Computed tomography perfusion (CTP) at admission is routinely used to estimate the ischemic core and penumbra, while follow-up diffusion-weighted MRI (DWI) provides the definitive infarct outcome. However, single time-point segmentations fail to capture the biological heterogeneity and temporal evolution of stroke. We propose a bi-temporal analysis framework that characterizes ischemic tissue using statistical descriptors, radiomic texture features, and deep feature embeddings from two architectures (mJ-Net and nnU-Net). Bi-temporal refers to admission (T1) and post-treatment follow-up (T2). All features are extracted at T1 from CTP, with follow-up DWI aligned to ensure spatial correspondence. Manually delineated masks at T1 and T2 are intersected to construct six regions of interest (ROIs) encoding both initial tissue state and final outcome. Features were aggregated per region and analyzed in feature space. Evaluation on 18 patients with successful reperfusion demonstrated meaningful clustering of region-level representations. Regions classified as penumbra or healthy at T1 that ultimately recovered exhibited feature similarity to preserved brain tissue, whereas infarct-bound regions formed distinct groupings. Both baseline GLCM and deep embeddings showed a similar trend: penumbra regions exhibit features that are significantly different depending on final state, whereas this difference is not significant for core regions. Deep feature spaces, particularly mJ-Net, showed strong separation between salvageable and non-salvageable tissue, with a penumbra separation index that differed significantly from zero (Wilcoxon signed-rank test). These findings suggest that encoder-derived feature manifolds reflect underlying tissue phenotypes and state transitions, providing insight into imaging-based quantification of stroke evolution.

翻译：入院时计算机断层扫描灌注成像（CTP）常规用于评估缺血核心区与半暗带，而后续弥散加权磁共振成像（DWI）可提供确切的梗死结局。然而，单一时相的图像分割无法捕捉卒中病变的生物学异质性及时序演变过程。本研究提出一种双时相分析框架，通过统计描述符、影像组学纹理特征以及来自两种架构（mJ-Net与nnU-Net）的深度特征嵌入来表征缺血组织。双时相指入院时相（T1）与治疗后随访时相（T2）。所有特征均在T1时相从CTP影像中提取，并通过空间配准确保与随访DWI影像的对应性。通过融合T1与T2时相的手动勾画掩膜，构建了六个同时编码初始组织状态与最终结局的关注区域。对各区域特征进行空间聚合分析。在18例成功实现再灌注的患者评估中，区域级表征呈现出具有临床意义的聚类模式：T1时相被归类为半暗带或健康且最终恢复的区域，其特征与保留的脑组织具有相似性，而最终梗死的区域则形成独立聚类组。基线灰度共生矩阵特征与深度嵌入特征均呈现相似趋势：半暗带区域的特征因其最终状态不同而呈现显著差异，而核心区则未出现显著差异。深度特征空间（特别是mJ-Net）在可挽救与不可挽救组织间表现出明显分离，其半暗带分离指数经Wilcoxon符号秩检验证实显著偏离零值。这些发现表明，编码器衍生的特征流形能够反映潜在的组织表型与状态转换，为基于影像的卒中演变量化分析提供了新的见解。