PDF(Pubmed)
Abstract：
Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its utilization remains limited. A key challenge in the quantitative analysis of dFDG-PET is characterizing a patient-specific blood input function, traditionally reliant on invasive arterial blood sampling. This research introduces a novel approach employing non-invasive deep learning model-based computations from the internal carotid arteries (ICA) with partial volume (PV) corrections, thereby eliminating the need for invasive arterial sampling. We present an end-to-end pipeline incorporating a 3D U-Net based ICA-net for ICA segmentation, alongside a Recurrent Neural Network (RNN) based MCIF-net for the derivation of a model-corrected blood input function (MCIF) with PV corrections. The developed 3D U-Net and RNN was trained and validated using a 5-fold cross-validation approach on 50 human brain FDG PET scans. The ICA-net achieved an average Dice score of 82.18% and an Intersection over Union of 68.54% across all tested scans. Furthermore, the MCIF-net exhibited a minimal root mean squared error of 0.0052. The application of this pipeline to ground truth data for dFDG-PET brain scans resulted in the precise localization of seizure onset regions, which contributed to a successful clinical outcome, with the patient achieving a seizure-free state after treatment. These results underscore the efficacy of the ICA-net and MCIF-net deep learning pipeline in learning the ICA structure\'s distribution and automating MCIF computation with PV corrections. This advancement marks a significant leap in non-invasive neuroimaging.
摘要：
动态2-[18F]氟-2-脱氧-D-葡萄糖正电子发射断层扫描（dFDG-PET）用于人脑成像具有相当大的临床潜力，然而，它的利用仍然有限。dFDG-PET定量分析的一个关键挑战是表征患者特定的血液输入功能。传统上依赖于侵入性动脉血采样。这项研究引入了一种新颖的方法，该方法采用了基于颈内动脉（ICA）的非侵入性深度学习模型的计算，并进行了部分容积（PV）校正。从而消除了侵入性动脉采样的需要。我们提出了一种端到端管道，该管道结合了基于3DU-Net的ICA网络，用于ICA分割，与基于递归神经网络（RNN）的MCIF网一起，用于推导具有PV校正的模型校正血液输入函数（MCIF）。在50个人脑FDGPET数据集上使用5倍交叉验证方法对开发的3DU-Net和RNN进行了训练和验证。在所有测试的扫描中，ICA-net的平均Dice评分为82.18％，而Union的交集为68.54％。此外，MCIF-net表现出0.0052的最小均方根误差。将该管道应用于dFDG-PET脑部扫描的地面实况数据导致了癫痫发作发作区域的精确定位，这有助于成功的临床结果，患者在治疗后达到无癫痫状态。这些结果强调了ICA-net和MCIF-net深度学习管道在学习ICA结构的分布和使用PV校正自动化MCIF计算方面的有效性。这一进步标志着非侵入性神经成像的重大飞跃。
