A method for generating large datasets of organ geometries for radiotherapy treatment planning studies
Identifieur interne : 001490 ( Main/Merge ); précédent : 001489; suivant : 001491A method for generating large datasets of organ geometries for radiotherapy treatment planning studies
Auteurs : Nan Hu [République populaire de Chine, États-Unis] ; Laura Cervi O [États-Unis] ; Paul Segars [États-Unis] ; John Lewis [États-Unis] ; Jinlu Shan [États-Unis] ; Steve Jiang [États-Unis] ; Xiaolin Zheng [République populaire de Chine] ; Ge Wang [République populaire de Chine]Source :
- Radiology and Oncology [ 1318-2099 ] ; 2014.
Abstract
With the rapidly increasing application of adaptive radiotherapy, large datasets of organ geometries based on the patient’s anatomy are desired to support clinical application or research work, such as image segmentation, re-planning, and organ deformation analysis. Sometimes only limited datasets are available in clinical practice. In this study, we propose a new method to generate large datasets of organ geometries to be utilized in adaptive radiotherapy.
Given a training dataset of organ shapes derived from daily cone-beam CT, we align them into a common coordinate frame and select one of the training surfaces as reference surface. A statistical shape model of organs was constructed, based on the establishment of point correspondence between surfaces and non-uniform rational B-spline (NURBS) representation. A principal component analysis is performed on the sampled surface points to capture the major variation modes of each organ.
A set of principal components and their respective coefficients, which represent organ surface deformation, were obtained, and a statistical analysis of the coefficients was performed. New sets of statistically equivalent coefficients can be constructed and assigned to the principal components, resulting in a larger geometry dataset for the patient’s organs.
These generated organ geometries are realistic and statistically representative.
Url:
DOI: 10.2478/raon-2014-0003
PubMed: 25435856
PubMed Central: 4230563
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<author><name sortKey="Hu, Nan" sort="Hu, Nan" uniqKey="Hu N" first="Nan" last="Hu">Nan Hu</name>
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<series><title level="j">Radiology and Oncology</title>
<idno type="ISSN">1318-2099</idno>
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<front><div type="abstract" xml:lang="en"><sec><title>Background</title>
<p>With the rapidly increasing application of adaptive radiotherapy, large datasets of organ geometries based on the patient’s anatomy are desired to support clinical application or research work, such as image segmentation, re-planning, and organ deformation analysis. Sometimes only limited datasets are available in clinical practice. In this study, we propose a new method to generate large datasets of organ geometries to be utilized in adaptive radiotherapy.</p>
</sec>
<sec><title>Methods</title>
<p>Given a training dataset of organ shapes derived from daily cone-beam CT, we align them into a common coordinate frame and select one of the training surfaces as reference surface. A statistical shape model of organs was constructed, based on the establishment of point correspondence between surfaces and non-uniform rational B-spline (NURBS) representation. A principal component analysis is performed on the sampled surface points to capture the major variation modes of each organ.</p>
</sec>
<sec><title>Results</title>
<p>A set of principal components and their respective coefficients, which represent organ surface deformation, were obtained, and a statistical analysis of the coefficients was performed. New sets of statistically equivalent coefficients can be constructed and assigned to the principal components, resulting in a larger geometry dataset for the patient’s organs.</p>
</sec>
<sec><title>Conclusions</title>
<p>These generated organ geometries are realistic and statistically representative.</p>
</sec>
</div>
</front>
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</back>
</TEI>
</record>
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