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Optimization of radiotherapy conside...

Sir, Mustafa Yasar.

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Optimization of radiotherapy considering uncertainties caused by daily setup procedures and organ motion.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Optimization of radiotherapy considering uncertainties caused by daily setup procedures and organ motion./
Author:	Sir, Mustafa Yasar.
Description:	206 p.
Notes:	Advisers: Marina A. Epelman; Stephen M. Pollock.
Contained By:	Dissertation Abstracts International68-10B.
Subject:	Engineering, Industrial. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3287634
ISBN:	9780549304852

Optimization of radiotherapy considering uncertainties caused by daily setup procedures and organ motion.
Sir, Mustafa Yasar.

Optimization of radiotherapy considering uncertainties caused by daily setup procedures and organ motion. - 206 p.

Advisers: Marina A. Epelman; Stephen M. Pollock.

Thesis (Ph.D.)--University of Michigan, 2007.

Computational experiments show that these individualized adaptive radiotherapy plans promise considerable improvements over non-adaptive treatment plans.

ISBN: 9780549304852Subjects--Topical Terms:

626639
Engineering, Industrial.

Optimization of radiotherapy considering uncertainties caused by daily setup procedures and organ motion.
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020 $a 9780549304852
035 $a (UMI)AAI3287634
035 $a AAI3287634
040 $a UMI $c UMI
100 1 $a Sir, Mustafa Yasar. $3 1270489
245 1 0 $a Optimization of radiotherapy considering uncertainties caused by daily setup procedures and organ motion.
300 $a 206 p.
500 $a Advisers: Marina A. Epelman; Stephen M. Pollock.
500 $a Source: Dissertation Abstracts International, Volume: 68-10, Section: B, page: 6889.
502 $a Thesis (Ph.D.)--University of Michigan, 2007.
520 $a Computational experiments show that these individualized adaptive radiotherapy plans promise considerable improvements over non-adaptive treatment plans.
520 $a These policies only differ in their re-optimization schemes, of which there are three major variants: certainty equivalent control, open-loop feedback control, and cost-to-go approximation via Lagrangian relaxation.
520 $a Most existing intensity-modulated radiation therapy (IMRT) optimization methods assume that the shape and location of the patient anatomy do not change throughout the treatment course and typically produce solutions containing sharp dose gradients between a tumor and its neighboring healthy tissue. Uncertainties caused by the daily setup procedures and organ motion, however, can lead to significant differences between the dose distribution as calculated by a treatment plan and the actual dose distribution delivered to a patient.
520 $a Using weighted power loss functions as a measure of performance for a treatment plan, a simple method of calculating the ideal spatial dose distribution when there is positional uncertainty is presented. Immediate results are qualitative insights into the suitability of using a margin to compensate for uncertainties at all, and (if one is to be used) how to select a "good" margin size. Moreover, it is shown that the common practice of raising power parameters in the treatment loss function, in order to enforce target dose requirements, is counter-productive. These insights into desirable dose distributions could be used, in conjunction with well-established inverse radiotherapy planning techniques, to produce dose distributions that are more adept at dealing with uncertainties.
520 $a Furthermore, techniques that exploit the dynamic nature of radiotherapy and information gathering by adapting the treatment plan to variations measured during the treatment course of an individual patient are developed. This problem, called off-line adaptive treatment planning, is structured within a dynamic programming (DP) framework. Several (suboptimal) control policies, which are obtained using an approximation to the underlying DP and computationally feasible to implement, are presented and compared. The common properties shared by these policies are that they all (1) perform a re-optimization of beamlet intensities before each fraction, (2) use Bayesian updating of the individual patient's setup variation parameters, (3) use "Multiple Instance of Geometry Approximation" as the model of uncertainty.
590 $a School code: 0127.
650 4 $a Engineering, Industrial. $3 626639
650 4 $a Health Sciences, Radiology. $3 1019076
690 $a 0546
690 $a 0574
710 2 $a University of Michigan. $3 777416
773 0 $t Dissertation Abstracts International $g 68-10B.
790 $a 0127
790 1 0 $a Epelman, Marina A., $e advisor
790 1 0 $a Pollock, Stephen M., $e advisor
791 $a Ph.D.
792 $a 2007
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3287634