Radiological Physics and Technology最新文献

This study evaluated organ-specific absorbed doses and sex-dependent dosimetric variations in 15-year-old patients undergoing panoramic radiography across different units technologies. ICRP 156 mesh-type reference computational phantoms (MRCPs) were coupled with the PHITS Monte Carlo code for this analysis. Three panoramic units with distinct kinematics and exposure protocols were simulated: Unit A (fixed isocentric rotation, constant parameters) and Units B and C (sliding rotation axis, dynamic kV/mA modulation). Absolute absorbed doses were estimated using experimentally derived air kerma-area product conversion factors. Salivary glands and oral mucosa received the highest absorbed doses, ranging from 270 to 640 µGy, depending mainly on the tube voltage protocols. Sex-specific dosimetric disparities appeared due to anatomical variations interacting with beam geometry. The female MRCP absorbed 26% to 28% higher doses in the head lymph nodes due to smaller craniofacial dimensions, increasing scattered radiation contribution. Male phantoms systematically received 12% to 13% higher doses to the extrathoracic lymph nodes across all systems, due to a larger cervical circumference intersecting the divergent primary beam. Patient anatomy dictated the relative dose distribution, but the unit's operational protocol, particularly dynamic modulation and beam energy, determined the absolute exposure magnitude. These findings emphasize that 15-year-old patients require age-specific protocol optimization. Using highly realistic MRCPs with dynamic exposure parameters shows that unit technology is the main determinant of patient risk, offering a framework for ALARA implementation in adolescent dental radiology.

The accurate estimation of absorbed doses outside the treatment field is relevant in pediatric radiotherapy due to radiosensitivity and long-life expectancy. This study aims to evaluate out-of-field doses in pediatric brain radiotherapy by comparing dose calculations from a commercial treatment planning system (TPS) with Monte Carlo (MC) simulations. A pediatric computational phantom representing a 10-year-old child treated for a brain tumor using Volumetric-Modulated Arc Therapy (VMAT) was used. Dose calculations were performed in the Eclipse TPS and compared with MC simulations carried out using PRIMO software. The MC source configuration, dose-normalization procedure, VMAT control-point reproduction and uncertainty assessment were described to improve reproducibility. Agreement between measurements and MC simulations was observed for reference PDDs, whereas discrepancies were obtained for small-field lateral profiles, mainly due to dose gradients and detector volume-averaging effects. Outside the treatment field, the TPS showed dose discrepancies when compared with MC simulations, particularly in organs located far from the target volume. In several OARs, the TPS underestimated the mean absorbed dose, while in some structures closer to the field, dose overestimation was observed. These out-of-field values were interpreted with caution because organ-specific experimental validation in the pediatric phantom was not available. Within the limitations of a single phantom and plan, MC simulations provide a complementary assessment of out-of-field doses and anatomical volumes when compared with TPS calculations. Their combined use can improve dosimetric characterization and support future pediatric radiotherapy studies, although experimental validation in anthropomorphic phantoms remains necessary before claiming patient-specific accuracy.

Content-based retinal image analysis plays a crucial role in the early diagnosis of ocular diseases. In this study, we proposed a novel approach for efficient content-based retinal image retrieval and Diabetic Retinopathy (DR) detection using variants of local texture features derived from Local Binary Pattern (LBP), Local Ternary Pattern (LTP), and Gray Level Co-occurrence Matrix (GLCM). The methodology begins with meticulous image preprocessing to enhance feature extraction, followed by the extraction of LBP, LTP, and GLCM features, which capture intricate texture patterns and enrich the feature space for robust analysis. Subsequently, we trained machine learning models, including Support Vector Machine (SVM), Decision Tree, and Random Forest, on the extracted features to effectively retrieve retinal images and detect DR. A comparative analysis between preprocessed and raw images highlights the impact of preprocessing techniques on performance. A key innovation of this study lies in the fusion of multiple texture-based features, creating a comprehensive representation that integrates high-level semantic information with fine-grained local patterns. This hybrid approach enhances the system's capability to handle diverse retinal image variations, leading to improved retrieval accuracy and robustness. Further, a metaheuristic approach for feature selection and optimization is employed, comparing Differential Evolution, Genetic Algorithm, and Particle Swarm Optimization to identify the most effective features for retrieval. Differential Evolution achieved the highest precision of 90.67 % for retrieving the top 10 relevant images. The proposed hybrid approach demonstrates the effectiveness of integrating classical image analysis methods with machine learning for DR detection and content-based image retrieval. This research contributes to precision medicine and healthcare innovation by advancing ML-driven retinal image analysis.

This study compared a dose-volume histogram-based machine learning (ML) approach with a three-dimensional dose distribution-based convolutional neural network (CNN) approach for volumetric-modulated arc therapy planning in head and neck cancer (HNC). Sixty-five patients who underwent whole-neck radiotherapy were retrospectively analyzed; 55 cases were used for model training and 10 for independent testing. Treatment plans generated by the CNN-based framework and a commercial ML-based planning system (RapidPlan) were evaluated using dose-volume indices (DVIs) and blinded qualitative scoring. In the DVI analysis, the ML-based plans achieved significantly higher target coverage than both the CNN-based and clinical plans. In contrast, the CNN-based plans maintained a mean error of less than 2% relative to the clinical plans, indicating close agreement with the clinical standard. No statistically significant differences in organs-at-risk dose metrics were observed among the three approaches. In the blinded qualitative evaluation, mean scores were 4.7 ± 0.56, 4.0 ± 1.07, and 2.7 ± 0.90 for the clinical, CNN-based, and ML-based plans, respectively, with the ML-based plans receiving significantly lower scores. These findings indicate that differences in prediction methodology and optimization strategy influence final plan quality, particularly with respect to spatial dose characteristics. Three-dimensional dose distribution-based prediction may provide clinical advantages for automated radiotherapy planning in HNC.

Synthetic mammography (SM) derived from digital breast tomosynthesis differs fundamentally from conventional digital mammography (DM) in noise characteristics; however, these differences remain poorly understood. This study aimed to comprehensively characterize the noise structures unique to SM and to clarify their physical origins through comparison with DM. SM and DM images were analyzed using a multi-perspective framework that integrated the normalized noise power spectrum (NPS), noise factor analysis based on the relative standard deviation method, subtraction-based analysis, and pixel-wise signal-to-noise ratio (SNR) maps. Directional NPS was evaluated to assess anisotropy, while subtracted images were used to distinguish fixed-pattern noise from processing-related noise. Noise factor analysis was applied to quantify Poisson, multiplicative, and additive noise contributions, and SNR maps derived from repeated acquisitions were used to evaluate pixel-level reproducibility. Compared with DM, SM images exhibited pronounced anisotropy and stronger spatial correlation in the NPS, reflecting structural noise introduced during image synthesis. The NPS of subtracted images closely matched that of the original SM images, indicating that the dominant noise components were not spatially fixed patterns but rather randomly generated structural texture noise. Noise factor analysis demonstrated that multiplicative noise dominated SM images across all dose levels. No clear differences were observed in the SNR maps, whereas the non-stationary nature of SM noise was confirmed. These results demonstrate that SM noise is dominated by randomly generated structural textures with strong spatial correlations. The findings further emphasize the need for multi-perspective and task-based approaches for accurate assessment and effective clinical utilization of SM images.

Glioblastoma is a highly malignant brain tumor, and patient survival remains short despite multimodal treatment. One important prognostic factor is the methylation status of the O⁶-methylguanine DNA methyltransferase (MGMT) gene, as MGMT promoter methylation is associated with a better response to temozolomide chemotherapy. The aim of this study was to evaluate the association between noninvasively obtained imaging features and MGMT methylation status. Magnetic resonance (MR) images and MGMT methylation data for 55 glioblastoma cases (33 methylated, 22 unmethylated) were obtained from the publicly available TCGA-GBM database. After brain morphological standardization, tumor centroid coordinates were calculated and used as inputs for a quadratic discriminant classifier to assess the separability of MGMT methylation status based on anatomical location. In addition, 279 radiomic features, including histogram- and texture-based metrics, were extracted from tumor regions. Using Lasso regression, nine features were selected and subsequently analyzed using linear discriminant analysis. The areas under the receiver operating characteristic curve (AUCs) were 0.71 for anatomical location-based classification and 0.85 for radiomic feature-based classification. Because the correlation between anatomical location and radiomic features was low, integrating both feature sets improved discrimination performance to an AUC of 0.90. These results suggest that imaging examinations capture complementary information regarding tumor morphology and anatomical origin. The anatomical location of glioblastoma may therefore provide useful clues for classifying MGMT gene methylation status.

An incorrect triggering condition owing to irradiation of a cardiac implantable electronic device (CIED) with a defibrillation function has not yet reached consideration. This study aimed to experimentally examine the dose rate dependence on incorrect sensing and triggering for defibrillation, as either direct or indirect irradiation of a CIED requires numerical explanation by the dose rate. Four devices were directly irradiated by X-ray beams of 160 kV at dose rates to the device surfaces of 50- 3600 cGy/min. The devices were set to a VVI mode of 60 bpm, where the function for defibrillation was deactivated for safety reasons. Wave signals monitored on an electrocardiogram were recorded as a movie. During the 15-second irradiation time, the number and the time interval of incorrect sensing caused by irradiation were counted. The dose rate, considering the attenuation effect in the titanium case, was calculated as 11 - 800 cGy/min. It has been clearly observed that the ratio of incorrect sensing and the reach rate of incorrect triggering for defibrillation depend on the dose rate as well as on the sensing threshold level. The dose dependence was well reproduced by an error function. Extrapolation of the present result from a single manufacturer's result predicts that an incorrect triggering of shock therapy would not occur if the sensing threshold level were set at higher than 0.9 mV for X-ray kV beams at a dose rate of 600 cGy/min. This was therefore the first study of a safe condition against incorrect defibrillation caused by direct irradiation.

We aimed to establish and evaluate a method for measuring the cardiac implantable electronic devices (CIEDs) dose of radiotherapy patients using radiophotoluminescence glass dosimeters (RPLDs). Thirty-two treatment courses of thirty patients were analyzed. The relationships between the CIEDs dose and the treatment site locations were analyzed, and the RPLD doses were compared to the treatment planning system (TPS) doses for head and neck or chest cases (n = 18). A phantom study was conducted in five cases wherein the dose discrepancies between RPLD and TPS in all fractions were more than double and 5 cGy or more, and filter of energy dependence correction of RPLDs was evaluated. The longer the distance between the irradiation field and the CIEDs, the lower the doses, and it was approximated by a function of the power law of distance from the field edge. In eighteen cases where TPS and RPLD doses could be compared, the dose discrepancies per prescribed dose between RPLD measurements and TPS calculations were within 1.15%, with a 95% confidence limit of 0.6%. In the phantom study, the dose discrepancies between the RPLD and TPS were decreased using filters for all five cases. In conclusion, in vivo dosimetry using RPLD was very useful as a means of avoiding the following risks: when planning CT did not include the CIED for dose calculation-based assessment; unintended direct exposure of the CIED to the treatment beam; furthermore, TPS dose calculation accuracy outside of the irradiated field remains an issue.

For the safe performance of CT and MRI examinations, it is essential to confirm patient information, including any contrast agent allergies and implanted metallic devices. However, the data items collected and their management methods vary among facilities, and the extent of this variation remains unclear. This study aimed to identify commonly used patient safety data items in CT and MRI examinations, propose standard candidate items, and evaluate their conceptual resource-level mapping to HL7 FHIR resources to promote interoperability. A nationwide questionnaire survey was conducted in April 2024, targeting 4,274 medical institutions in Japan equipped with radiology examination ordering systems. The survey investigated the management status of data items-including examination ordering, contrast agent safety, MRI safety, and general safety management-across different systems (EMR/CPOE and RIS). Items with a usage rate of 80% or higher were extracted as standard candidate items. Responses were received from 782 facilities (18.3%). Ten items were identified as standard candidate items: purpose of examination (95.4%), disease name (87.5%), examination site (99.9%), contrast agent allergy (84.5%), creatinine (88.2%), eGFR (87.1%), infection (92.3%), and drug allergy (86.0%), along with pacemakers/ICDs (87.0%) and implanted metals (82.4%) among MRI-equipped facilities (n = 638). All 10 items could be mapped to HL7 FHIR R4 resources, and 6 of them had dedicated profiles in the JP Core FHIR Implementation Guide v1.1.2. This study identified data items commonly used for patient safety in CT and MRI examinations, confirmed the conceptual resource-level mapping to FHIR, and provided a foundation for standardization and interoperability.

Accurate delineation of cervical cancer clinical target volume (CTV) remains labor-intensive and variable. This retrospective single-center study internally evaluated a SAM-Med3D baseline initialized from public pretrained weights and fine-tuned on the study training cohort for prompted cervical cancer CTV segmentation on planning CT. The eligible cohort comprised 182 cases split into training ([Formula: see text]), validation ([Formula: see text]), and temporally subsequent independent test ([Formula: see text]) sets. CT scans were resampled, normalized, and prepared as label-centered 128 × 128 × 128 patches. The SAM-Med3D model was initialized from public pretrained weights and fine-tuned on the training cohort without architectural modification, then assessed with simulated/oracle-guided prompts at 1, 3, 5, 7, 9, and 11 clicks using Dice, HD95, 3-mm surface Dice, and volume consistency. Mean Dice increased from 0.827 at 1 click to a peak of 0.835 at 7 clicks and was 0.833 at 11 clicks. HD95 decreased from 12.46 mm at 1 click to 9.48 mm at 9 clicks and 10.18 mm at 11 clicks, and 3-mm surface Dice increased from 0.707 to 0.721 at 7 clicks. Thus, multi-click prompting produced modest improvements that plateaued at later clicks. A supplementary risk-guided prompt-selection pilot was performed on the same test set but was treated only as exploratory. Because patch extraction and prompt generation used reference-contour information, all analyses represent controlled upper-bound evidence. This study provides an internal feasibility baseline for promptable cervical cancer CTV segmentation; label-free ROI selection, full-volume inference, external validation, and clinician-in-the-loop testing remain necessary before clinical deployment.