TU‐G‐BRB‐07: Improve PTV Dose Distribution by Using Spatial Information in IMRT Optimization

A. Uribe‐sanchez, M. Zarepisheh, X. Jia, S. Jiang

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Purpose: The spatial distribution of radiation dose within the PTV does have clinical significance. For instance, if hot/cold spots in the PTV cannot be avoided, high doses are preferred to be located in the center, and low doses at the peripheries. However, traditional plan optimization models for IMRT usually treat equally voxels inside the same structure, failing to incorporate those location preferences. We present a re‐optimization model that, while preserving the quality of an initial treatment plan represented by DVH curves, incorporates spatial information for voxels inside the PTV into the optimization to generate a more desirable dose distribution. Methods: Our re‐optimization model incorporates a convex function that penalizes the deviation of the dose received by each voxel from an individual reference value. For PTV, the reference values per voxel match the ideal redistribution of the initial PTV dose, where voxels close to the boundary receive the low doses, while voxels in the center receive the high doses. For OAR, the reference value per voxel corresponds to its dose from the initial plan. In addition to the structure‐based weighting factors in traditional planning approaches, we incorporated individual penalty weights for PTV voxels. Structure‐based factors are calibrated according to the difference from the reference DVH curves, while voxel‐based, according to the difference to reference value. Results: We tested our model in four gynecologic cancer cases. For each case, we compare the resulting dose distribution within the PTV to that from the initial plan. It is observed that without sacrificing the plan quality represented by DVH curves, our re‐optimization model generates more desirable PTV dose distributions. Conclusions: We have presented a re‐optimization model that, by incorporating spatial location information for PTV voxels, yields to more clinically favorable dose distributions with similar DVH curves. This work is supported by Varian Medical Systems through a Master Research Agreement.

Original languageEnglish (US)
Pages (from-to)3920-3921
Number of pages2
JournalMedical Physics
Volume39
Issue number6
DOIs
StatePublished - 2012

Fingerprint

Reference Values
Radiation
Weights and Measures
Research
Neoplasms

ASJC Scopus subject areas

  • Biophysics
  • Radiology Nuclear Medicine and imaging

Cite this

TU‐G‐BRB‐07 : Improve PTV Dose Distribution by Using Spatial Information in IMRT Optimization. / Uribe‐sanchez, A.; Zarepisheh, M.; Jia, X.; Jiang, S.

In: Medical Physics, Vol. 39, No. 6, 2012, p. 3920-3921.

Research output: Contribution to journalArticle

@article{b0009dfac1f749fe85f43954663fb0c5,
title = "TU‐G‐BRB‐07: Improve PTV Dose Distribution by Using Spatial Information in IMRT Optimization",
abstract = "Purpose: The spatial distribution of radiation dose within the PTV does have clinical significance. For instance, if hot/cold spots in the PTV cannot be avoided, high doses are preferred to be located in the center, and low doses at the peripheries. However, traditional plan optimization models for IMRT usually treat equally voxels inside the same structure, failing to incorporate those location preferences. We present a re‐optimization model that, while preserving the quality of an initial treatment plan represented by DVH curves, incorporates spatial information for voxels inside the PTV into the optimization to generate a more desirable dose distribution. Methods: Our re‐optimization model incorporates a convex function that penalizes the deviation of the dose received by each voxel from an individual reference value. For PTV, the reference values per voxel match the ideal redistribution of the initial PTV dose, where voxels close to the boundary receive the low doses, while voxels in the center receive the high doses. For OAR, the reference value per voxel corresponds to its dose from the initial plan. In addition to the structure‐based weighting factors in traditional planning approaches, we incorporated individual penalty weights for PTV voxels. Structure‐based factors are calibrated according to the difference from the reference DVH curves, while voxel‐based, according to the difference to reference value. Results: We tested our model in four gynecologic cancer cases. For each case, we compare the resulting dose distribution within the PTV to that from the initial plan. It is observed that without sacrificing the plan quality represented by DVH curves, our re‐optimization model generates more desirable PTV dose distributions. Conclusions: We have presented a re‐optimization model that, by incorporating spatial location information for PTV voxels, yields to more clinically favorable dose distributions with similar DVH curves. This work is supported by Varian Medical Systems through a Master Research Agreement.",
author = "A. Uribe‐sanchez and M. Zarepisheh and X. Jia and S. Jiang",
year = "2012",
doi = "10.1118/1.4736002",
language = "English (US)",
volume = "39",
pages = "3920--3921",
journal = "Medical Physics",
issn = "0094-2405",
publisher = "AAPM - American Association of Physicists in Medicine",
number = "6",

}

TY - JOUR

T1 - TU‐G‐BRB‐07

T2 - Improve PTV Dose Distribution by Using Spatial Information in IMRT Optimization

AU - Uribe‐sanchez, A.

AU - Zarepisheh, M.

AU - Jia, X.

AU - Jiang, S.

PY - 2012

Y1 - 2012

N2 - Purpose: The spatial distribution of radiation dose within the PTV does have clinical significance. For instance, if hot/cold spots in the PTV cannot be avoided, high doses are preferred to be located in the center, and low doses at the peripheries. However, traditional plan optimization models for IMRT usually treat equally voxels inside the same structure, failing to incorporate those location preferences. We present a re‐optimization model that, while preserving the quality of an initial treatment plan represented by DVH curves, incorporates spatial information for voxels inside the PTV into the optimization to generate a more desirable dose distribution. Methods: Our re‐optimization model incorporates a convex function that penalizes the deviation of the dose received by each voxel from an individual reference value. For PTV, the reference values per voxel match the ideal redistribution of the initial PTV dose, where voxels close to the boundary receive the low doses, while voxels in the center receive the high doses. For OAR, the reference value per voxel corresponds to its dose from the initial plan. In addition to the structure‐based weighting factors in traditional planning approaches, we incorporated individual penalty weights for PTV voxels. Structure‐based factors are calibrated according to the difference from the reference DVH curves, while voxel‐based, according to the difference to reference value. Results: We tested our model in four gynecologic cancer cases. For each case, we compare the resulting dose distribution within the PTV to that from the initial plan. It is observed that without sacrificing the plan quality represented by DVH curves, our re‐optimization model generates more desirable PTV dose distributions. Conclusions: We have presented a re‐optimization model that, by incorporating spatial location information for PTV voxels, yields to more clinically favorable dose distributions with similar DVH curves. This work is supported by Varian Medical Systems through a Master Research Agreement.

AB - Purpose: The spatial distribution of radiation dose within the PTV does have clinical significance. For instance, if hot/cold spots in the PTV cannot be avoided, high doses are preferred to be located in the center, and low doses at the peripheries. However, traditional plan optimization models for IMRT usually treat equally voxels inside the same structure, failing to incorporate those location preferences. We present a re‐optimization model that, while preserving the quality of an initial treatment plan represented by DVH curves, incorporates spatial information for voxels inside the PTV into the optimization to generate a more desirable dose distribution. Methods: Our re‐optimization model incorporates a convex function that penalizes the deviation of the dose received by each voxel from an individual reference value. For PTV, the reference values per voxel match the ideal redistribution of the initial PTV dose, where voxels close to the boundary receive the low doses, while voxels in the center receive the high doses. For OAR, the reference value per voxel corresponds to its dose from the initial plan. In addition to the structure‐based weighting factors in traditional planning approaches, we incorporated individual penalty weights for PTV voxels. Structure‐based factors are calibrated according to the difference from the reference DVH curves, while voxel‐based, according to the difference to reference value. Results: We tested our model in four gynecologic cancer cases. For each case, we compare the resulting dose distribution within the PTV to that from the initial plan. It is observed that without sacrificing the plan quality represented by DVH curves, our re‐optimization model generates more desirable PTV dose distributions. Conclusions: We have presented a re‐optimization model that, by incorporating spatial location information for PTV voxels, yields to more clinically favorable dose distributions with similar DVH curves. This work is supported by Varian Medical Systems through a Master Research Agreement.

UR - http://www.scopus.com/inward/record.url?scp=84890416041&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84890416041&partnerID=8YFLogxK

U2 - 10.1118/1.4736002

DO - 10.1118/1.4736002

M3 - Article

AN - SCOPUS:84890416041

VL - 39

SP - 3920

EP - 3921

JO - Medical Physics

JF - Medical Physics

SN - 0094-2405

IS - 6

ER -