Back to Search
Start Over
Technical Note:In silico and experimental evaluation of two leaf-fitting algorithms for MLC tracking based on exposure error and plan complexity
- Source :
- Caillet, V, O'Brien, R, Moore, D, Poulsen, P, Pommer, T, Colvill, E, Sawant, A, Booth, J & Keall, P 2019, ' Technical Note : In silico and experimental evaluation of two leaf-fitting algorithms for MLC tracking based on exposure error and plan complexity ', Medical Physics, vol. 46, no. 4, pp. 1814-1820 . https://doi.org/10.1002/mp.13425, Med Phys
- Publication Year :
- 2019
-
Abstract
- Purpose: Multileaf collimator (MLC) tracking is being clinically pioneered to continuously compensate for thoracic and pelvic motion during radiotherapy. The purpose of this work was to characterize the performance of two MLC leaf-fitting algorithms, direct optimization and piecewise optimization, for real-time motion compensation with different plan complexity and tumor trajectories. Methods: To test the algorithms, both in silico and phantom experiments were performed. The phantom experiments were performed on a Trilogy Varian linac and a HexaMotion programmable motion platform. High and low modulation VMAT plans for lung and prostate cancer cases were used along with eight patient-measured organ-specific trajectories. For both MLC leaf-fitting algorithms, the plans were run with their corresponding patient trajectories. To compare algorithms, the average exposure errors, i.e., the difference in shape between ideal and fitted MLC leaves by the algorithm, plan complexity and system latency of each experiment were calculated. Results: Comparison of exposure errors for the in silico and phantom experiments showed minor differences between the two algorithms. The average exposure errors for in silico experiments with low/high plan complexity were 0.66/0.88 cm 2 for direct optimization and 0.66/0.88 cm 2 for piecewise optimization, respectively. The average exposure errors for the phantom experiments with low/high plan complexity were 0.73/1.02 cm 2 for direct and 0.73/1.02 cm 2 for piecewise optimization, respectively. The measured latency for the direct optimization was 226 ± 10 ms and for the piecewise algorithm was 228 ± 10 ms. In silico and phantom exposure errors quantified for each treatment plan demonstrated that the exposure errors from the high plan complexity (0.96 cm 2 mean, 2.88 cm 2 95% percentile) were all significantly different from the low plan complexity (0.70 cm 2 mean, 2.18 cm 2 95% percentile) (P
- Subjects :
- Male
Organs at Risk
Lung Neoplasms
Computer science
medicine.medical_treatment
real-time
Radiotherapy Setup Errors
Tracking (particle physics)
Imaging phantom
Article
030218 nuclear medicine & medical imaging
03 medical and health sciences
Prostate cancer
0302 clinical medicine
motion management
medicine
Humans
Computer Simulation
Organ Motion
radiotherapy
Motion compensation
Phantoms, Imaging
Radiotherapy Planning, Computer-Assisted
Prostatic Neoplasms
Radiotherapy Dosage
General Medicine
medicine.disease
Multileaf collimator
Radiation therapy
030220 oncology & carcinogenesis
Piecewise
fitting algorithm
Radiotherapy, Intensity-Modulated
MLC tracking
Particle Accelerators
Algorithm
Algorithms
Subjects
Details
- Language :
- English
- Database :
- OpenAIRE
- Journal :
- Caillet, V, O'Brien, R, Moore, D, Poulsen, P, Pommer, T, Colvill, E, Sawant, A, Booth, J & Keall, P 2019, ' Technical Note : In silico and experimental evaluation of two leaf-fitting algorithms for MLC tracking based on exposure error and plan complexity ', Medical Physics, vol. 46, no. 4, pp. 1814-1820 . https://doi.org/10.1002/mp.13425, Med Phys
- Accession number :
- edsair.doi.dedup.....89977cb98578a958e0f2d58dab328686
- Full Text :
- https://doi.org/10.1002/mp.13425