Source:http://linkedlifedata.com/resource/pubmed/id/10533926
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:issue |
10
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| pubmed:dateCreated |
1999-11-30
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| pubmed:abstractText |
Inverse treatment planning starts with a treatment objective and obtains the solution by optimizing an objective function. The clinical objectives are usually multifaceted and potentially incompatible with one another. A set of importance factors is often incorporated in the objective function to parametrize trade-off strategies and to prioritize the dose conformality in different anatomical structures. Whereas the general formalism remains the same, different sets of importance factors characterize plans of obviously different flavour and thus critically determine the final plan. Up to now, the determination of these parameters has been a 'guessing' game based on empirical knowledge because the final dose distribution depends on the parameters in a complex and implicit way. The influence of these parameters is not known until the plan optimization is completed. In order to compromise properly the conflicting requirements of the target and sensitive structures, the parameters are usually adjusted through a trial-and-error process. In this paper, a method to estimate these parameters computationally is proposed and an iterative computer algorithm is described to determine these parameters numerically. The treatment plan selection is done in two steps. First, a set of importance factors are chosen and the corresponding beam parameters (e.g. beam profiles) are optimized under the guidance of a quadratic objective function using an iterative algorithm reported earlier. The 'optimal' plan is then evaluated by an additional scoring function. The importance factors in the objective function are accordingly adjusted to improve the ranking of the plan. For every change in the importance factors, the beam parameters need to be re-optimized. This process continues in an iterative fashion until the scoring function is saturated. The algorithm was applied to two clinical cases and the results demonstrated that it has the potential to improve significantly the existing method of inverse planning. It was noticed that near the final solution the plan became insensitive to small variations of the importance factors.
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| pubmed:grant | |
| pubmed:language |
eng
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| pubmed:journal | |
| pubmed:citationSubset |
IM
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| pubmed:status |
MEDLINE
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| pubmed:month |
Oct
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| pubmed:issn |
0031-9155
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| pubmed:author | |
| pubmed:issnType |
Print
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| pubmed:volume |
44
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| pubmed:owner |
NLM
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| pubmed:authorsComplete |
Y
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| pubmed:pagination |
2525-36
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| pubmed:dateRevised |
2007-11-14
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| pubmed:meshHeading |
pubmed-meshheading:10533926-Algorithms,
pubmed-meshheading:10533926-Eye Neoplasms,
pubmed-meshheading:10533926-Humans,
pubmed-meshheading:10533926-Lung,
pubmed-meshheading:10533926-Lymphoma,
pubmed-meshheading:10533926-Pleural Neoplasms,
pubmed-meshheading:10533926-Radiotherapy, Conformal,
pubmed-meshheading:10533926-Radiotherapy Planning, Computer-Assisted,
pubmed-meshheading:10533926-Spinal Cord
|
| pubmed:year |
1999
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| pubmed:articleTitle |
Optimization of importance factors in inverse planning.
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| pubmed:affiliation |
Department of Radiation Oncology, Stanford University School of Medicine, CA 94305-5304, USA. lei@reyes.stanford.edu
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| pubmed:publicationType |
Journal Article,
Research Support, U.S. Gov't, P.H.S.,
Research Support, Non-U.S. Gov't
|