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From Fluence to Dose: Real-Time Adaptive MLC Tracking Using Dose Optimization

L Mejnertsen1*, E Hewson1, D Nguyen1,2, J Booth3,4, P Keall1, (1) ACRF Image-X Institute, The University of Sydney, Camperdown, NSW, AU, (2) School of Biomedical Engineering, University of Technology Sydney, NSW, AU, (3) Royal North Shore Hospital, Sydney, NSW, AU, (4) Medical Physics, School of Physics, University of Sydney, Camperdown, NSW, AU.


(Sunday, 7/12/2020)   [Eastern Time (GMT-4)]

Room: AAPM ePoster Library

Motion in the patient anatomy causes a reduction in dose delivered to the target, while increasing dose to healthy tissue. Multi-Leaf Collimator (MLC) tracking has been clinically implemented to adapt for intrafraction motion. This implementation optimizes the MLC based on fluence, the drawback of which is that 3D dose, a function of patient anatomy and MLC aperture sequence is not properly accounted for. We aim to improve on current methods by developing and investigating dose optimization with real-time adaptive MLC tracking.

A novel motion adaptation algorithm (dose optimization) has been developed which accounts for the moving patient anatomy by accumulating dose in silico during treatment. The planned dose is calculated in the patient volume, then shifted in the direction of motion. The MLC aperture is optimized by minimizing the difference between the accumulation of the delivered and planned dose. Finally, the delivered dose is calculated with the new apertures. This process repeats until treatment finishes.
The method was benchmarked against a prostate cancer VMAT treatment dataset with observed intrafraction motion. MLC tracking was applied to fifteen fractions, comparing three
methods: dose optimization, fluence optimization, and without optimization. To assess performance, we compare the dose error fraction of the total planned dose.

Dose optimization shows a reduction of dose error due to motion from (8.3±4.4)% (mean and standard deviation) of the total dose with no optimization to (3.6±0.6)%. This shows an improvement over fluence optimization (4.8±1.3)%. The algorithm takes (38±9)ms per aperture calculation.

By considering the accumulation of dose in the moving anatomy during treatment, dose optimization has been shown to reduce the dose error to levels below the clinical standard and the current fluence optimization. This shows that adapting the MLC to account for dose accumulation can provide better conformity to the planned dose.

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Funding Support, Disclosures, and Conflict of Interest: This work is funded by Cancer Council NSW. DTN is funded by NHMRC and Cancer Institute NSW early career fellowships. PK is NHMRC Senior Principal Research Fellow.


DMLC, Optimization, Patient Movement


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