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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.


Spectra of relative eigenvalues for training datasets (sum of all eigenvalues normalized to 100%).
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f7-rado-48-04-408: Spectra of relative eigenvalues for training datasets (sum of all eigenvalues normalized to 100%).

Mentions: PCA was performed on the sampled surface points to capture the major variation modes of the surfaces for the same organ. For the pelvic organs in our study, shape variations have shown to be clearly dominated by only a few eigenvalues, indicating that the geometric variability of the measured organ samples is concentrated in just a few deformation modes. From the statistical shape modeling of pelvic organ, we described the shape variability in the training sets by the first five principal modes, which covered > 90% of the variance in shape change found within the training sets. Figure 7 illustrates the spectra of eigen values of a training dataset.


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)

Spectra of relative eigenvalues for training datasets (sum of all eigenvalues normalized to 100%).
© Copyright Policy - open-access
Related In: Results  -  Collection

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

f7-rado-48-04-408: Spectra of relative eigenvalues for training datasets (sum of all eigenvalues normalized to 100%).
Mentions: PCA was performed on the sampled surface points to capture the major variation modes of the surfaces for the same organ. For the pelvic organs in our study, shape variations have shown to be clearly dominated by only a few eigenvalues, indicating that the geometric variability of the measured organ samples is concentrated in just a few deformation modes. From the statistical shape modeling of pelvic organ, we described the shape variability in the training sets by the first five principal modes, which covered > 90% of the variance in shape change found within the training sets. Figure 7 illustrates the spectra of eigen values of a training dataset.

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.