Dynamic indocyanine green angiography measurements

Area of Science:

• Ophthalmology
• Medical Imaging
• Biomedical Engineering

Background:

• Dynamic indocyanine green (ICG) imaging visualizes retinal and choroidal blood flow using a scanning laser ophthalmoscope and fluorescent dye.
• Current interpretation of ICG angiography movies is complex, limiting its clinical utility for conditions like choroidal neovascularization.
• Accurate assessment of vascular anatomy and hemodynamics is crucial for treatment planning and monitoring disease progression.

Purpose of the Study:

• To develop and validate software algorithms for simplifying the interpretation of dynamic ICG angiography.
• To create user-friendly images from ICG movies that reveal key vascular and perfusion parameters.
• To enhance the clinical application of dynamic ICG imaging in ophthalmology and potentially other surgical fields.

Main Methods:

• Development of a mathematical model for blood flow dynamics in the retina and choroid.
• Design of a fitting algorithm to solve for flow parameters from ICG movie data.
• Generation of interpretable images quantifying parameters like dye fill-time and temporal dispersion.

Main Results:

• The software successfully generates images that are easier to interpret than raw ICG angiography movies.
• Identified clinically relevant anatomical structures, including feeder vessels, drain vessels, and capillary networks.
• Quantified perfusion characteristics, revealing changes between examinations and aiding in lesion characterization.

Conclusions:

• The developed software significantly improves the interpretability of dynamic ICG angiography, aiding in the diagnosis and management of ocular vascular diseases.
• This approach offers a valuable tool for research into neovascular conditions and their treatments.
• The methodology shows promise for broader applications in assessing tissue perfusion and vascularity across various surgical specialties.