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Gago-Arias A, Aguiar P, Espinoza I, Sánchez-Nieto B, Pardo-Montero J. Modelling radiation-induced cell death and tumour re-oxygenation: local versus global and instant versus delayed cell death. Phys Med Biol 2016; 61:1204-16. [DOI: 10.1088/0031-9155/61/3/1204] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
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Yu ZH, Kudchadker R, Dong L, Zhang Y, Court LE, Mourtada F, Yock A, Tucker SL, Yang J. Learning anatomy changes from patient populations to create artificial CT images for voxel-level validation of deformable image registration. J Appl Clin Med Phys 2016; 17:246-258. [PMID: 26894362 PMCID: PMC5690226 DOI: 10.1120/jacmp.v17i1.5888] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2015] [Revised: 09/21/2015] [Accepted: 09/16/2015] [Indexed: 12/20/2022] Open
Abstract
The purpose of this study was to develop an approach to generate artificial computed tomography (CT) images with known deformation by learning the anatomy changes in a patient population for voxel‐level validation of deformable image registration. Using a dataset of CT images representing anatomy changes during the course of radiation therapy, we selected a reference image and registered the remaining images to it, either directly or indirectly, using deformable registration. The resulting deformation vector fields (DVFs) represented the anatomy variations in that patient population. The mean deformation, computed from the DVFs, and the most prominent variations, which were captured using principal component analysis (PCA), composed an active shape model that could generate random known deformations with realistic anatomy changes based on those learned from the patient population. This approach was applied to a set of 12 head and neck patients who received intensity‐modulated radiation therapy for validation. Artificial planning CT and daily CT images were generated to simulate a patient with known anatomy changes over the course of treatment and used to validate the deformable image registration between them. These artificial CT images potentially simulated the actual patients' anatomies and also showed realistic anatomy changes between different daily CT images. They were used to successfully validate deformable image registration applied to intrapatient deformation. PACS number: 87.57.nj
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Affiliation(s)
- Z Henry Yu
- The University of Texas MD Anderson Cancer Center; Christiana Health Care Systems.
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Li H, Zhang X, Park P, Liu W, Chang J, Liao Z, Frank S, Li Y, Poenisch F, Mohan R, Gillin M, Zhu R. Robust optimization in intensity-modulated proton therapy to account for anatomy changes in lung cancer patients. Radiother Oncol 2015; 114:367-72. [PMID: 25708992 DOI: 10.1016/j.radonc.2015.01.017] [Citation(s) in RCA: 63] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2014] [Revised: 12/26/2014] [Accepted: 01/06/2015] [Indexed: 12/21/2022]
Abstract
BACKGROUND AND PURPOSE Robust optimization for IMPT takes setup and range uncertainties into account during plan optimization. However, anatomical changes were not prospectively included. The purpose of this study was to examine robustness and dose variation due to setup uncertainty and anatomical change in IMPT of lung cancer. MATERIAL AND METHODS Plans were generated with multi-field optimization based on planning target volume (MFO-PTV) and worst-case robust optimization (MFO-RO) on simulation computed tomography scans (CT0) for nine patients. Robustness was evaluated on the CT0 by computing the standard deviation of DVH (SD-DVH). Dose variations calculated on weekly CTs were compared with SD-DVH. Equivalent uniform dose (EUD) change from the original plan on weekly dose was also calculated for both plans. RESULTS SD-DVH and dose variation on weekly CTs were both significantly lower in the MFO-RO plans than in the MFO-PTV plans for targets, lungs, and the esophagus (p<0.05). When comparing EUD for ITV between weekly and planned dose distributions, three patients and 28% of repeated CTs for MFO-RO plans, and six patients and 44% of repeated CTs for MFO-PTV plans, respectively, showed an EUD change of >5%. CONCLUSIONS RO in IMPT reduces the dose variation due to setup uncertainty and anatomy changes during treatment compared with PTV-based planning. However, dose variation could still be substantial; repeated imaging and adaptive planning as needed are highly recommended for IMPT of lung tumors.
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Affiliation(s)
- Heng Li
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
| | - Xiaodong Zhang
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Peter Park
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Wei Liu
- Department of Radiation Oncology, Mayo Clinic, Scottsdale, AZ, USA
| | - Joe Chang
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Zhongxing Liao
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Steve Frank
- Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Yupeng Li
- Applied Research, Varian Medical Systems, Palo Alto, CA, USA
| | - Falk Poenisch
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Radhe Mohan
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Michael Gillin
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
| | - Ronald Zhu
- Department of Radiation Physics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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