Normalization is essential for accurate intraoperative perfusion assessment using near-infrared fluorescence imaging

Area of Science:

• Medical imaging
• Biophotonics
• Surgical technology

Background:

• Near-infrared fluorescence (NIRF) imaging is vital for assessing tissue perfusion in clinical and research settings.
• Quantitative analysis of NIRF imaging is essential for broader adoption but faces challenges due to strict standardization requirements conflicting with clinical workflows.
• Current limitations hinder the widespread implementation of quantitative NIRF perfusion imaging.

Purpose of the Study:

• To evaluate if normalization of fluorescence measurements can correct for variability and reduce the need for strict standardization in quantitative NIRF perfusion imaging.
• To test the hypothesis that normalization enhances the consistency of perfusion parameter measurements.
• To assess the impact of normalization under varying measurement conditions.

Main Methods:

• A simulation model was used to generate consistent fluorescence perfusion patterns.
• Experiments were conducted in an operating room using four different NIRF camera systems.
• Measurements were taken under varied conditions, including camera type, angle, rotation, and settings, with and without normalization.

Main Results:

• Normalization to maximum signal intensity yielded consistent perfusion parameters across different camera systems and settings (Δ≤1%).
• Normalization effectively corrected for measurement variability under standard conditions.
• In cases of severe malperfusion, where peak intensity was not reached, normalization was less effective in correcting variability.

Conclusions:

• Appropriate normalization significantly reduces the reliance on strict measurement protocols for quantitative NIRF imaging.
• While standardization is still valuable, normalization improves parameter accuracy and consistency.
• These findings facilitate broader clinical adoption of quantitative NIRF imaging in routine surgical care.