Improved Quantification of ICG Perfusion Through Motion Compensation in Fluorescence-Guided Surgery

Area of Science:

• Surgical imaging and diagnostics
• Medical device technology
• Quantitative fluorescence imaging

Background:

• Motion artifacts in fluorescence imaging distort perfusion measurements during surgery.
• Inaccurate perfusion assessment can lead to critical clinical decisions.
• Quantitative indocyanine green (q-ICG) imaging is vital for surgical perfusion assessment.

Purpose of the Study:

• To evaluate the efficacy of automated motion compensation (MC) in q-ICG imaging.
• To improve the accuracy of surgical perfusion assessment by reducing motion artifacts.
• To enhance the reliability of ICG-derived perfusion parameters.

Main Methods:

• Analysis of ICG perfusion assessment frames from 17 pancreaticoduodenectomies.
• Application of automated MC to track tissue movement and correct artifacts.
• Comparison of MC performance against surgeon-adjusted placement using image quality and perfusion metrics.
• Utilized Principal Component Analysis (PCA) to assess correction success.

Main Results:

• Automated MC successfully corrected motion artifacts in 67.5% of sequences, comparable to surgeon adjustments.
• PCA showed clear distinction between sufficient and insufficient motion correction (AUC = 0.80).
• While population-level perfusion metrics were unchanged, individual-level slope and Tmax values showed significant percentage changes after MC (e.g., 108.5% for slope).

Conclusions:

• Automated motion compensation is effective in mitigating motion artifacts in fluorescence-guided perfusion assessment.
• MC enhances the precision and reliability of ICG-derived perfusion parameters.
• This technology is a key enabler for accurate intraoperative perfusion quantification.