Spectral analysis assisted photoacoustic imaging for lipid composition differentiation

Area of Science:

• Biomedical Optics
• Medical Imaging
• Cardiovascular Research

Background:

• Atherosclerotic plaque vulnerability is linked to lipid core size, depth, and composition, particularly cholesterol deposition.
• Accurate characterization of plaque lipid content is crucial for predicting rupture and thrombus formation.
• Current imaging methods may lack the specificity to differentiate various lipid compositions within plaques.

Purpose of the Study:

• To develop and evaluate a spectral analysis assisted photoacoustic imaging (PAI) approach for differentiating and mapping arterial lipid compositions.
• To assess the accuracy and efficiency of this PAI method in identifying cholesterol and other lipids.

Main Methods:

• Utilized time-of-flight photoacoustic signals analyzed using spectral curves.
• Employed a numerical k-means clustering method for classifying spectral curves from sliding windows.
• Validated the approach on a vessel-mimicking phantom containing cholesterol and olive oil.

Main Results:

• The spectral analysis PAI method successfully differentiated between cholesterol and olive oil in the phantom.
• The k-means clustering accurately classified spectral curves, demonstrating the method's efficiency.
• Achieved high accuracy in mapping lipid compositions within the arterial phantom.

Conclusions:

• Spectral analysis assisted photoacoustic imaging is a promising technique for detailed lipid composition mapping in arteries.
• This method offers potential for improved assessment of atherosclerotic plaque vulnerability.
• Further application in clinical settings could enhance the diagnosis and management of atherosclerosis.