TY - JOUR
T1 - A method for generating large datasets of organ geometries for radiotherapy treatment planning studies
AU - Hu, Nan
AU - Cerviño, Laura
AU - Segars, Paul
AU - Lewis, John
AU - Shan, Jinlu
AU - Jiang, Steve
AU - Zheng, Xiaolin
AU - Wang, Ge
N1 - Funding Information:
This research is partly supported by Chinese Scholarship Council.
PY - 2014/3/1
Y1 - 2014/3/1
N2 - 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.
AB - 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.
KW - Adaptive radiotherapy
KW - New geometries
KW - Non-uniform rational B-spline technique
KW - Statistical shape model
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U2 - 10.2478/raon-2014-0003
DO - 10.2478/raon-2014-0003
M3 - Article
C2 - 25435856
AN - SCOPUS:84990841006
SN - 1318-2099
VL - 48
SP - 408
EP - 415
JO - Radiology and Oncology
JF - Radiology and Oncology
IS - 4
ER -