• Journal of Radiation Protection and Research
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• v.44 no.1
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• pp.32-42
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• 2019

Background: There have been much efforts to develop the proper and realistic machine Quality Assurance (QA) reflecting on real Volumetric Modulated Arc Therapy (VMAT) plan. In this work we propose and test a special VMAT plan of plan-class specific (pcsr) QA, as a machine QA so that it might be a good solution to supplement weak point of present machine QA to make it more realistic for VMAT treatment. Materials and Methods: We divided human body into 5 treatment sites: brain, head and neck, chest, abdomen, and pelvis. One plan for each treatment site was selected from real VMAT cases and contours were mapped into the computational human phantom where the same plan as real VMAT plan was created and called plan-class specific reference (pcsr) QA plan. We delivered this pcsr QA plan on a daily basis over the full research period and tracked how much MLC movement and dosimetric error occurred in regular delivery. Several real patients under treatments were also tracked to test the usefulness of pcsr QA through comparisons between them. We used dynalog file viewer (DFV) and Dynalog file to analyze position and speed of individual MLC leaf. The gamma pass rate from portal dosimetry for different gamma criteria was analyzed to evaluate analyze dosimetric accuracy. Results and Discussion: The maxRMS of MLC position error for all plans were all within the tolerance limit of < 0.35 cm and the positional variation of maxPEs for both pcsr and real plans were observed very stable over the research session. Daily variations of maxRMS of MLC speed error and gamma pass rate for real VMAT plans were observed very comparable to those in their pcsr plans in good acceptable fluctuation. Conclusion: We believe that the newly proposed pcsr QA would be useful and helpful to predict the mid-term quality of real VMAT treatment delivery.

• Journal of the Korean Society of Radiology
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• v.18 no.1
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• pp.1-9
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• 2024

Proton therapy is known for its superior treatment method due to Bragg peak. To enhance the therapeutic effects of protons, research has been conducted on distributing gold nanoparticles within tumors to increase the absorbed dose. While previous studies focused on handling gold nanoparticles at micrometer and nonometer scale, this study proposes a method to computationally estimate the effect of gold nanoparticles at the millimeter scale. The Geant4 toolkit was applied to computational modeling. Assuming a uniform distribution of water, similar to the human body, and gold nanoparticles, the concentration of gold nanoparticles was adjusted using density ratios. When the density ratio was 5%, the gain in absorbed energy due to gold nanoparticles was nearly twice that of the pure water phantom at the Bragg peak. As the density ratio increased, the gain in absorbed energy linearly increased. When gold nanoparticles were distributed in only one voxel at the Bragg peak, the energy of the protons affected only the neighboring voxels. However, in cases where gold nanoparticles were distributed over a wide area, the volume showing 95% of the maximum absorbed energy (9.46 keV) for the pure water phantom (9.95 keV) exhibited an improvement in absorbed energy over a region 16 times larger, and this region increased as the density ratio increased. Further research is needed to quantify the relationship between the density ratio of gold nanoparticles and the relative biological effect (RBE) in the millimeter scale.

• Journal of Radiation Protection and Research
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• v.41 no.4
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• pp.389-394
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• 2016

Background: Tetrahedral-mesh geometries can be used in the MCNP code, but the MCNP code accepts only the geometry in the Abaqus input file format; hence, the existing tetrahedral-mesh models first need to be converted to the Abacus input file format to be used in the MCNP code. In the present study, we developed a simple but useful computer program, TET2MCNP, for converting TetGen-generated tetrahedral-mesh models to the Abacus input file format. Materials and Methods: TET2MCNP is written in C++ and contains two components: one for converting a TetGen output file to the Abacus input file and the other for the reverse conversion process. The TET2MCP program also produces an MCNP input file. Further, the program provides some MCNP-specific functions: the maximum number of elements (i.e., tetrahedrons) per part can be limited, and the material density of each element can be transferred to the MCNP input file. Results and Discussion: To test the developed program, two tetrahedral-mesh models were generated using TetGen and converted to the Abaqus input file format using TET2MCNP. Subsequently, the converted files were used in the MCNP code to calculate the object- and organ-averaged absorbed dose in the sphere and phantom, respectively. The results show that the converted models provide, within statistical uncertainties, identical dose values to those obtained using the PHITS code, which uses the original tetrahedral-mesh models produced by the TetGen program. The results show that the developed program can successfully convert TetGen tetrahedral-mesh models to Abacus input files. Conclusion: In the present study, we have developed a computer program, TET2MCNP, which can be used to convert TetGen-generated tetrahedral-mesh models to the Abaqus input file format for use in the MCNP code. We believe this program will be used by many MCNP users for implementing complex tetrahedral-mesh models, including computational human phantoms, in the MCNP code.

• The Korean Journal of Nuclear Medicine
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• v.37 no.5
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• pp.288-300
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• 2003

Purpose: To obtain regional blood flow and tissue-blood partition coefficient with time-activity curves from ${H_2}^{15}O$ PET, fitting of some parameters in the Kety model is conventionally accomplished by nonlinear least squares (NLS) analysis. However, NLS requires considerable compuation time then is impractical for pixel-by-pixel analysis to generate parametric images of these parameters. In this study, we investigated several fast parameter estimation methods for the parametric image generation and compared their statistical reliability and computational efficiency. Materials and Methods: These methods included linear least squres (LLS), linear weighted least squares (LWLS), linear generalized least squares (GLS), linear generalized weighted least squares (GWLS), weighted Integration (WI), and model-based clustering method (CAKS). ${H_2}^{15}O$ dynamic brain PET with Poisson noise component was simulated using numerical Zubal brain phantom. Error and bias in the estimation of rCBF and partition coefficient, and computation time in various noise environments was estimated and compared. In audition, parametric images from ${H_2}^{15}O$ dynamic brain PET data peformed on 16 healthy volunteers under various physiological conditions was compared to examine the utility of these methods for real human data. Results: These fast algorithms produced parametric images with similar image qualify and statistical reliability. When CAKS and LLS methods were used combinedly, computation time was significantly reduced and less than 30 seconds for $128{\times}128{\times}46$ images on Pentium III processor. Conclusion: Parametric images of rCBF and partition coefficient with good statistical properties can be generated with short computation time which is acceptable in clinical situation.