional FLAIR-datasets. The resulting synthetically enhanced FLAIR-anatomies were compared with conventional MRI-data utilizing regions of interest based analysis techniques.

The synthetically enhanced FLAIR-anatomies showed higher signal levels than conventional FLAIR-data at the FCD sites (p = 0.005). In addition, the enhanced FLAIR-anatomies exhibited higher signal levels at the FCD sites than in the corresponding contralateral regions (p = 0.005). However, false positive findings occurred, so careful comparison with conventional datasets is mandatory.

Synthetically enhanced FLAIR-anatomies resulting from surface-based multiparametric qMRI-analyses have the potential to improve the visualization of FCD and, accordingly, the treatment of the respective patients.