Radiological Physics and Technology最新文献

This study aimedto investigate the relationship between MRI-derived skeletal muscle biomarkers and subjective exercise intensity, measured by the Rating of Perceived Exertion (RPE). Both T2* value and CSA showed significant time-dependent changes following exercise. The percent change (PC) in T2* immediately after exercise (T2* PC-post/pre) was most strongly associated with RPE (ρ = 0.45,p < 0.01), while CSA showed a weaker correlation. Muscle strength was not significantly associated with RPE.Random forest analysis identified T2* PC-post/pre as the most important predictor of RPE, supported by partial dependence plots showing a nonlinear increase in RPE with higher T2* value changes. T2* value changes after exercise reflect metabolic stress and serve as a more specific predictor of RPE than CSA or muscle strength. These findings highlight the potential of T2* value as a non-invasive biomarker for assessing subjective exercise intensity.

Purpose: In this study, we measured the radiation exposure of the medical relief team of the Japanese Red Cross Society (JRCS) members during the Noto Peninsula earthquake using electronic personal dosimeters (EPDs) and investigated the frequency of electromagnetic interference (EMI) events that caused abnormally high dose readings.

Methods: Six JRCS medical relief team members (two physicians, three nurses, and one logistics officer) involved in the Noto Peninsula earthquake disaster relief activities were provided with EPDs to measure their radiation exposure during the activity period. A background radiation dosimeter was also installed on-site to record ambient radiation levels.

Results: Over 2.5 days of disaster relief activities, the background radiation dose was 3.0 ± 3.0 μSv. However, the highest recorded dose among the team members was 2075.0 ± 207.5 μSv for a nurse, while the average dose for the other members was 40.0 ± 39.5 μSv. A significant radiation dose was observed despite no radioactive material dispersion.

Conclusions: Among the four individuals who exhibited abnormally high dose readings, three were operating digital devices at the time of measurement, suggesting a strong likelihood that electromagnetic interference was the cause. The effective management of radiation doses using EPDs during nuclear disasters requires the implementation of countermeasures against EMI from digital devices.

This study aimed to develop a model for predicting the bladder volume ratio between daily CBCT and CT to determine adequate bladder filling in patients undergoing treatment for prostate cancer with external beam radiation therapy (EBRT). The model was trained using 465 datasets obtained from 34 prostate cancer patients. A total of 16 features were collected as input data, which included basic patient information, patient health status, blood examination laboratory results, and specific radiation therapy information. The ratio of the bladder volume between daily CBCT (dCBCT) and planning CT (pCT) was used as the model response. The model was trained using a bootstrap aggregation (bagging) algorithm with two machine learning (ML) approaches: classification and regression. The model accuracy was validated using other 93 datasets. For the regression approach, the accuracy of the model was evaluated based on the root mean square error (RMSE) and mean absolute error (MAE). By contrast, the model performance of the classification approach was assessed using sensitivity, specificity, and accuracy scores. The ML model showed promising results in the prediction of the bladder volume ratio between dCBCT and pCT, with an RMSE of 0.244 and MAE of 0.172 for the regression approach, sensitivity of 95.24%, specificity of 92.16%, and accuracy of 93.55% for the classification approach. The prediction model could potentially help the radiological technologist determine whether the bladder is full before treatment, thereby reducing the requirement for re-scan CBCT. HIGHLIGHTS: The bagging model demonstrates strong performance in predicting optimal bladder filling. The model achieves promising results with 95.24% sensitivity and 92.16% specificity. It supports therapists in assessing bladder fullness prior to treatment. It helps reduce the risk of requiring repeat CBCT scans.

The International Atomic Energy Agency (IAEA) released a protocol named Technical Report Series (TRS-398) to measure the absorbed dose in water for external radiotherapy beams. It provides a unified approach for using calibrated ionization chambers that are traceable to standard laboratories in determining the absorbed dose to water. The objective of the study was to compare the reference dosimetry [dose at 10 cm depth ( $D_{10}$ )] between TRS-398 and its revised version. Reference dosimetry was performed for flattened photon beam with nominal energies of 6, 10, and 15 MV as well as flattening filter free (FFF) beam of energies 6 FFF and 10 FFF based on the guidelines of both TRS-398 and its revised version using two different ionization chambers of varying sensitive volumes such as 0.65 cm³ (FC65-G) & 0.13 cm³ (CC13) IBA ionization chambers with calibration coefficient traceable to absorbed dose to water (D_w) standards. The comparison of absorbed dose at 10 cm ( $D_{10}$ ) depth using both protocols with FC65-G chamber shows a difference ranging from 0.40 to 0.63% in FF beams and 0.03 to 0.05% in FFF beams. For CC13 chamber, the difference ranged from -0.60 to -0.77% in FF beams and -0.40 to -0.50% in FFF beams. The differences in absorbed dose between TRS-398 old and revised protocols were evaluated and a variation of up to 0.63% in $D_{10}$ was observed for FC65-G and -0.77% in $D_{10}$ was observed for CC13 chamber. The use of old $k_{Q,Q_O}$ value affects the reference dose measurements, which overestimates the results by an average of 0.5%. The use of cross-calibrated chamber following the old protocol in determining the $D_{10}$ underestimates the results by maximum of -0.77% in FF beams and -0.5% in FFF beams.

Mobile X-ray radiography is crucial for imaging patients with limited mobility; however, radiological technologists (RTs) may be positioned closer to patients and thus be at risk of harmful radiation doses owing to scattered radiation. As X-ray systems transitioned from digital computed radiography (CR) to flat panel detector (FPD) systems, we studied the RTs' eye lens and neck radiation doses over 2 years. Three RTs participated in measurements using a CR system, and five RTs participated in measurements using a FPD system. We measured radiation exposure with neck dosimeters (glass badges) and lens dosimeters (DOSIRIS®). The results showed a 66% reduction in lens dose after switching from the CR system to the FPD system. Comparisons of specific RT members also revealed significantly lower doses for the FPD system than for the CR system. Two main factors contributed to this decrease: the FPD system used a virtual grid instead of a scatter removal grid, and the RTs' awareness of radiation exposure increased with experience. Although the lens dose was significantly reduced, RTs should still wear protective eyewear and equipment when frequent imaging is expected or when working close to patients.

Aim: This study investigates secondary cancer risks in the contralateral breast (CB) and ipsilateral lung (IL) in postmastectomy radiotherapy (PMRT) patients treated with forward-planned intensity-modulated radiation therapy (IMRT). It is the first analysis of Dose-Volume Histogram (DVH)-based secondary cancer risks for patients undergoing forward-planned IMRT for PMRT. The objective is to compare cancer risks between conventional fractionated (CF) IMRT and hypofractionated (HF) IMRT. A retrospective analysis was conducted on 20 patients (aged 37-69 years) treated with 6 MV forward-planned IMRT. Treatment plans included CF IMRT (50 Gy in 25 fractions) and HF IMRT (42.56 Gy in 16 fractions). Organ equivalent doses (OED), excess absolute risk (EAR), lifetime attributable risk (LAR), and Relative Risk (RR) were calculated for CB and IL using Schneider non-linear mechanistic model & differential DVH. HF IMRT demonstrated a significant reduction in IL secondary cancer risk compared to CF IMRT (P = 0.0001), with LAR values decreasing from 54.9%-75.5% (CF) to 48.3%-66.5% (HF). The RR for IL cancer induction also declined from 10.16-13.6 (CF) to 9.06-12.1 (HF). In contrast, CB cancer risks exhibited minimal change, with LAR values slightly reducing from 1.08%-6.9% (CF) to 0.96%-6.1% (HF) (P = 0.52). The RR for CB remained relatively stable at 1.10-1.55 (CF) and 1.09-1.48 (HF). HF IMRT is more effective in reducing IL secondary cancer risk compared to CF IMRT, presenting it as a safer PMRT option. However, CB cancer risks remained largely unchanged, suggesting the need for further dose optimization research.

The landscape of computer vision is predominantly shaped by two groundbreaking methodologies: transformers and convolutional neural networks (CNNs). In this study, we aim to introduce an innovative mobile CNN architecture designed for orthopedic imaging that efficiently identifies both Bankart and SLAP lesions. Our approach involved the collection of two distinct magnetic resonance (MR) image datasets, with the primary goal of automating the detection of Bankart and SLAP lesions. A novel mobile CNN, dubbed MobileTurkerNeXt, forms the cornerstone of this research. This newly developed model, comprising roughly 1 million trainable parameters, unfolds across four principal stages: the stem, main, downsampling, and output phases. The stem phase incorporates three convolutional layers to initiate feature extraction. In the main phase, we introduce an innovative block, drawing inspiration from ConvNeXt, EfficientNet, and ResNet architectures. The downsampling phase utilizes patchify average pooling and pixel-wise convolution to effectively reduce spatial dimensions, while the output phase is meticulously engineered to yield classification outcomes. Our experimentation with MobileTurkerNeXt spanned three comparative scenarios: Bankart versus normal, SLAP versus normal, and a tripartite comparison of Bankart, SLAP, and normal cases. The model demonstrated exemplary performance, achieving test classification accuracies exceeding 96% across these scenarios. The empirical results underscore the MobileTurkerNeXt's superior classification process in differentiating among Bankart, SLAP, and normal conditions in orthopedic imaging. This underscores the potential of our proposed mobile CNN in advancing diagnostic capabilities and contributing significantly to the field of medical image analysis.

Objectives: The purpose of this study is to automatically extract the information necessary for chart recording from panoramic radiographs, to reduce the workload for dentists.

Study design: Using 1,085 dental panoramic radiographs (994 of permanent dentition and 91 of mixed dentition) taken at 10 facilities, we conducted tooth detection, numbering, and condition classification. Tooth condition was defined into five classes: natural, partial restoration, prosthetic crown, implant, and pontic. First, the YOLOv7 model was used to simultaneously detect 10 classes of deciduous teeth, 16 classes of permanent teeth, and four classes of tooth condition (excluding natural). We applied rule-based post-processing to the detected objects. Precision, Recall, and F1-score were used to evaluate our method, with an IoU (Intersection over Union) threshold set at 0.5.

Results: We achieved Precision, Recall, and F1-score of 98.51%, 98.38%, and 98.45%, respectively, in tooth numbering. In tooth condition classification, the average F1-score across the 5 classes was 95.47%.

Conclusions: Our method, which detects and classifies the tooth numbers of permanent and deciduous teeth and their tooth condition simultaneously, is expected to contribute to reducing the workload of dentists and improving accuracy.

Solid-state luminescence dosimeters face challenge in achieving accurate dosimetry in proton therapy owing to the linear energy transfer (LET)-dependent response. In this study, we proposed a two-dosimeter-based methodology to improve the accuracy of proton dosimetry by correcting the LET-dependent response of a radiophotoluminescence glass dosimeter (RPLD) and an Al₂O₃:Cr-based ceramic-type thermoluminescence dosimeter (TLD) for postal dosimetry. The LET dependent response for the RPLD and Al₂O₃:Cr TLD was investigated using an unmodulated 235 MeV proton beam delivered by a passive scattering system. Both dosimeters were individually calibrated in terms of the absorbed dose to water using a 6 MV X-ray beam. The luminescence efficiency ratio between the RPLD and Al₂O₃:Cr TLD ( ${\eta }_{{\text{RPLD},\text{Al}}_2{\text{O}}_3:\text{Cr}}$ ) was used as an index to determine the LET dependence correction factor for the RPLD and Al₂O₃:Cr TLD ( $k_{\text{LET}}^{\text{RPLD}}$ and $k_{\text{LET}}^{{\text{Al}}_2{\text{O}}_3:\text{Cr}}$ ). Modulated proton beams with different spread-out Bragg peak (SOBP) widths were used to evaluate the feasibility of the proposed two-dosimeter methodology. ${\eta }_{{\text{RPLD},\text{Al}}_2{\text{O}}_3:\text{Cr}}$ decreased with increasing LET. $k_{\text{LET}}^{\text{RPLD}}$ and $k_{\text{LET}}^{{\text{Al}}_2{\text{O}}_3:\text{Cr}}$ were fitted using exponential curves. Proton dosimetry based on the proposed methodology underestimated the absorbed dose to water by an averages of 1.88% and 3.21% for RPLD and Al₂O₃:Cr TLD, respectively. This demonstrated the feasibility of the proposed methodology. Although the method shows promise for LET correction, the uncertainties in the LET-dependent correction factors, namely 2.39% for the RPLD and 5.84% for the Al₂O₃:Cr TLD, indicate the need for further refinement.

Trigeminal neuralgia (TN) is characterized by severe facial pain and is treated with medications, surgery, percutaneous procedures, and stereotactic radiosurgery (SRS). The Gamma Knife (GK) has historically been the gold standard for SRS in TN, with alternatives such as the CyberKnife (CK) and standard linear accelerator (LA) having recently emerged. This study compared GK, CK, and LA treatments for TN via dosimetric analysis. Twenty patients (10 right- and 10 left-sided) with TN were planned in the three modalities. Dosimetric parameters, including D_Max, D_Min, D_Mean, D_98%, D_90%, D_50%, D_30%, and V_4Gy, were evaluated. The statistical significance was assessed using paired t tests. The CK and LA plans achieved a 60 Gy target coverage comparable to the GK plan. The GK plan exhibited superior brain stem sparing and lower V_4Gy compared with CK (p = 0.0013) and LA (p = 0.0001). Significant differences in D_Min, D_98%, D_90%, D_50%, and D_30% were observed between GK and CK (p < 0.05) and GK and LA (p < 0.05), but not for the CK-LA comparisons. The brain stem dose parameters (D_0.03 cc, D_1%, and D_2%) were significantly lower in the GK plan (p < 0.05). The GK exhibited better normal tissue sparing and brain stem dose distribution than CK and LA, attributable partly to its higher beam count. CK and LA require more intricate planning times. Despite the established efficacy of GK, CK and LA offer viable alternatives, underscoring the need for further research on the clinical outcomes of TN treatment in the respective modalities.