Intensity Modulated Radiation Therapy (IMRT) has recently emerged as an effective clinical treatment tool to treat various types of cancers by limiting the external beam dose to the surrounding normal tissue. However, the process of limiting external radiation dose to the tissue surrounding the tumor volume is not a trivial task. Several parameters including tumor volume and inhomogeneity, position and shape of the tumor volume, and the geometrical distribution of the radiation beams directly affect the determination of the external radiation dose. In addition, a major variable in effective delivery of the radiation dose is "set-up error" caused by the changes in patient position. Any changes in the position of the patient affect the geometrical location of the tumor volume and, therefore, need to be accommodated in the delivery of radiation beams during the treatment. This work presents a complete matrix representation required to calculate the three-dimensional rigid body homogeneous transformation matrices corresponding to external beam radiotherapy setup error and subsequent corrections in treatment beam parameters. A new concise orthogonal rotation solution is presented for use with clinical noisy data. Monte Carlo simulations prove the new matrix results are consistently better than the standard inverse solution. The required corrections in beam table, gantry, and collimator angles as function of the planned beam gantry angle are derived. For transformations that include a rotation on the sagittal plane, the required offsets to beam parameters are complex functions of the planned gantry angle but are clearly documented graphically for clinical use. A case study is presented with an error analysis that supports the use of the presented method in a clinical environment. Clinical implementation and evaluation of the presented method with patient data is also included in the paper.
An iterative algorithm has been developed to analytically determine patient specific input parameters for intensity‐modulated radiotherapy prostate treatment planning. The algorithm starts with a generic set of inverse planning parameters that include dose and volume constraints for the target and surrounding critical structures. The overlap region between the target volume and the rectum is used to determine the optimized target volume coverage goal. Sequential iterations are performed to vary the numerous parameters individually or in sets while other parameters remain fixed. A coarse grid search is first used to avoid convergence on a local maximum. Linear interpolation is then used to define a region for a fine grid search. Selected parameters are also tested for possible improvements in target coverage. In several representative test cases investigated the coverage of the planning target volume improved with the use of the algorithm while still meeting the clinical acceptability criteria for critical structures. The algorithm avoids time‐consuming random trial and error variations that are often associated with difficult cases and also eliminates lengthy user learning curves. The methodology described in this paper can be applied to any treatment planning system that requires the user to select the input optimization parameters.PACS number(s): 87.53.–j, 87.90.+y
An iterative algorithm has been developed to analytically determine patient specific input parameters for intensity‐modulated radiotherapy prostate treatment planning. The algorithm starts with a generic set of inverse planning parameters that include dose and volume constraints for the target and surrounding critical structures. The overlap region between the target volume and the rectum is used to determine the optimized target volume coverage goal. Sequential iterations are performed to vary the numerous parameters individually or in sets while other parameters remain fixed. A coarse grid search is first used to avoid convergence on a local maximum. Linear interpolation is then used to define a region for a fine grid search. Selected parameters are also tested for possible improvements in target coverage. In several representative test cases investigated the coverage of the planning target volume improved with the use of the algorithm while still meeting the clinical acceptability criteria for critical structures. The algorithm avoids time‐consuming random trial and error variations that are often associated with difficult cases and also eliminates lengthy user learning curves. The methodology described in this paper can be applied to any treatment planning system that requires the user to select the input optimization parameters.PACS number(s): 87.53.–j, 87.90.+y
Image guided radiotherapy can be performed by fusing the daily treatment and reference planning computed tomography scans. Decreased errors in patient setup can lead to smaller target margins that significantly improve treatment efficacy and outcome. The purpose of this work is to present a clinical procedure to analytically compute the accuracy of the registration. Accepted techniques such as normalized mutual information intensity based three-dimensional image registration can be validated using a large automated point sample. Without a user independent metric it is not possible to determine effect of the fusion error on the calculated correction in patient setup.
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