Characteristics of a real-time radiation exposure dosimetry system using a synthetic ruby for radiotherapy.

Radiotherapy-related medical accidents are frequently caused by planning problems, excessive irradiation during radiotherapy, or patient movement. This is partly because the local exposure dose cannot be directly monitored during radiotherapy. This article discusses the development of our recent real-time radiation exposure dosimetry system that uses a synthetic ruby for radiation therapy. Background noise was observed before the measurement of the short-term characteristic features. Regarding the relationship between the number of photons and dose rate, using 100 monitor units (MU)/min as the measurement value, the counts decreased by approximately 10% at 600 MU/min. A clear correlation was observed between the MU value and the number of photons (R2 = 0.9987). The coefficient of variation (%CV) was less than ± 1.0% under all the irradiation conditions. Slight differences were observed between the ion chamber and the synthetic ruby dosimeters in the measurement of the percentage depth dose. However, this difference was almost matched by correcting for the Cherenkov light. Although some problems were observed with the synthetic ruby dosimeter system, our results indicate that the developed dosimeter can be used to measure the irradiation dose of patients in real time, with no significant impact on the data, as any effect would be masked by the larger effect of the ruby; however, the impact requires a detailed assessment in the future.

[Evaluation of AEC Consistency in Digital X-ray Imaging Systems].

The Japan Network for Research and Information on Medical Exposures (J-RIME) established the diagnostic reference level (DRL) and is advancing optimization of radiation protection. We believe that the difference in the imaging dose between facilities may be due to the fact that automatic exposure control (AEC) adjustment is not unified among manufacturers. The consistency of AEC is specified in JIS 4751-2-54, but it is not applicable to digital X-ray imaging systems because it is for optical density of analog X-ray imaging systems. This article evaluates the consistency of AEC in digital X-ray imaging systems. The AEC consistency was compared with the AEC-estimated dose from the air kerma (KAEC) using the phosphor-based imaging plate placed at the back of the AEC detector. We measured the AEC tube voltage and subject thickness characteristics (tracking) of four types of digital X-ray imaging systems at three facilities. In the test of tube voltage characteristics, the average KAEC values at all tube voltages were 2.37±0.04 µGy for A system, 7.30±1.44 µGy for B system, 3.53±0.13 µGy for C system, and 5.70±0.18 µGy for D system. The relative errors were +2.6 to -1.8% for A system, +25.3 to -22.6% for B system, +5.2 to -1.4% for C system, and +2.5 to -4.4% for D system. In the subject thickness characteristics test, the average KAEC values for all Al thicknesses were 2.34±0.02 µGy for A system, 5.95±0.23 µGy for B system, 4.25±1.12 µGy for C system, and 5.03±1.27 µGy for D system. The relative errors were +1.0 to -0.9% for A system, +4.1 to -5.0% for B system, +40.5 to -28.1% for C system, and +19.7 to -42.9% for D system.

Comparison of Count Normalization Methods for Statistical Parametric Mapping Analysis Using a Digital Brain Phantom Obtained from Fluorodeoxyglucose-positron Emission Tomography.

OBJECTIVES: Alternative normalization methods were proposed to solve the biased information of SPM in the study of neurodegenerative disease. The objective of this study was to determine the most suitable count normalization method for SPM analysis of a neurodegenerative disease based on the results of different count normalization methods applied on a prepared digital phantom similar to one obtained using fluorodeoxyglucose-positron emission tomography (FDG-PET) data of a brain with a known neurodegenerative condition. METHODS: Digital brain phantoms, mimicking mild and intermediate neurodegenerative disease conditions, were prepared from the FDG-PET data of 11 healthy subjects. SPM analysis was performed on these simulations using different count normalization methods. RESULTS: In the slight-decrease phantom simulation, the Yakushev method correctly visualized wider areas of slightly decreased metabolism with the smallest artifacts of increased metabolism. Other count normalization methods were unable to identify this slightly decreases and produced more artifacts. The intermediate-decreased areas were well visualized by all methods. The areas surrounding the grey matter with the slight decreases were not visualized with the GM and VOI count normalization methods but with the Andersson. The Yakushev method well visualized these areas. Artifacts were present in all methods. When the number of reference area extraction was increased, the Andersson method better-captured the areas with decreased metabolism and reduced the artifacts of increased metabolism. In the Yakushev method, increasing the threshold for the reference area extraction reduced such artifacts. CONCLUSION: The Yakushev method is the most suitable count normalization method for the SPM analysis of neurodegenerative disease.