Radiological Physics and Technology最新文献

Cone-beam computed tomography (CBCT) is commonly utilized in radiation therapy to visualize soft tissues and bone structures. This study aims to develop a machine learning model that predicts optimal, patient-specific CBCT doses that minimize radiation exposure while maintaining soft tissue image quality in prostate radiation therapy. Phantom studies evaluated the relationship between dose and two image quality metrics: image standard deviation (SD) and contrast-to-noise ratio (CNR). In a prostate-simulating phantom, CNR did not significantly decrease at doses above 40% compared to the 100% dose. Based on low-contrast resolution, this value was selected as the minimum clinical dose level. In clinical image analysis, both SD and CNR degraded with decreasing dose, consistent with the phantom findings. The structural similarity index between CBCT and planning computed tomography (CT) significantly decreased at doses below 60%, with a mean value of 0.69 at 40%. Previous studies suggest that this level may correspond to acceptable registration accuracy within the typical planning target volume margins applied in image-guided radiotherapy. A machine learning model was developed to predict CBCT doses using patient-specific metrics from planning CT scans and CBCT image quality parameters. Among the tested models, support vector regression achieved the highest accuracy, with an R² value of 0.833 and a root mean squared error of 0.0876, and was therefore adopted for dose prediction. These results support the feasibility of patient-specific CBCT imaging protocols that reduce radiation dose while maintaining clinically acceptable image quality for soft tissue registration.

The aim of the study was to evaluate the degree of error between Monte Carlo simulations of pediatric lens dose outside the scan range and measured values obtained with a dosimeter. Two types of computed tomography (CT) equipment and three pediatric anthropomorphic phantoms were used, each with a nanoDot optically stimulated luminescence dosimeter (nanoDot OSLD; Landauer, Inc., Glenwood, IL, USA) mounted on its left and right lenses. The scatter dose measurements obtained from the nanoDot were compared with those predicted by the particle and heavy ion transport code system, which served as a Monte Carlo simulation tool during pediatric chest CT examinations. The error rate between the mean measured dose and the simulated dose was within 1.5% for Aquilion Genesis and within 8.0% for Revolution. We evaluated the degree of error between Monte Carlo simulations of pediatric lens dose outside the scan range and measured values obtained with a dosimeter. The Monte Carlo simulations tended to underestimate the error.

Automatic exposure control (AEC) in digital radiography adjusts exposure time based on the chosen milliamperage (mA) and the patient's anatomical characteristics, ensuring the delivery of an appropriate radiation dose for optimal image quality. This study aimed to evaluate the reproducibility of AEC systems in general X-ray machines under various conditions. AEC reproducibility was assessed in two general X-ray machines: the SIEMENS Multix Top and the DRGEM GXR-40S. Both systems offer three sensitivity settings (high, medium, and low). A stack of Thai ten-baht coins, consisting of one and five layers, was used as a test object and placed directly over the AEC sensor. Ten exposures were carried out for repeated measurements. Differences in mAs values and coefficients of variation (CV) were calculated, and statistical analysis was performed using the Mann-Whitney U test. Results showed that mAs values changed in response to tube voltage, sensitivity setting, object thickness, and sensor position; however, these variations remained within acceptable limits. A higher mAs value was observed at lower tube voltages (80-81 kVp), a lower sensitivity setting (D or Slow), and a five-layer coin thickness. No significant differences were observed at higher tube voltage (100 kVp) and higher sensitivity (H or Fast; p > 0.05). In conclusion, AEC reproducibility testing showed mean mAs ranges of 0.51-3.25 with a maximum CV of 2.60% for SIEMENS, and 0.37-1.62 with a maximum CV of 3.37% for DRGEM. Both systems met international standard guidelines, with a CV below 5.00%, as recommended by AAPM Report No. 150, confirming consistent mAs values under various conditions.

Purpose: The purpose of this study was to evaluate the predictive value of MRI-based texture features for assessing stroke risk from vulnerable carotid plaques.

Method: Among patients diagnosed with carotid artery plaque by MRI, 10 patients with whom Time-to-Event for atherothrombotic stroke could be obtained were enrolled. Radiomics features were extracted from T1/T2-weighted black-blood images and cervical 3D time-of-flight images. Additionally, this investigation employed the extraction of 16 Gray-Level Fluid Zone Matrix (GLFZM) features, specifically developed for this analysis. Wall shear stress (WSS), a biomechanical characteristic, was also subjected to calculation. These features served as the basis for developing clinical models, radiomics-plaque models, radiomics-lumen models, GLFZM models, WSS models, and combined models. The performance of each model was evaluated using regression analysis by calculating mean squared error (MSE). As one aspect of the robustness of each model, we evaluated the models using Cox proportional hazard models and concordance indices (CI) derived from synthetic data generated with the noise scale.

Result: The LOOCV MSE and mean CI values were: clinical model (2.58 × 10⁶, 0.65), radiomics-plaque model (4.62 × 10⁶, 0.75), radiomics-lumen model (3.30 × 10⁶, 0.81), GLFZM model (2.00 × 10⁶, 0.74), WSS model (2.47 × 10⁶, 0.46), and combined model (1.48 × 10⁶, 0.78). The combined model demonstrated the minimal MSE.

Conclusion: This study demonstrated via preliminary simulations that analyzed clinical variables, radiomic features (plaque and lumen), texture features indicative of flow velocity (GLFZM), and biomechanical features (WSS) as model predictors, the potential utility of texture analysis in forecasting ischemic events in cerebral infarction resulting from vulnerable carotid plaques.

Pituitary adenomas, ranging from subtle microadenomas to mass-effect macroadenomas, pose diagnostic challenges for radiologists due to increasing scan volumes and the complexity of dynamic contrast-enhanced MRI interpretation. A hybrid CNN-LSTM model was trained and validated on a multi-center dataset of 2,163 samples from Tehran and Babolsar, Iran. Transfer learning and preprocessing techniques (e.g., Wiener filters) were utilized to improve classification performance for microadenomas (< 10 mm) and macroadenomas (> 10 mm). The model achieved 90.5% accuracy, an area under the receiver operating characteristic curve (AUROC) of 0.92, and 89.6% sensitivity (93.5% for microadenomas, 88.3% for macroadenomas), outperforming standard CNNs by 5-18% across metrics. With a processing time of 0.17 s per scan, the model demonstrated robustness to variations in imaging conditions, including scanner differences and contrast variations, excelling in real-time detection and differentiation of adenoma subtypes. This dual-path approach, the first to synergize spatial and temporal MRI features for pituitary diagnostics, offers high precision and efficiency. Supported by comparisons with existing models, it provides a scalable, reproducible tool to improve patient outcomes, with potential adaptability to broader neuroimaging challenges.

Measurement of renal volume is useful in the early detection and monitoring of renal disease. However, changes in renal volume during postural changes are not clear. Therefore, this study used multi-posture MRI system that can obtain renal images in any posture to assess the effect of posture on renal volume in the supine and upright positions. This study included 11 healthy volunteers (8 men and 3 women; mean age, 23.1 years; body mass index, 19.9 ± 1.3 kg/m²). Multi-posture MRI was used to compare renal volumes (total kidney, renal cortex, renal medulla, and renal pelvis volumes) between supine and upright positions. Wilcoxon signed-rank test was used. A P < 0.05 indicated significance. The total kidney, renal cortex, and renal medulla volumes in the upright position were significantly smaller than those in the supine position (P < 0.05 for all). Multi-posture MRI may provide new information on renal volume.

Although some studies have reported the radiation doses associated with kilovoltage (kV) X-ray imaging in motion-tracking radiotherapy using Radixact Synchrony, detailed assessments of skin doses that reflect the actual imaging frequency and patient positioning remain insufficient. This study aimed to estimate the entrance skin dose (ESD) associated with frequent kV image acquisition, evaluate the radiation dose in past patients at our hospital, and identify the contribution of imaging dose relative to the prescription dose and dose constraints. For each protocol available on the Radixact Synchrony system, the half-value layer, X-ray tube voltage, and air kerma at the kV imaging isocenter were measured using a semiconductor detector (RaySafe X2, Unfors RaySafe AB, Sweden). The ESD associated with the kV image acquisition was estimated based on the number of kV image acquisitions during tracking and corresponding imaging angles from 109 patients who underwent motion-tracking radiotherapy. The total number of imaging exposures throughout the treatment period averaged 1230 per patient, with a maximum of 3750. The cumulative ESD per acquisition angle averaged 69.1 mGy, with a maximum of 367.2 mGy. Furthermore, by considering the overlap of imaging angles and the influence of transmitted X-rays, the estimated maximum skin dose was 951.9 mGy. In this case, the maximum skin dose in the treatment plan was 47.9 Gy, and so the imaging dose corresponded to approximately 2% of the prescribed skin dose. Our findings indicate that the contribution of the imaging dose to the treatment dose is sufficiently low.

Variability in image quality and quantitative accuracy of Fluorine-18-fluorodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG-PET/CT) has been reported across institutions and devices. The Japanese Society of Nuclear Medicine (JSNM) introduced two guidelines to facilitate image quality assurance. Recently, a fully automated software, Arimaru (PDRadiopharma Inc.), has been developed to streamline this process. However, its performance relative to conventional software has not yet been fully validated. This study aimed to compare the physical parameters calculated using Arimaru and conventional software (PETquact IE, Nihon Medi-Physics Co., Ltd.) from PET images acquired using two PET/CT systems: Discovery MI and Discovery IQ (GE Healthcare). Images of the NEMA IEC Body phantom were acquired in list mode for 1800 s and reconstructed at multiple time points (30-1800 s) to simulate different noise levels. Five physical parameters (Q_H,10, N₁₀, Q_H,10/N₁₀, CV_BG, and SUV_max) were calculated using both the methods. The results showed that the automated method accurately positioned the region of interests (ROIs) and had a strong correlation with the conventional method across all parameters (r > 0.85, p < 0.05). However, some physical parameter values from the automated method were significantly different from those obtained using conventional software program. In conclusion, the automated software showed strong concordance with the conventional method and met JSNM guidelines. Nevertheless, systematic differences in the calculated values highlight the need to understand software-specific characteristics. The adoption of such tools may promote a broader and more consistent implementation of standardized PET imaging practices.

Prostate cancer volumetric modulated arc therapy (VMAT) planning often faces challenges in the construction of high-quality RapidPlan models when the number of cases is limited. In the present study, we retrospectively scored 90 VMAT plans using Plan Quality Metrics (PQM) and Adjusted PQM (APQM) and constructed 12 RapidPlan models from various combinations of cases with high and low PQM or APQM scores, each trained on 30 cases. Six representative models were selected for a detailed evaluation, including the P_H model based on the top 30 PQM cases and the AP_H model based on the top 30 APQM cases. All models were tested on ten independent cases that exhibited varying planning difficulties. The overall plan quality was assessed using PQM scores and dose-volume histogram (DVH) metrics for targets and organs at risk (OARs). The P_H model demonstrated significantly higher PQM scores than all other models (p < 0.05), with superior consistency and improved OAR sparing. Although the AP_H model performed well, the results were inconsistent. In challenging cases, the P_H model maintained a stable quality and outperformed both manual plans and APQM-based models. These findings indicated that case selection based on the actual clinical plan quality (PQM) is more effective than selection based on theoretical dose distributions (APQM) for building robust RapidPlan models, particularly when data are limited. This method is practical for small institutions and could be further improved by standardizing the PQM-based selection criteria and optimizing priority settings to enhance the generalizability and clinical utility of knowledge-based planning.

This study investigated the effects of preparation temperature and usage period on the relaxation times and apparent diffusion coefficients (ADC) of sucrose phantoms to enhance imaging reliability. Phantoms were prepared using 10% sucrose solutions at 20, 50, and 80 °C. T1 and T2 relaxation times and ADC were monitored over 80 days using magnetic resonance imaging on a 1.5 T system. T2 relaxation time in 50 °C solutions increased from 245.5 to 1579 ms, while 80 °C solutions showed the highest stability (coefficient of variation ≈ 1.8%). T1 relaxation time changes were minimal, and ADC decreased at an average rate of 2.19 × 10^-6 mm²/s per day. Bacteria were observed in the sucrose solution, and higher protein concentrations were strongly correlated with decreased 1/T2. In conclusion, sucrose phantoms exhibited temperature-dependent stability, with 80 °C preparations providing the most reliable T2 relaxation time.