用GAN从MRI生成假PET图像，提升癫痫异常检测模型性能
GAN-based synthetic FDG PET images from T1 brain MRI can serve to improve performance of deep unsupervised anomaly detection models
- 用GAN将T1 MRI转为合成FDG PET图像
- 合成数据训练的模型在癫痫检测中达74%敏感度
- 适合医学影像异常检测研究者参考
近年来，基于GAN的跨模态医学图像生成在缓解多模态数据稀缺问题上表现优异。本文设计并比较多种GAN框架，从T1加权MRI生成[18F]氟脱氧葡萄糖（FDG）PET图像。首先进行标准的定性与定量视觉质量评估，随后探索这些合成PET数据对深度无监督异常检测（UAD）模型训练的影响。该模型用于检测癫痫患者在T1 MRI和FDG PET中的细微病灶，采用基于孪生自编码器的表征学习结合OC-SVM密度估计。模型仅在正常受试者数据上训练，可识别与正常模式的偏离。对比在35例真实配对数据和35例合成数据上训练的模型，评估在17例手术癫痫患者检查中的表现。最佳生成模型的合成图像在结构相似性（SSIM）和峰值信噪比（PSNR）分别达0.9和23.8，且分布上与真实对照组一致。使用最优合成数据训练的UAD模型达到74%敏感度。结果表明，GAN在MR T1到FDG PET转换中优于变换器或扩散模型，并证明合成数据对训练诊断模型具有实际价值。代码与正常对照图像数据集已公开。
原文摘要 · Abstract (English)
Background and Objective. Research in the cross-modal medical image translation domain has been very productive over the past few years in tackling the scarce availability of large curated multimodality datasets with the promising performance of GAN-based architectures. However, only a few of these studies assessed task-based related performance of these synthetic data, especially for the training of deep models. Method. We design and compare different GAN-based frameworks for generating synthetic brain [18F]fluorodeoxyglucose (FDG) PET images from T1 weighted MRI data. We first perform standard qualitative and quantitative visual quality evaluation. Then, we explore further impact of using these fake PET data in the training of a deep unsupervised anomaly detection (UAD) model designed to detect subtle epilepsy lesions in T1 MRI and FDG PET images. We introduce novel diagnostic task-oriented quality metrics of the synthetic FDG PET data tailored to our unsupervised detection task, then use these fake data to train a use case UAD model combining a deep representation learning based on siamese autoencoders with a OC-SVM density support estimation model. This model is trained on normal subjects only and allows the detection of any variation from the pattern of the normal population. We compare the detection performance of models trained on 35 paired real MR T1 of normal subjects paired either on 35 true PET images or on 35 synthetic PET images generated from the best performing generative models. Performance analysis is conducted on 17 exams of epilepsy patients undergoing surgery. Results. The best performing GAN-based models allow generating realistic fake PET images of control subject with SSIM and PSNR values around 0.9 and 23.8, respectively and in distribution (ID) with regard to the true control dataset. The best UAD model trained on these synthetic normative PET data allows reaching 74% sensitivity. Conclusion. Our results confirm that GAN-based models are the best suited for MR T1 to FDG PET translation, outperforming transformer or diffusion models. We also demonstrate the diagnostic value of these synthetic data for the training of UAD models and evaluation on clinical exams of epilepsy patients. Our code and the normative image dataset are available.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。