Intraoperative blood vessel detection and quantification: a Monte Carlo study

Area of Science:

• Biomedical Engineering
• Surgical Technology

Background:

• Minimally invasive surgery (MIS) offers benefits like faster recovery and reduced scarring.
• A key limitation of MIS is the loss of tactile feedback, preventing surgeons from palpating blood vessels.
• Accidental intraoperative blood vessel injury during MIS can lead to severe complications.

Purpose of the Study:

• To develop a novel method for detecting hidden blood vessels during MIS.
• To enhance the safety of laparoscopic procedures by identifying subsurface vasculature.

Main Methods:

• Integration of a near-infrared (NIR) light-emitting diode (LED) and a photodiode onto laparoscopic grasper jaws.
• Utilizing Monte Carlo simulations to model light interaction with tissue and blood vessels.
• Quantifying the detection capabilities for blood vessels of varying sizes and depths.

Main Results:

• The developed laparoscopic graspers can detect blood vessels ranging from 3 to 6 mm in diameter.
• Detection is feasible for vessels buried under up to 1 cm of tissue.
• Monte Carlo simulations support the theoretical basis for this detection technology.

Conclusions:

• This technology offers a potential solution to the challenge of subsurface blood vessel identification in MIS.
• Adding NIR detection capabilities to existing laparoscopic tools can significantly improve surgical safety.
• Further development could lead to widespread adoption in minimally invasive procedures.