<abstract xmlns="http://www.w3.org/1999/xhtml"> 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</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

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

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

Department of Radiation Oncology, University of California, San Diego, USA

Department of Radiology, Duke University, USA

College of Bioengineering, Chongqing University, China 4 Department of Radiology, Duke University, USA

Department of Radiation Oncology, Cancer Center,Research Institute of Surgery, Daping Hospital, Third Military Medical University, China

Radiology and Oncology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Background. With the rapidly increasing application of adaptive radiotherapy, large datasets of organ geometries based on the patient’s anatomy are desired to...

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

Published Online: Nov 05, 2014

Page range: 408 - 415

Received: Jul 19, 2013

Accepted: Oct 11, 2013

DOI: https://doi.org/10.2478/raon-2014-0003

Keywords
non-uniform rational B-spline technique, new geometries, statistical shape model, adaptive radiotherapy

© by Xiaolin Zheng

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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

Published Online: Nov 05, 2014

Page range: 408 - 415

Received: Jul 19, 2013

Accepted: Oct 11, 2013

DOI: https://doi.org/10.2478/raon-2014-0003

Keywordsnon-uniform rational B-spline technique, new geometries, statistical shape model, adaptive radiotherapy

© by Xiaolin Zheng

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
non-uniform rational B-spline technique, new geometries, statistical shape model, adaptive radiotherapy