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A method for generating large datasets of organ geometries for radiotherapy treatment planning studies.

Hu N, Cerviño L, Segars P, Lewis J, Shan J, Jiang S, Zheng X, Wang G - Radiol Oncol (2014)

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology, Cancer Center,Research Institute of Surgery, Daping Hospital, Third Military Medical University, China ; Department of Radiation Oncology, University of California, San Diego, USA ; College of Bioengineering, Chongqing University, China.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: These generated organ geometries are realistic and statistically representative.

No MeSH data available.


Probability density function (PDF) approximation of coefficient and random coefficients generation. (A) PDF of coefficient; (B) Random generated coefficient.
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Related In: Results  -  Collection

License 1 - License 2
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f8-rado-48-04-408: Probability density function (PDF) approximation of coefficient and random coefficients generation. (A) PDF of coefficient; (B) Random generated coefficient.

Mentions: Figure 8 illustrates the estimation and the corresponding approximation of the PDF of one of the coefficients bq, which contains a mixture of two gaussian distributions. Random coefficients were generated to match this kind of mixture-gaussian distribution by Monte-Carlo sampling.


A method for generating large datasets of organ geometries for radiotherapy treatment planning studies.

Hu N, Cerviño L, Segars P, Lewis J, Shan J, Jiang S, Zheng X, Wang G - Radiol Oncol (2014)

Probability density function (PDF) approximation of coefficient and random coefficients generation. (A) PDF of coefficient; (B) Random generated coefficient.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4230563&req=5

f8-rado-48-04-408: Probability density function (PDF) approximation of coefficient and random coefficients generation. (A) PDF of coefficient; (B) Random generated coefficient.
Mentions: Figure 8 illustrates the estimation and the corresponding approximation of the PDF of one of the coefficients bq, which contains a mixture of two gaussian distributions. Random coefficients were generated to match this kind of mixture-gaussian distribution by Monte-Carlo sampling.

Bottom Line: 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.

View Article: PubMed Central - PubMed

Affiliation: Department of Radiation Oncology, Cancer Center,Research Institute of Surgery, Daping Hospital, Third Military Medical University, China ; Department of Radiation Oncology, University of California, San Diego, USA ; College of Bioengineering, Chongqing University, China.

ABSTRACT

Background: 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.

Methods: 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.

Results: 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.

Conclusions: These generated organ geometries are realistic and statistically representative.

No MeSH data available.