Winder Anthony J, Wilms Matthias, Amador Kimberly, Flottmann Fabian, Fiehler Jens, Forkert Nils D
Department of Radiology, University of Calgary, Calgary, AB, Canada.
Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada.
Front Neurosci. 2022 Nov 4;16:1009654. doi: 10.3389/fnins.2022.1009654. eCollection 2022.
Predicting follow-up lesions from baseline CT perfusion (CTP) datasets in acute ischemic stroke patients is important for clinical decision making. Deep convolutional networks (DCNs) are assumed to be the current state-of-the-art for this task. However, many DCN classifiers have not been validated against the methods currently used in research (random decision forests, RDF) and clinical routine (Tmax thresholding). Specialized DCNs have even been designed to extract complex temporal features directly from spatiotemporal CTP data instead of using standard perfusion parameter maps. However, the benefits of applying deep learning to source or deconvolved CTP data compared to perfusion parameter maps have not been formally investigated so far. In this work, a modular UNet-based DCN is proposed that separates temporal feature extraction from tissue outcome prediction, allowing for both model validation using perfusion parameter maps as well as end-to-end learning from spatiotemporal CTP data. 145 retrospective datasets comprising baseline CTP imaging, perfusion parameter maps, and follow-up non-contrast CT with manual lesion segmentations were assembled from acute ischemic stroke patients treated with intravenous thrombolysis alone (IV; = 43) or intra-arterial mechanical thrombectomy (IA; = 102) with or without combined IV. Using the perfusion parameter maps as input, the proposed DCN (mean Dice: 0.287) outperformed the RDF (0.262) and simple Tmax-thresholding (0.249). The performance of the proposed DCN was approximately equal using features optimized from the deconvolved residual curves (0.286) compared to perfusion parameter maps (0.287), while using features optimized from the source concentration-time curves (0.296) provided the best tissue outcome predictions.
预测急性缺血性中风患者基线CT灌注（CTP）数据集中的后续病变对于临床决策至关重要。深度卷积网络（DCN）被认为是目前这项任务的最先进技术。然而，许多DCN分类器尚未针对目前研究中使用的方法（随机决策森林，RDF）和临床常规方法（Tmax阈值法）进行验证。专门设计的DCN甚至可以直接从时空CTP数据中提取复杂的时间特征，而不是使用标准灌注参数图。然而，到目前为止，与灌注参数图相比，将深度学习应用于源CTP数据或去卷积CTP数据的优势尚未得到正式研究。在这项工作中，提出了一种基于模块化UNet的DCN，它将时间特征提取与组织结果预测分开，既允许使用灌注参数图进行模型验证，也允许从时空CTP数据进行端到端学习。从仅接受静脉溶栓治疗（IV；n = 43）或接受动脉内机械取栓治疗（IA；n = 102）（有或没有联合IV治疗）的急性缺血性中风患者中收集了145个回顾性数据集，包括基线CTP成像、灌注参数图以及带有手动病变分割的随访非增强CT。以灌注参数图作为输入，所提出的DCN（平均Dice系数：0.287）优于RDF（0.262）和简单的Tmax阈值法（0.249）。与灌注参数图（0.287）相比，使用从去卷积残差曲线优化的特征时，所提出的DCN的性能大致相同（0.286），而使用从源浓度-时间曲线优化的特征时，提供了最佳的组织结果预测（0.296）。
