In this study, we verified the accuracy of CCC algorithm in the TPS. Calculated results by our implemented algorithm was well satisfied with measured dose at small field size (〈20 7 times; 20 cm ). Our next study will perform to compensate theses inconsistencies.
Purpose: The present study was designed to develop the validation tool for compensation of patient positioning error using digitally reconstructed radiograph (DRR) extracted from three‐dimensional computed tomography (3DCT) and two orthogonal kilo‐voltage x‐ray images. Methods: To generate DRR image from 3DCT, the ray casting which is most straightforward method was applied in this study. The traditional ray casting algorithm finds the intersections of a ray with all objects, voxels of the 3DCT volume in the scene, with nearest‐neighbor interpolation method. Similarity between extracted DRR and orthogonal image was measured by using normalized mutual information method. All process was done by using Matlab. Two orthogonal image was acquired from Cyber‐knife system from anterior‐posterior view and right lateral view. 3DCT and two orthogonal image of an anthropomorphic Alderson‐Rando phantom and head and neck cancer patient were applied in this study. Finally, we designed graphic user interface (GUI) for easy use. Results: Registration accuracy with average errors of 2.12 mm ± 0.5 mm for transformation and 1.23° ± 0.4° for rotation using an anthropomorphic Alderson‐Rando phantom has been acquired. Conclusion: We demonstrated that this validation tool could compensate the patient positioning error. For further study, with the developed validation GUI tool for compensation of patient positioning error, we will add the registration tool by manual/auto using cone‐beam CT and kilo‐voltage CT image to utilize clinically in heavy‐ion radiation treatment center in Korea which scheduled for completion in 2016.
Electron beam therapy could be recommendable method to treat tumor at LIQ-S, LIQ-D locations rather than 3D-conformal radiation therapy, helical-tomotherapy at PBI technique because electron beam therapy is considered to provide the acceptable target coverage and the greatly lower dose to surrounding tissue.
Purpose: A potential validation tool for compensating patient positioning error was developed using 2D/3D and 3D/3D image registration. Methods: For 2D/3D registration, digitally reconstructed radiography (DRR) and three‐dimensional computed tomography (3D‐CT) images were applied. The ray‐casting algorithm is the most straightforward method for generating DRR. We adopted the traditional ray‐casting method, which finds the intersections of a ray with all objects, voxels of the 3D‐CT volume in the scene. The similarity between the extracted DRR and orthogonal image was measured by using a normalized mutual information method. Two orthogonal images were acquired from a Cyber‐Knife system from the anterior‐posterior (AP) and right lateral (RL) views. The 3D‐CT and two orthogonal images of an anthropomorphic phantom and head and neck cancer patient were used in this study. For 3D/3D registration, planning CT and in‐room CT image were applied. After registration, the translation and rotation factors were calculated to position a couch to be movable in six dimensions. Results: Registration accuracies and average errors of 2.12 mm ± 0.50 mm for transformations and 1.23° ± 0.40° for rotations were acquired by 2D/3D registration using an anthropomorphic Alderson‐Rando phantom. In addition, registration accuracies and average errors of 0.90 mm ± 0.30 mm for transformations and 1.00° ± 0.2° for rotations were acquired using CT image sets. Conclusion: We demonstrated that this validation tool could compensate for patient positioning error. In addition, this research could be the fundamental step for compensating patient positioning error at the first Korea heavy‐ion medical accelerator treatment center.
Purpose: An air bubble‐free flood phantom with grid patterns is fabricated to measure extrinsic resolution and linearity of PET scanner. It is designed to implement a 99m‐Tc flood phantom with motorized water pump to reduce the shaking procedure as well as reduce radiation exposure to workers. Method and Materials: The flood phantom is fabricated with circular (50cm diameter) and rectangular shapes (60cm*40cm) to measure the extrinsic resolution of conventional gamma camera. It has a handle in which an air pocket and water pump are placed. The air pocket is made of two chambers with conical valve between them. During the shaking procedure, the air bubbles are sucked into to the air pocket due to gravity effect and water perturbation. Once the air bubbles are trapped in the air pocket. They could not escape from it. After the radioisotopes are injected through a pin hole in the air pocket, water pump is turned on. The shaking procedure starts. In order to estimate the effectiveness of the shaking mechanism, the survey meter with well‐guided collimator is manually rotated on the flood phantom until uniformity of 3% is reached. It take approximately 5 minutes to mix the water with radioisotopes thoroughly. Results: The phantom is used to evaluate the extrinsic resolution and to estimate the uniformity/linearity of gamma camera or SPECT camera. The images are analyzed to quantitatively measure the integral and differential uniformity and the non‐linearity of the imaging system. The detailed data will be presented in the meeting. Conclusion: The phantom was very handy and effective to minimize radiation exposure to an operator. The multi‐channel ionization chamber will be replaced manual rotation procedure to design fully automatic monitoring system.
Purpose: Intensity modulated radiation therapy has been a very popular and effective treatment technique for the treatment of prostate, head & neck and liver etc. Meanwhile, another innovative treatment technique, intensity modulated arc therapy, was developed to complement some drawbacks of IMRT like long treatment time and low MU efficiency. Since the IMAT completes the treatment just within one or two rotations, it is not easy to get optimized leaf sequences in a deterministic way. In this study, we tried to get optimized IMAT treatment plan by genetic algorithm. Method and Materials: First, the start/end positions of MLC leaves at each rotation angle with 10° interval were selected as optimization variables and encoded into genetic chromosomes. They experience genetic operations such as generation, selection, crossover, mutation and reproduction and the most optimized solution remains in the end of iteration. The constraint of maximum leaf speed was included in these operations. And the fitness of each population was evaluated by DVH volume constraint based objective function. IMAT dose distribution was calculated as a weighted sum of MLC shape at each angle and related Dij matrix similar to IMRT dose calculation. The algorithm was implemented in our treatment planning system and the dose distributions and DVHs of single and double gantry rotation cases were compared. Results: IMAT plan gave comparable results with conventional IMRT even with single gantry rotation and there was not significant improvement in double gantry rotations. Genetic algorithm required about 3,000 generations to reach optimized value due to its stochastic nature. Conclusion: It was possible to optimize IMAT plan with genetic algorithm and the results are optimized MLC leaf sequences readily deliverable in general linear accelerators. It can be an efficient method to solve IMAT optimization problem despite of relative slow convergence.
Purpose: To suggest a novel analytic beam source model for a flattening filter free(FFF) beam, a multi‐source model and its optimization method applicable to a treatment planning system were developed. Methods: Previous three source model was improved by introducing off axis ratio (OAR) of primary photon fluence to generate cone shape profiles. The parameters of the model and the OAR were determined from measured head scatter factors and a measured dose profile of a 40 × 40 cm2 field size using a line search optimization technique. A new method to acquire gradient terms for OARˈs was developed to enhance the speed of the optimization process. The improved model was validated with measured dose profiles from 3 × 3 cm2 to 40 × 40 cm2 field sizes at 6 and 10 MV from a TrueBeamTM STx linear accelerator. Furthermore, planar dose distributions for clinically used radiation fields were also calculated and compared with measurements using a 2D array detector using the gamma index method. Results: All dose values for the calculated profiles agreed with the measured dose profiles within 0.5% at 6 and 10 MV beams, except for some low dose regions for larger field sizes. A slight overestimation was seen in the lower penumbra region near the field edge for the large field sizes by 1 % ∼ 4%. The planar dose calculations showed comparable passing rates (> 98%) when the criterion of the gamma index method was selected to be 3%/3 mm. Conclusions: Developed source model showed good agreements between measured and calculated dose distributions. The model is easily applicable to any other linear accelerator using FFF beams as the data required include only the measured PDD, dose profiles and output factors for various field sizes, which are easily acquired during conventional beam commissioning process.
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