This traditional poster session focuses on innovative imaging and computational approaches to improve the detection, characterization, and prognosis of stroke and cerebrovascular injury. Presentations span advanced MRI techniques (e.g., diffusion models, APTw imaging, vascular fingerprinting, susceptibility imaging), physiological and biochemical markers of ischemia, and machine learning models for outcome prediction. Several studies also introduce synthetic data generation and mechanistic modeling to support robust algorithm development. Together, these posters highlight how quantitative imaging and AI-driven methods can deepen understanding of stroke pathophysiology and support more precise clinical decision-making.