Beyond Core and Penumbra: Bi-Temporal Image-Driven Stroke Evolution Analysis

2026-02-07

AI Summary

Current single-timepoint imaging segmentation struggles to capture the biological heterogeneity and dynamic evolution of stroke tissue. This study proposes a dual-timepoint analytical framework—integrating baseline CT perfusion (CTP) and follow-up diffusion-weighted imaging (DWI)—that, for the first time, combines spatial alignment, multimodal features, and deep learning (mJ-Net/nnU-Net) with GLCM texture analysis and manually annotated masks to define six distinct regions characterizing the trajectory of ischemic tissue from initial state to final outcome. In 18 successfully reperfused patients, deep learning-derived features—particularly those from mJ-Net—significantly differentiated salvageable from nonsalvageable tissue, yielding a penumbral dissociation index significantly different from zero. These findings transcend the conventional core/penumbra dichotomy and reveal the phenotypic underpinnings of tissue fate transformation.

Technology Category

• Computer Vision: Segmentation
• Machine Learning: Multi-instance/Multi-view Learning
• Intelligent Robots: Multimodal Perception & Sensor Fusion

Application Category

• Web Mining and Content Analysis: Models for Web evolution
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

Abstract

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.

Problem

• stroke evolution
• ischemic core
• penumbra
• bi-temporal imaging
• tissue heterogeneity

Innovation

• bi-temporal analysis
• stroke evolution
• radiomic features
• deep feature embeddings
• penumbra salvageability