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：针对急性缺血性卒中（AIS）患者的缺血区域（包括坏死组织即核心区、可挽救组织即半暗带）的精准快速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着关键作用。计算机断层扫描（CT）是疑似AIS患者早期评估的主要模态之一，其中CT灌注成像（CTP）常被用于初步评估以确定卒中部位、严重程度及缺血病灶体积。当前CTP的自动分割方法大多以放射科医师临床判读中常规使用的已处理3D参数图作为输入。另一种方式是将原始CTP数据以逐层切片形式作为2D+时间输入，但忽略了三维空间信息。此外，现有方法仅关注核心区分割，而半暗带预测对治疗规划至关重要。本文研究了利用完整4D CTP数据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4D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，所提出的模型能生成比其它探索方法更精确的结果。采用所提出的4D mJ-Net后，半暗带与核心区分割的Dice系数分别达到0.53和0.23。相关代码已在https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git上公开。