Precise and fast prediction methods for ischemic areas comprised of dead tissue, core, and salvageable tissue, penumbra, in acute ischemic stroke (AIS) patients are of significant clinical interest. They play an essential role in improving diagnosis and treatment planning. Computed Tomography (CT) scan is one of the primary modalities for early assessment in patients with suspected AIS. CT Perfusion (CTP) is often used as a primary assessment to determine stroke location, severity, and volume of ischemic lesions. Current automatic segmentation methods for CTP mostly use already processed 3D parametric maps conventionally used for clinical interpretation by radiologists as input. Alternatively, the raw CTP data is used on a slice-by-slice basis as 2D+time input, where the spatial information over the volume is ignored. In addition, these methods are only interested in segmenting core regions, while predicting penumbra can be essential for treatment planning. This paper investigates different methods to utilize the entire 4D CTP as input to fully exploit the spatio-temporal information, leading us to propose a novel 4D convolution layer. Our comprehensive experiments on a local dataset of 152 patients divided into three groups show that our proposed models generate more precise results than other methods explored. Adopting the proposed 4D mJ-Net, a Dice Coefficient of 0.53 and 0.23 is achieved for segmenting penumbra and core areas, respectively. The code is available on https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git.

翻译：急性缺血性脑卒中患者的缺血区域包括死亡组织（核心区）和可挽救组织（半暗带），对其精确、快速的预测方法具有显著的临床意义。这些方法在改善诊断和治疗规划中发挥关键作用。计算机断层扫描是疑似AIS患者早期评估的主要成像方式之一。CT灌注成像常作为一级评估手段，用于确定卒中位置、严重程度及缺血病灶的容量。现有基于CTP的自动分割方法大多采用放射科医师临床判读时常规使用的已处理三维参数图作为输入。另一些方法则按单层切片将原始CTP数据作为二维+时间输入，忽略了体积空间信息。此外，这些方法仅聚焦于核心区域的分割，而半暗带的预测对治疗规划至关重要。本文探索了利用完整四维CTP作为输入以充分挖掘时空信息的不同方法，并由此提出一种新型四维卷积层。我们在包含152名患者（分为三组）的本地数据集上进行的综合实验表明，所提模型生成的结果较其他方法更为精确。采用提出的4D mJ-Net，在半暗带和核心区域分割上分别实现了0.53和0.23的Dice系数。代码已发布在https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git。