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：针对急性缺血性脑卒中（AIS）患者，准确且快速地预测由坏死组织（核心区）和可挽救组织（半暗带）构成的缺血区域具有重要的临床意义。这些方法在改善诊断和治疗规划中扮演着关键角色。计算机断层扫描（CT）是疑似AIS患者早期评估的主要检查手段之一。CT灌注成像（CTP）常被用作确定卒中位置、严重程度及缺血病灶体积的主要评估方法。当前主流的CTP自动分割方法大多使用已处理的三维参数图（放射科医师临床判读的常规输入）作为输入。另一种方法是逐层使用原始CTP数据，即采用二维+时间输入，此时忽略了整个容积的空间信息。此外，这些方法仅关注核心区的分割，而预测半暗带对治疗规划至关重要。本文研究了利用完整四维CTP作为输入以充分挖掘时空信息的不同方法，并据此提出了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全面实验表明，所提出的模型比探索的其他方法能生成更精确的结果。采用所提出的4D mJ-Net模型，半暗带和核心区分割的Dice系数分别达到0.53和0.23。相关代码已开源在 https://github.com/Biomedical-Data-Analysis-Laboratory/4D-mJ-Net.git。