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Related Experiment Video

Updated: Jan 29, 2026

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Technical Approach for Infrared Tracking for Soft Tissue Navigation with a Holographic Head-Mounted Display and Preclinical Validation

Published on: September 2, 2025

Technical Note: In silico and experimental evaluation of two leaf-fitting algorithms for MLC tracking based on

Vincent Caillet^1,2, Ricky O'Brien², Douglas Moore³

¹Northern Sydney Cancer Centre, Sydney, NSW, Australia.

Medical Physics

|February 6, 2019

Summary

This study compared two multileaf collimator (MLC) leaf-fitting algorithms for radiotherapy motion compensation. Plan complexity significantly impacts exposure errors more than the choice of algorithm.

Keywords:

MLC tracking fitting algorithm radiotherapy real-time

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Last Updated: Jan 29, 2026

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Area of Science:

Medical Physics
Radiation Oncology
Radiotherapy Technology

Background:

Real-time multileaf collimator (MLC) tracking is emerging for radiotherapy to manage thoracic and pelvic motion.
Accurate MLC leaf positioning is crucial for effective motion compensation during treatment.

Purpose of the Study:

To evaluate and compare the performance of direct optimization and piecewise optimization MLC leaf-fitting algorithms.
To characterize algorithm performance under varying plan complexity and tumor trajectories for real-time motion compensation.

Main Methods:

In silico and phantom experiments were conducted using a Varian linac and a HexaMotion platform.
High and low modulation VMAT plans for lung and prostate cancer were tested with patient-specific trajectories.
Average exposure errors, plan complexity, and system latency were calculated to compare algorithms.

Main Results:

Both direct and piecewise optimization algorithms showed minor differences in average exposure errors across in silico and phantom experiments.
Average exposure errors were 0.66-0.88 cm² (in silico) and 0.73-1.02 cm² (phantom) for low/high plan complexity.
High plan complexity resulted in significantly higher exposure errors (mean 0.96 cm²) compared to low plan complexity (mean 0.70 cm²).

Conclusions:

No significant differences in exposure errors were found between the direct optimization and piecewise optimization MLC leaf-fitting algorithms.
Plan complexity is a more significant factor influencing overall exposure errors than the specific leaf-fitting algorithm used.
These findings support the clinical implementation of MLC tracking, highlighting the importance of treatment planning considerations.