Abstract
Objective
The detection of focal cortical dysplasia (FCD) in magnetic resonance imaging is challenging. Voxel-based morphometric analysis and automated FCD detection using an artificial neural network (ANN) integrated into the Morphometric Analysis Program (MAP18) have been shown to facilitate FCD detection. This study aimed to evaluate whether the detection of FCD can be further improved by feeding this approach with magnetization prepared two rapid acquisition gradient echoes (MP2RAGE) instead of magnetization-prepared rapid acquisition gradient echo (MPRAGE) datasets.
Methods
MPRAGE and MP2RAGE datasets were acquired in a consecutive sample of 32 patients with FCD and postprocessed using MAP18. Visual analysis and, if available, histopathology served as the gold standard for assessing the sensitivity and specificity of FCD detection. Out-of-sample specificity was evaluated in a cohort of 32 healthy controls.
Results
The sensitivity and specificity of FCD detection were 82.4% and 62.5% for the MPRAGE and 97.1% and 34.4% for the MP2RAGE sequences, respectively. Median volumes of true-positive voxel clusters were .16 ml for the MPRAGE and .52 ml for the MP2RAGE sequences compared to .08- and .04-ml volumes of false-positive clusters. With regard to cluster volumes, FCD detection was substantially improved for the MP2RAGE data when the estimated optimal threshold of .23 ml was applied (sensitivity = 72.9%, specificity = 83.0%). In contrast, the estimated optimal threshold of .37 ml for the MPRAGE data did not improve FCD lesion detection (sensitivity = 42.9%, specificity = 79.5%).
Significance
In this study, the sensitivity of FCD detection by morphometric analysis and an ANN integrated into MAP18 was higher for MP2RAGE than for MPRAGE sequences. Additional usage of cluster volume information helped to discriminate between true- and false-positive MP2RAGE results.
NOV