AI-Enhanced Cerebral Perfusion Evaluation in Dementia Using 3T ASL MRI (Code-T0284)

Introduction: Dementia is a progressive neurodegenerative disorder characterized by cognitive decline and impaired cerebral perfusion. Arterial Spin Labeling (ASL) MRI is a non-invasive imaging technique that quantifies cerebral blood flow (CBF), aiding in early diagnosis and disease monitoring. Integrating artificial intelligence (AI) into ASL MRI analysis enhances precision, automation, and diagnostic accuracy.

Aims and Objectives: This study aims to evaluate cerebral perfusion patterns in dementia patients using 3T ASL MRI and assess the role of AI-driven analysis in improving diagnostic accuracy. The study also seeks to establish a correlation between CBF alterations and cognitive impairment severity.

Methods: A prospective observational study will be conducted on dementia patients and age-matched healthy controls. Participants will undergo 3T ASL MRI to measure regional CBF, with AI-based algorithms applied for automated segmentation and quantification. Statistical analysis will compare perfusion deficits across dementia subtypes and correlate findings with cognitive assessments.

Results: It is anticipated that AI-assisted ASL MRI will enhance the detection of hypoperfusion in key brain regions, including the temporoparietal and frontal lobes. AI-based analysis is expected to improve diagnostic efficiency and provide reliable biomarkers for early-stage dementia.

Conclusion: AI-driven ASL MRI presents a promising advancement in dementia diagnostics, offering a non-invasive and precise method for assessing cerebral perfusion. This approach could facilitate early intervention and personalized treatment strategies, improving patient outcomes.

Keywords: Dementia, Arterial Spin Labeling, Cerebral Perfusion, Artificial Intelligence, MRI, Neuroimaging.