Ding Zheng, Morris Spencer, Hu Siyuan, Su Ting-Yu, Choi Joon Yul, Blümcke Ingmar, Wang Xiaofeng, Sakaie Ken, Murakami Hiroatsu, Alexopoulos Andreas V, Jones Stephen E, Najm Imad M, Ma Dan, Wang Zhong Irene
Epilepsy Center, Neurological Institute, Cleveland Clinic, OH.
Department of Biomedical Engineering, Case Western Reserve University, OH.
Neurology. 2025 Jun 10;104(11):e213691. doi: 10.1212/WNL.0000000000213691. Epub 2025 May 16.
Focal cortical dysplasia (FCD) is a common pathology for pharmacoresistant focal epilepsy, yet detection of FCD on clinical MRI is challenging. Magnetic resonance fingerprinting (MRF) is a novel quantitative imaging technique providing fast and reliable tissue property measurements. The aim of this study was to develop an MRF-based deep-learning (DL) framework for whole-brain FCD detection.
We included patients with pharmacoresistant focal epilepsy and pathologically/radiologically diagnosed FCD, as well as age-matched and sex-matched healthy controls (HCs). All participants underwent 3D whole-brain MRF and clinical MRI scans. T1, T2, gray matter (GM), and white matter (WM) tissue fraction maps were reconstructed from a dictionary-matching algorithm based on the MRF acquisition. A 3D ROI was manually created for each lesion. All MRF maps and lesion labels were registered to the Montreal Neurological Institute space. Mean and SD T1 and T2 maps were calculated voxel-wise across using HC data. T1 and T2 -score maps for each patient were generated by subtracting the mean HC map and dividing by the SD HC map. MRF-based morphometric maps were produced in the same manner as in the morphometric analysis program (MAP), based on MRF GM and WM maps. A no-new U-Net model was trained using various input combinations, with performance evaluated through leave-one-patient-out cross-validation. We compared model performance using various input combinations from clinical MRI and MRF to assess the impact of different input types on model effectiveness.
We included 40 patients with FCD (mean age 28.1 years, 47.5% female; 11 with FCD IIa, 14 with IIb, 12 with mMCD, 3 with MOGHE) and 67 HCs. The DL model with optimal performance used all MRF-based inputs, including MRF-synthesized T1w, T1z, and T2z maps; tissue fraction maps; and morphometric maps. The patient-level sensitivity was 80% with an average of 1.7 false positives (FPs) per patient. Sensitivity was consistent across subtypes, lobar locations, and lesional/nonlesional clinical MRI. Models using clinical images showed lower sensitivity and higher FPs. The MRF-DL model also outperformed the established MAP18 pipeline in sensitivity, FPs, and lesion label overlap.
The MRF-DL framework demonstrated efficacy for whole-brain FCD detection. Multiparametric MRF features from a single scan offer promising inputs for developing a deep-learning tool capable of detecting subtle epileptic lesions.
局灶性皮质发育不良（FCD）是药物难治性局灶性癫痫的常见病理表现，但在临床磁共振成像（MRI）上检测FCD具有挑战性。磁共振指纹识别（MRF）是一种新型定量成像技术，可提供快速且可靠的组织特性测量。本研究的目的是开发一种基于MRF的深度学习（DL）框架用于全脑FCD检测。
我们纳入了药物难治性局灶性癫痫且经病理/放射学诊断为FCD的患者，以及年龄和性别匹配的健康对照（HC）。所有参与者均接受了3D全脑MRF和临床MRI扫描。基于MRF采集的字典匹配算法重建T1、T2、灰质（GM）和白质（WM）组织分数图。为每个病变手动创建一个3D感兴趣区（ROI）。所有MRF图和病变标签均配准到蒙特利尔神经病学研究所空间。使用HC数据逐体素计算平均和标准差T1及T2图。通过减去平均HC图并除以标准差HC图，为每位患者生成T1和T2评分图。基于MRF GM和WM图，以与形态计量分析程序（MAP）相同的方式生成基于MRF的形态计量图。使用各种输入组合训练一个无新U-Net模型，通过留一患者交叉验证评估性能。我们比较了使用临床MRI和MRF的各种输入组合的模型性能，以评估不同输入类型对模型有效性的影响。
我们纳入了40例FCD患者（平均年龄28.1岁，47.5%为女性；11例为FCD IIa型，14例为IIb型，12例为轻度皮质发育不良（mMCD），3例为多小脑回样局灶性皮质发育不良伴气球样细胞（MOGHE））和67例HC。性能最佳的DL模型使用了所有基于MRF的输入，包括MRF合成的T1w、T1z和T2z图；组织分数图；以及形态计量图。患者水平的敏感性为80%，平均每位患者有1.7例假阳性（FP）。各亚型、脑叶位置以及病变/非病变临床MRI的敏感性一致。使用临床图像的模型敏感性较低且FP较高。MRF-DL模型在敏感性、FP和病变标签重叠方面也优于已建立的MAP18流程。
MRF-DL框架在全脑FCD检测中显示出有效性。单次扫描的多参数MRF特征为开发能够检测细微癫痫病变的深度学习工具提供了有前景的输入。
