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Dose errors in the treatment plannin...
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Carleton University (Canada).
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Dose errors in the treatment planning process of cancer radiotherapy.
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Dose errors in the treatment planning process of cancer radiotherapy./
作者:
Myint, W. Kenji.
面頁冊數:
199 p.
附註:
Source: Dissertation Abstracts International, Volume: 69-06, Section: B, page: 3639.
Contained By:
Dissertation Abstracts International69-06B.
標題:
Biophysics, Medical. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=NR40529
ISBN:
9780494405291
Dose errors in the treatment planning process of cancer radiotherapy.
Myint, W. Kenji.
Dose errors in the treatment planning process of cancer radiotherapy.
- 199 p.
Source: Dissertation Abstracts International, Volume: 69-06, Section: B, page: 3639.
Thesis (Ph.D.)--Carleton University (Canada), 2008.
This thesis reports on the examination of specific dose errors in the treatment planning process. This process begins with the acquisition of the treatment planning CT (computed tomography) dataset and ends with the calculation of dose in the patient. The treatment planning CT is a Hounsfield unit (HU) representation of the patient that is converted to relative electron density in the treatment planning system. The treatment planning system utilizes a dose calculation algorithm to predict the dose based on the relative electron density distribution of the patient. The sources of dose error investigated in this thesis can be categorized as: (i) errors in the HU representation of the patient; (ii) errors in the relative electron density distribution of the patient; and (iii) errors in the dose calculation algorithm.
ISBN: 9780494405291Subjects--Topical Terms:
1017681
Biophysics, Medical.
Dose errors in the treatment planning process of cancer radiotherapy.
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Errors in the dose calculation algorithms were examined in Chapter 3, where the accuracy of the Theraplan Plus treatment planning system's implementation of the pencil beam and collapsed cone convolution algorithms were investigated in lung-equivalent material. Both algorithms had difficulty modeling the broadening of the beam in the lung-equivalent material but the collapsed cone convolution algorithm generally showed consistently smaller dose errors than the pencil beam algorithm. As expected, the pencil beam model could not model any lateral electron transport and the largest dose errors were observed near lateral lung-acrylic interfaces. In chapter 4, objects present during dose delivery but not accounted for in the treatment planning CT dataset were investigated. These can be categorized as errors in the HU representation of the patient. One such example is the treatment tabletop present during delivery, but replaced with a different table during the CT scan. In this study, the attenuation of the beam by a carbon fiber treatment tabletop was quantified and a practical solution to account for the tabletop was proposed. It was determined that neglecting the attenuation of oblique treatment fields by the carbon fiber couch can result in localized dose reduction from 4% to 16% depending on energy, field size, and geometry. To remedy this attenuation, the results showed that incorporating the carbon fiber couch in the TPS patient model reduces the dose error to less than 2% (using Theraplan Plus v3.8). In chapter 5, dose errors in the presence of high-Z prosthetic devices were reported, representing errors in both the HU and electron density distribution of the patient. This study also investigated the reduction in dose error from using treatment planning calculations based on MVCT images rather than conventional kVCT images. The MVCT-based calculations showed consistently smaller dose errors than kVCT-based calculations for both pencil beam and superposition algorithms. The reconstruction artifacts were greatly reduced with the use of MVCT, giving a much more accurate HU representation of the patient. MVCT images also improved the reliability of the electron density map.
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