SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time

Tiwari, P; Xie, Y; Chen, Y; Deasy, J

doi:10.1118/1.4888351

Title: SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time

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Abstract

Purpose: The IMRT optimization problem requires substantial computer time to find optimal dose distributions because of the large number of variables and constraints. Voxel sampling reduces the number of constraints and accelerates the optimization process, but usually deteriorates the quality of the dose distributions to the organs. We propose a novel sampling algorithm that accelerates the IMRT optimization process without significantly deteriorating the quality of the dose distribution. Methods: We included all boundary voxels, as well as a sampled fraction of interior voxels of organs in the optimization. We selected a fraction of interior voxels using a clustering algorithm, that creates clusters of voxels that have similar influence matrix signatures. A few voxels are selected from each cluster based on the pre-set sampling rate. Results: We ran sampling and no-sampling IMRT plans for de-identified head and neck treatment plans. Testing with the different sampling rates, we found that including 10% of inner voxels produced the good dose distributions. For this optimal sampling rate, the algorithm accelerated IMRT optimization by a factor of 2–3 times with a negligible loss of accuracy that was, on average, 0.3% for common dosimetric planning criteria. Conclusion: We demonstrated that a sampling could be developed thatmore »« less

Authors:

Tiwari, P; Xie, Y; Chen, Y ^[1]; Deasy, J ^[2]

Washington University in Saint Louis, Saint Louis, Missouri (United States)
Memorial Sloan Kettering Cancer Center, NY, NY (United States)

Publication Date:: Sun Jun 01 00:00:00 EDT 2014

OSTI Identifier:: 22339800

Resource Type:: Journal Article

Journal Name:: Medical Physics

Additional Journal Information:: Journal Volume: 41; Journal Issue: 6; Other Information: (c) 2014 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA); Journal ID: ISSN 0094-2405

Country of Publication:: United States

Language:: English

Subject:: 62 RADIOLOGY AND NUCLEAR MEDICINE; 60 APPLIED LIFE SCIENCES; ACCURACY; ALGORITHMS; HEAD; LIMITING VALUES; NECK; OPTIMIZATION; ORGANS; PLANNING; RADIATION DOSE DISTRIBUTIONS; RADIATION DOSES; RADIOTHERAPY; SAMPLING

Citation Formats


                    Tiwari, P, Xie, Y, Chen, Y, and Deasy, J. SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time.  United States: N. p., 2014. 
        Web.  doi:10.1118/1.4888351.

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                    Tiwari, P, Xie, Y, Chen, Y, & Deasy, J. SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time.  United States.  https://doi.org/10.1118/1.4888351

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                    Tiwari, P, Xie, Y, Chen, Y, and Deasy, J. 2014.  
        "SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time".  United States.  https://doi.org/10.1118/1.4888351.

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@article{osti_22339800,

  title        = {SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time},

  author       = {Tiwari, P and Xie, Y and Chen, Y and Deasy, J},

  abstractNote = {Purpose: The IMRT optimization problem requires substantial computer time to find optimal dose distributions because of the large number of variables and constraints. Voxel sampling reduces the number of constraints and accelerates the optimization process, but usually deteriorates the quality of the dose distributions to the organs. We propose a novel sampling algorithm that accelerates the IMRT optimization process without significantly deteriorating the quality of the dose distribution. Methods: We included all boundary voxels, as well as a sampled fraction of interior voxels of organs in the optimization. We selected a fraction of interior voxels using a clustering algorithm, that creates clusters of voxels that have similar influence matrix signatures. A few voxels are selected from each cluster based on the pre-set sampling rate. Results: We ran sampling and no-sampling IMRT plans for de-identified head and neck treatment plans. Testing with the different sampling rates, we found that including 10% of inner voxels produced the good dose distributions. For this optimal sampling rate, the algorithm accelerated IMRT optimization by a factor of 2–3 times with a negligible loss of accuracy that was, on average, 0.3% for common dosimetric planning criteria. Conclusion: We demonstrated that a sampling could be developed that reduces optimization time by more than a factor of 2, without significantly degrading the dose quality.},

  doi          = {10.1118/1.4888351},

  url          = {https://www.osti.gov/biblio/22339800},
  journal      = {Medical Physics},

  issn         = {0094-2405},

  number       = 6,

  volume       = 41,

  place        = {United States},

  year         = {Sun Jun 01 00:00:00 EDT 2014},

  month        = {Sun Jun 01 00:00:00 EDT 2014}

}

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