A comprehensive framework for the detection of individual brain perfusion abnormalities using arterial spin labeling

Area of Science:

• Neuroimaging
• Medical Physics
• Radiology

Background:

• Arterial spin labeling (ASL) is a non-invasive MRI technique for measuring cerebral blood flow (CBF).
• ASL-derived perfusion patterns are crucial for diagnosing pathologies linked to altered blood flow.
• Current methods for identifying abnormal perfusion patterns in ASL are subjective, relying on visual inspection or manual region delineation.

Purpose of the Study:

• To introduce a novel framework for the automated outlining of abnormal perfusion patterns in individual ASL datasets.
• To enable objective and efficient identification of perfusion abnormalities without manual intervention.
• To compare the performance of two normal perfusion models and evaluate detection quality using an a contrario approach versus the generalized linear model (GLM).

Main Methods:

• Development of a new framework utilizing an ASL template for automated pattern outlining.
• Comparison of two distinct models representing normal cerebral perfusion.
• Assessment of detection quality through a comparative analysis of an a contrario method and the generalized linear model (GLM).

Main Results:

• The proposed framework demonstrates the capability to automatically outline abnormal perfusion patterns in individual ASL data.
• Comparative analysis provides insights into the performance differences between the evaluated perfusion models and detection methods.
• The study lays the groundwork for more objective and reproducible analysis of ASL perfusion data.

Conclusions:

• The developed framework offers a significant advancement over manual methods for identifying abnormal perfusion in ASL.
• Automated outlining of perfusion patterns enhances diagnostic efficiency and objectivity in neurological assessments.
• Further research comparing different statistical models and validation across diverse patient cohorts is warranted.