Improved partial volume correction method for detecting brain activation in disease using Arterial Spin Labeling...

Area of Science:

• Neuroimaging
• Functional Magnetic Resonance Imaging (fMRI)
• Cerebral Blood Flow (CBF) measurement

Background:

• Blood-Oxygen-Level-Dependent (BOLD) fMRI is crucial for brain function insights but limited in clinical research.
• Aging and diseased brains present challenges in fMRI signal measurement, interpretation, and partial voluming effects (PVE).

Purpose of the Study:

• To address limitations of existing fMRI techniques, particularly PVE in Arterial Spin Labeling (ASL) fMRI.
• To introduce and validate a novel, improved PVE correction (ssPVEc) algorithm for ASL fMRI.

Main Methods:

• Developed a structure-informed PVE correction (ssPVEc) algorithm for ASL fMRI.
• Applied ssPVEc to ASL data from patients with asymptomatic carotid occlusive disease during rest and motor activation.
• Compared the sensitivity of ssPVEc against the original functional space PVE correction (fsPVEc).

Main Results:

• The ssPVEc algorithm corrects for tissue-driven signal heterogeneity in ASL images.
• ssPVEc demonstrated at least 1.5 times greater sensitivity compared to fsPVEc in detecting motor activation in patients.
• The improved method enhances the detection of local CBF changes.

Conclusions:

• The ssPVEc algorithm significantly improves the sensitivity of ASL fMRI for clinical applications.
• This advancement helps overcome PVE challenges, enabling more accurate assessment of brain function in patient populations.
• ssPVEc offers a promising tool for clinical neuroimaging research.