Lung 4D-IMRT treatment planning

An evaluation of three methods applied to four-dimensional data sets

Eric D. Ehler, Wolfgang A. Tome

Research output: Contribution to journalArticle

24 Citations (Scopus)

Abstract

Purpose: To compare 4D-dose distributions for IMRT planning on three data sets: a single 4D-CT phase, a 4D-CT phase with a density override to the tumor motion envelope (TME) volume, and the average intensity projection (AIP). Methods: Eight planning cases were considered. IMRT inverse planning optimization was performed on each of the three data set types, for each case considered. The plans were then applied to all ten phases of the associated 4D-CT data set. The dose to the GTV in each breathing phase was compared to the TME dose from the optimized dose distribution, as well as the GTV dose determined from a model-based deformable registration algorithm. Results: IMRT optimization on a single 3D data set resulted in a greater equivalent uniform dose (EUD) to the GTV when applied to a 4D-CT data set than the EUD for the TME in the optimized plan. The difference was up to 5.5 Gy in one case. For all cases and planning techniques considered, a maximum difference of 0.3 Gy in the NTDmean to the healthy lung throughout the breathing cycle was found. Conclusions: For tumors located in the periphery of the lung, optimization on the AIP image resulted in a more uniform GTV dose throughout the breathing cycle. Averages in GTV EUD and healthy lung NTDmean taken over all the breathing phases were found to be in agreement with the dose effect parameters obtained from model-based deformable registration algorithms. All planning methods yielded GTV EUD values that were larger than the prescribed dose when the full 4D data set was considered.

Original languageEnglish (US)
Pages (from-to)319-325
Number of pages7
JournalRadiotherapy and Oncology
Volume88
Issue number3
DOIs
StatePublished - Sep 2008
Externally publishedYes

Fingerprint

Four-Dimensional Computed Tomography
Lung
Respiration
Neoplasms
Planning Techniques
Datasets

Keywords

  • 4D IMRT
  • 4D-CT
  • Average intensity projection
  • Intrafraction motion
  • Tumor motion

ASJC Scopus subject areas

  • Oncology
  • Radiology Nuclear Medicine and imaging
  • Urology

Cite this

Lung 4D-IMRT treatment planning : An evaluation of three methods applied to four-dimensional data sets. / Ehler, Eric D.; Tome, Wolfgang A.

In: Radiotherapy and Oncology, Vol. 88, No. 3, 09.2008, p. 319-325.

Research output: Contribution to journalArticle

@article{f7fecb7b88214e0583e14a1d3ebfdcb3,
title = "Lung 4D-IMRT treatment planning: An evaluation of three methods applied to four-dimensional data sets",
abstract = "Purpose: To compare 4D-dose distributions for IMRT planning on three data sets: a single 4D-CT phase, a 4D-CT phase with a density override to the tumor motion envelope (TME) volume, and the average intensity projection (AIP). Methods: Eight planning cases were considered. IMRT inverse planning optimization was performed on each of the three data set types, for each case considered. The plans were then applied to all ten phases of the associated 4D-CT data set. The dose to the GTV in each breathing phase was compared to the TME dose from the optimized dose distribution, as well as the GTV dose determined from a model-based deformable registration algorithm. Results: IMRT optimization on a single 3D data set resulted in a greater equivalent uniform dose (EUD) to the GTV when applied to a 4D-CT data set than the EUD for the TME in the optimized plan. The difference was up to 5.5 Gy in one case. For all cases and planning techniques considered, a maximum difference of 0.3 Gy in the NTDmean to the healthy lung throughout the breathing cycle was found. Conclusions: For tumors located in the periphery of the lung, optimization on the AIP image resulted in a more uniform GTV dose throughout the breathing cycle. Averages in GTV EUD and healthy lung NTDmean taken over all the breathing phases were found to be in agreement with the dose effect parameters obtained from model-based deformable registration algorithms. All planning methods yielded GTV EUD values that were larger than the prescribed dose when the full 4D data set was considered.",
keywords = "4D IMRT, 4D-CT, Average intensity projection, Intrafraction motion, Tumor motion",
author = "Ehler, {Eric D.} and Tome, {Wolfgang A.}",
year = "2008",
month = "9",
doi = "10.1016/j.radonc.2008.07.004",
language = "English (US)",
volume = "88",
pages = "319--325",
journal = "Radiotherapy and Oncology",
issn = "0167-8140",
publisher = "Elsevier Ireland Ltd",
number = "3",

}

TY - JOUR

T1 - Lung 4D-IMRT treatment planning

T2 - An evaluation of three methods applied to four-dimensional data sets

AU - Ehler, Eric D.

AU - Tome, Wolfgang A.

PY - 2008/9

Y1 - 2008/9

N2 - Purpose: To compare 4D-dose distributions for IMRT planning on three data sets: a single 4D-CT phase, a 4D-CT phase with a density override to the tumor motion envelope (TME) volume, and the average intensity projection (AIP). Methods: Eight planning cases were considered. IMRT inverse planning optimization was performed on each of the three data set types, for each case considered. The plans were then applied to all ten phases of the associated 4D-CT data set. The dose to the GTV in each breathing phase was compared to the TME dose from the optimized dose distribution, as well as the GTV dose determined from a model-based deformable registration algorithm. Results: IMRT optimization on a single 3D data set resulted in a greater equivalent uniform dose (EUD) to the GTV when applied to a 4D-CT data set than the EUD for the TME in the optimized plan. The difference was up to 5.5 Gy in one case. For all cases and planning techniques considered, a maximum difference of 0.3 Gy in the NTDmean to the healthy lung throughout the breathing cycle was found. Conclusions: For tumors located in the periphery of the lung, optimization on the AIP image resulted in a more uniform GTV dose throughout the breathing cycle. Averages in GTV EUD and healthy lung NTDmean taken over all the breathing phases were found to be in agreement with the dose effect parameters obtained from model-based deformable registration algorithms. All planning methods yielded GTV EUD values that were larger than the prescribed dose when the full 4D data set was considered.

AB - Purpose: To compare 4D-dose distributions for IMRT planning on three data sets: a single 4D-CT phase, a 4D-CT phase with a density override to the tumor motion envelope (TME) volume, and the average intensity projection (AIP). Methods: Eight planning cases were considered. IMRT inverse planning optimization was performed on each of the three data set types, for each case considered. The plans were then applied to all ten phases of the associated 4D-CT data set. The dose to the GTV in each breathing phase was compared to the TME dose from the optimized dose distribution, as well as the GTV dose determined from a model-based deformable registration algorithm. Results: IMRT optimization on a single 3D data set resulted in a greater equivalent uniform dose (EUD) to the GTV when applied to a 4D-CT data set than the EUD for the TME in the optimized plan. The difference was up to 5.5 Gy in one case. For all cases and planning techniques considered, a maximum difference of 0.3 Gy in the NTDmean to the healthy lung throughout the breathing cycle was found. Conclusions: For tumors located in the periphery of the lung, optimization on the AIP image resulted in a more uniform GTV dose throughout the breathing cycle. Averages in GTV EUD and healthy lung NTDmean taken over all the breathing phases were found to be in agreement with the dose effect parameters obtained from model-based deformable registration algorithms. All planning methods yielded GTV EUD values that were larger than the prescribed dose when the full 4D data set was considered.

KW - 4D IMRT

KW - 4D-CT

KW - Average intensity projection

KW - Intrafraction motion

KW - Tumor motion

UR - http://www.scopus.com/inward/record.url?scp=52049095720&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=52049095720&partnerID=8YFLogxK

U2 - 10.1016/j.radonc.2008.07.004

DO - 10.1016/j.radonc.2008.07.004

M3 - Article

VL - 88

SP - 319

EP - 325

JO - Radiotherapy and Oncology

JF - Radiotherapy and Oncology

SN - 0167-8140

IS - 3

ER -