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When Delivery Quality Assurance (DQA) results do not coincide with specific criteria, re-measurement and re-treatment planning are required because incorrect beam delivery to the patient may occur. Therefore, an analysis of the plan parameters to find reasons for failure is essential. In this paper, we examine the effect of plan parameters on the DQA passing rate and clarify the plan parameters that should be adjusted mainly for each Tomotherapy when DQA fails and re-planning is required. Two versions of Tomotherapy treatment machines, Tomotherapy Hi-art and Radixact X9, were used. An independent t-test was performed to confirm the significant difference between the DQA passing rates of Tomotherapy Hi-art and Radixact X9. Also, a regression analysis was performed to analyze the correlation between plan parameters and passing rates. The average passing rate for Radixact X9 was 99.82 (standard deviation [SD] = 0.32) and that for Tomotherapy Hi-art was 98.46 (SD = 1.76). The passing rate of Radixact X9 was significantly higher than that of Tomotherapy Hi-art (P = 0.000). Statistically significant independent variables adopted from Tomotherapy Hi-art were IEC Yf (p = 0.025), the modulation factor (p = 0.003), and the number of control points (p = 0.006). The statistically significant parameter for Radixact X9 was IEC Xf (p = 0.000). According to the results, the new version of Tomotherapy(Radixact X9) has a relatively higher DQA pass rate than the old version (Tomotherapy Hi-art), and if the DQA fails and re-planning is required, each of the plan parameters can be adjusted to achieve a higher pass rate.

During hyperthermia therapy, cancer cells are heated to a temperature in the range of 40 ~ 45 °C for a defined time period to damage these cells while keeping healthy tissues at safe temperatures. Prior to hyperthermia therapy, the amount of heat energy transferred to the cancer cells must be predicted. Among various non-invasive methods, the thermal prediction method using the specific absorption rate (SAR) is the most widely used method. The existing methods predict the thermal distribution by using a single constant for the mass density in one organ through assignment. However, because the SAR and the bio heat equation (BHE) vary with the mass density, the mass density of each organ must be accurately considered. In this study, the mass density distribution was calculated using the relationship between the Hounsfield unit and the mass density of tissues in preceding research. The SAR distribution was found using a quasi-static approximation to Maxwell’s equation and was used to calculate the potential distribution and the energy distributions for capacitive RF heating. The thermal distribution during exposure to RF waves was determined by solving the BHE with consideration given to the considering contributions of heat conduction and external heating. Compared with reference data for the mass density, our results was within 1%. When the reconstructed temperature distribution was compared to the measured temperature distribution, the difference was within 3%. In this study, the density distribution and the thermal distribution were reconstructed for the agar phantom. Based on these data, we developed an algorithm that could be applied to patients.

This study proposed to develop a phantom material that can represent the various contrasts of both MRI and CT images and is available to use in MRI-guided radiation treatment. Materials used for making a phantom that can be used for both MRI and CT image were agarose (T2 modifier), gadolinium-based contrast agent (T1 modifier), sodium fluoride (CT number modifier), and distilled water. They were mixed at various composition ratios and stirred until transparent. For the relationship between the ingredients and values, 48 samples were manufactured at various composition ratios. The relationship was expressed as equations, to be able to get the composition ratios of organs that we wanted to make. MR relaxation times were measured using 1.5 T MRI equipment. CT scans were performed at 120 kVp and extracted CT numbers from images. Based on the fitted equations derived from the relationship between ingredients and values, materials were manufactured using the composition ratio of human organs; brain (white and gray matter), liver, spleen, kidney, and prostate. The all values were within the reference range, but some exceeded the range due to the image noise. A phantom composed of substitutes made from the derived equations added other substances of different density like bone or lung can be used as an inhomogeneity dose calculation phantom for both CT and MRI. Furthermore, it can be applied to MRI-only based RTP systems and MRI-guided radiation treatment QA in the future.

Currently, the American Association of Physicists in Medicine (AAPM) recommends measuring the surface field size once a week by using an analog film in order to verify light and radiation field coincidence in the Quality Assurance (QA) of radiotherapy. However, the use of the film does not allow for a quantitative method of evaluation, and measuring the light field with radiation field detectors in a 2D array is difficult. Therefore, we used an amorphous-Se (a-Se) digital radiation detection system to measure the light and radiation fields simultaneously for a quantitative QA system, and the feasibility of using such a system was confirmed by ensuring the coincidence of the light and the radiation field measurements. The characteristics of the analog film and the a-Se digital radiation detection system were compared by delivering to each doses of 100, 10 monitor units(MU) of radiation at a rate of 400 MU/min to a radiation field 100 × 100 mm2 in size from a 100 cm source-surface distance (SSD). A 0.5 mm to 0.6 mm difference was measured in the X-axis, and a 0.3 mm difference was measured in the Y-axis. The difference in the measurements of the coincidence of light and the radiation field was less than 0.3 mm, which is relatively insignificant. These results indicate that the use of an a-Se digital radiation detection system is adequate for quality assurance of radiotherapy using light and radiation field coincidence. In addition, the experiment is considered to have provided valuable results in that the a-Se based digital radiation detection system enables simple and accurate QA for clinical radiation therapy by assessing the coincidence in the alignment of the light and the radiation fields.