Nihon Hoshasen Gijutsu Gakkai zasshi最新文献

Purpose: In radiation therapy, the absorbed dose is corrected for changes in the nominal treatment distance using the inverse square law. However, in the case of electron beams, the inverse square law using the nominal treatment distance is invalid. Therefore, an effective source-to-surface distance (SSD) should be determined. The effective SSD must be measured for all electron beam energies and applicator sizes. Here, we calculated the effective SSD using a radiotherapy planning system with an electron Monte Carlo (eMC) calculation algorithm and evaluated its usefulness.

Methods: The effective SSD was calculated from the absorbed dose ratio at d_max at extended SSDs under 5 gap conditions, using both eMC calculations and LINAC measurements. The consistency between calculated and measured values was evaluated based on the absorbed dose ratio at d_max, effective SSD, and distance correction factor.

Results: The difference in the absorbed dose ratio at d_max between eMC calculations and measurements at extended SSDs was within 1.38%, and the effective SSD values agreed within 5.40 cm. Larger discrepancies in effective SSD were observed under conditions of high energy with large field sizes and low energy with small field sizes.

Conclusion: The good agreement in absorbed dose ratio at d_max, effective SSD, and distance correction factor between eMC calculations and measurements indicates that effective SSD calculation using eMC is feasible and can be employed for comparative verification against measured values.

Purpose: The objective of this study is to achieve site-specific three-dimensional (3D) automatic segmentation of skeletal muscles in body CT images. We aimed to improve recognition accuracy of nine muscle regions: sternocleidomastoid, erector spinae, trapezius, supraspinatus, rectus abdominis, obliques, quadratus lumborum, psoas major, and iliacus. Then, we focused on utilizing all skeletal muscle areas outside the target recognition regions that were not previously used.

Methods: Our method trains the 2D U-Net to learn both the target site-specific skeletal muscle region and all other skeletal muscles together. We utilized 30 cases of unenhanced body CT images and performed three-fold cross-validation.

Results: The proposed method achieved an average Dice coefficient of 88.37% across nine regions, showing improvements of 25.78% and 1.86% compared to the individual learning of each region (baseline) and the simultaneous learning of erector spinae (previous method), respectively.

Conclusion: Comprehensive learning of all skeletal muscle regions successfully improved the accuracy of U-Net-based 3D automatic segmentation for site-specific skeletal muscles in body CT. It leads to enhanced, precise body composition analysis for skeletal muscle regions.

Purpose: The combination of vessel wall imaging (VWI) and MR angiography (MRA) is useful for differentiating between intracranial atherosclerotic disease and arterial dissection. However, additional VWI scans increase total scan time. The purpose of this study was to develop and evaluate a simultaneous acquisition method for VWI and MRA using phase-sensitive inversion recovery (PSIR) in brain MRI.

Methods: Imaging parameters were optimized using a phantom simulating cerebral blood flow, brain parenchyma, and plaque. The proposed method was validated in an in vivo study by comparing it with the conventional method (T₁-turbo spin echo+time of flight-MRA).

Results: The proposed method achieved a higher contrast ratio than the conventional method. Additionally, the acquisition time was reduced to less than one-half (2 min 18 s vs 7 min 15 s).

Conclusion: This simultaneous acquisition method using PSIR is useful for brain MRI.

The half-value layer (HVL), an indicator of X-ray quality, is defined as the thickness of an aluminum (Al) filter that reduces the air kerma by half and is used to calculate the backscatter coefficient. HVL is determined from the attenuation curve of air kerma using log-linear interpolation. However, there are no references detailing the specific measurement method. This study aims to investigate the impact of varying the interval of Al filters used in the log-linear interpolation on the HVL, using Monte Carlo simulations. The Monte Carlo simulation was performed using Particle and Heavy Ion Transport code System (PHITS) Ver. 3.29. A photon point source was placed in the air, and the rectangular irradiation field was set to 5×5 cm at the detector position. The detector, simulated as a volume of 1 cm³ of air, was positioned 100 cm from the source. The Al filter thickness for the HVL was varied in increments of 0.1 mm. The HVL was calculated by linear interpolation, and the relative error was determined based on the minimum Al spacing. The X-ray tube voltages used were those of the RQR series (40, 50, 60, 70, 80, 90, 100, 120, and 150 kV). The beam qualities obtained from the measurements and the Monte Carlo simulation system were consistent with those specified for the RQR series in IEC 61267 within ±3.5%. The relative error of HVL for each tube voltage determined by simulation ranged from -0.5 to 4.6%, with a mean±standard deviation (median) of 1.12±0.95% (0.88%). The relative error was larger when the difference between the Al filter combinations was large and the interpolation coefficient α was 0.5. When the filter spacing is less than half the HVL, the accuracy of log-linear interpolation in HVL is less than ±1.5% relative error.

Purpose: This study aimed to clarify the actual conditions of near-miss incidents experienced by radiological technology students during clinical training and to analyze the contributing factors, to provide suggestions for future safety education and curriculum development.

Methods: An anonymous self-administered questionnaire was conducted among students and graduates of a radiological technology training program. The survey items included the specific nature of the incidents, background factors at the time of occurrence, the modality in which the incidents occurred, and the discoverer of the incidents. Quantitative data were analyzed using descriptive statistics and cross-tabulation, while qualitative responses were analyzed using content analysis.

Results: A total of 75 valid responses were obtained. The reported near-miss incidents were diverse, with many related to basic confirmation procedures such as patient misidentification, entanglement of tubes and cables, and failure to remove metallic items. Most of the incidents occurred in general radiography and computed tomography, accounting for approximately 70% of all reports. In terms of discoverers, the majority of incidents were noticed by the students themselves (58.3%), followed by clinical instructors (30.5%).

Conclusion: Near-miss experiences serve as valuable educational resources in student training. Redesigning safety education by focusing on common patterns shared with cases reported by licensed professionals, and systematizing these experiences into structured learning modules, may help enhance both the quality of education and safety awareness in radiological practice.