正电子发射断层摄影术
磁共振成像
正电子发射
断层摄影术
正电子
脑正电子发射断层扫描术
临床前影像学
核磁共振
核医学
医学
放射科
物理
核物理学
电子
生物
生物技术
体内
作者
Ruifeng Zheng,Ruotong Chen,Cong Chen,Yuyu Yang,Yi Ge,Linqi Ye,Pu Miao,Bo Jin,Hong Li,Jiang Zhu,Shuang Wang,Kejie Huang
标识
DOI:10.1016/j.seizure.2024.02.009
摘要
Abstract
Purpose: Focal cortical dysplasia (FCD) is a common etiology of drug-resistant focal epilepsy. Visual identification of FCD is usually time-consuming and depends on personal experience. Herein, we propose an automated type II FCD detection approach utilizing multi-modal data and 3D convolutional neural network (CNN). Methods: MRI and positron emission tomography (PET) data of 82 patients with FCD were collected, including 55 (67.1%) histopathologically, and 27 (32.9%) radiologically diagnosed patients. Three types of morphometric feature maps and three types of tissue maps were extracted from the T1-weighted images. These maps, T1, and PET images formed the inputs for CNN. Five-fold cross-validations were carried out on the training set containing 62 patients, and the model behaving best was chosen to detect FCD on the test set of 20 patients. Furthermore, ablation experiments were performed to estimate the value of PET data and CNN. Results: On the validation set, FCD was detected in 90.3% of the cases, with an average of 1.7 possible lesions per patient. The sensitivity on the test set was 90.0%, with 1.85 possible lesions per patient. Without the PET data, the sensitivity decreased to 80.0%, and the average lesion number increased to 2.05 on the test set. If an artificial neural network replaced the CNN, the sensitivity decreased to 85.0%, and the average lesion number increased to 4.65. Significance: Automated detection of FCD with high sensitivity and few false-positive findings is feasible based on multi-modal data. PET data and CNN could improve the performance of automated detection.
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