{"title":"A multi-institutional survey on technical variations in total body irradiation in Japan.","authors":"Masayasu Kitagawa, Ryoichi Notake, Ryuta Nakahara, Shogo Hatanaka, Tatsunori Saho, Kengo Matsuda","doi":"10.1007/s12194-025-00894-2","DOIUrl":"10.1007/s12194-025-00894-2","url":null,"abstract":"<p><p>This study aimed to survey technical variations in total body irradiation (TBI) across Japan. A web-based questionnaire investigating technical aspects (irradiation method, in vivo dosimetry, organ shielding, and boluses) of TBI was distributed via the authors' acquaintances in each region of Japan using snowball sampling, and 73 institutions responded. The data were collected from January to April 2024. Three institutions used two distinct irradiation methods, yielding 76 reported techniques. The reported irradiation techniques included long source-to-surface distance (SSD) techniques, which involve using a large field and extended distance; helical intensity-modulated radiation therapy (IMRT) using specialized equipment (e.g., TomoTherapy), moving couch techniques, and volumetric modulated arc therapy (VMAT) using a standard C-arm linac, with responses totaling 60 (79%), 10 (13%), 4 (5%), and 2 (3%), respectively. All institutions performing IMRT-based (helical IMRT and VMAT) TBI used computed tomography simulation with the patient in the supine position and utilized a 6 MV photon beam. Conversely, the long SSD technique exhibited significant variation; while 47 institutions treated patients exclusively in the supine position, others reported using the prone and lateral positions. Furthermore, the photon beam energies varied, with 10 MV (41 responses), 6 MV (20 responses), and 4 MV (1 response) reported. Notably, 17 institutions using long SSD techniques did not perform in vivo dosimetry and 32 did not use boluses. The differences in the methods used to shield the organs were also reported. These variations highlight the need for standardization of in vivo dosimetry, dose homogeneity strategies, and organ-shielding in TBI.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"347-357"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143630883","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Masayuki Hattori, Hongbo Chai, Toshitada Hiraka, Koji Suzuki, Tetsuya Yuasa
{"title":"Cone-beam computed tomography (CBCT) image-quality improvement using a denoising diffusion probabilistic model conditioned by pseudo-CBCT of pelvic regions.","authors":"Masayuki Hattori, Hongbo Chai, Toshitada Hiraka, Koji Suzuki, Tetsuya Yuasa","doi":"10.1007/s12194-025-00892-4","DOIUrl":"10.1007/s12194-025-00892-4","url":null,"abstract":"<p><p>Cone-beam computed tomography (CBCT) is widely used in radiotherapy to image patient configuration before treatment but its image quality is lower than planning CT due to scattering, motion, and reconstruction methods. This reduces the accuracy of Hounsfield units (HU) and limits its use in adaptive radiation therapy (ART). However, synthetic CT (sCT) generation using deep learning methods for CBCT intensity correction faces challenges due to deformation. To address these issues, we propose enhancing CBCT quality using a conditional denoising diffusion probability model (CDDPM), which is trained on pseudo-CBCT created by adding pseudo-scatter to planning CT. The CDDPM transforms CBCT into high-quality sCT, improving HU accuracy while preserving anatomical configuration. The performance evaluation of the proposed sCT showed a reduction in mean absolute error (MAE) from 81.19 HU for CBCT to 24.89 HU for the sCT. Peak signal-to-noise ratio (PSNR) improved from 31.20 dB for CBCT to 33.81 dB for the sCT. The Dice and Jaccard coefficients between CBCT and sCT for the colon, prostate, and bladder ranged from 0.69 to 0.91. When compared to other deep learning models, the proposed sCT outperformed them in terms of accuracy and anatomical preservation. The dosimetry analysis for prostate cancer revealed a dose error of over 10% with CBCT but nearly 0% with the sCT. Gamma pass rates for the proposed sCT exceeded 90% for all dose criteria, indicating high agreement with CT-based dose distributions. These results show that the proposed sCT improves image quality, dosimetry accuracy, and treatment planning, advancing ART for pelvic cancer.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"425-438"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Enhanced urethral identification for radiotherapy planning using fat-suppressed 3D T2-weighted magnetic resonance imaging.","authors":"Yutaka Kato, Takayoshi Nakaya, Kuniyasu Okudaira, Yumiko Noguchi, Mariko Kawamura, Shunichi Ishihara, Shinji Naganawa","doi":"10.1007/s12194-025-00903-4","DOIUrl":"10.1007/s12194-025-00903-4","url":null,"abstract":"<p><p>This study proposes a fat-suppressed three-dimensional T2-weighted (3D-T2W) sequence on magnetic resonance imaging to enhance prostatic urethral identification in radiotherapy planning. Conventional 3D-T2W and the proposed sequence were obtained to evaluate prostatic urethral identification in 13 male patients. The proposed sequence demonstrated significantly higher Dice similarity coefficients compared to conventional 3D-T2W sequence (p = 0.001) and superior contrast-to-noise ratios. The proposed sequence also achieved significantly better visibility scores in visual assessment (p = 0.001). The proposed technique uses fat suppression in a standard 3D-T2W sequence, making it a simple and clinically applicable method that does not require specialized sequence designs. Our findings suggest that this approach could be a valuable noninvasive method for enhancing prostatic urethral identification, although further research with larger sample sizes and optimization of acquisition parameters is needed.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"589-596"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143754958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"External and internal GATE/Geant4 dosimetric calculations on voxelized phantoms.","authors":"Merai Sondes, Benrachi Fatima, Laouet Nadjet","doi":"10.1007/s12194-025-00904-3","DOIUrl":"10.1007/s12194-025-00904-3","url":null,"abstract":"<p><p>Monte Carlo simulation employing the GATE (Geant4 Application for Tomographic Emission) code plays a crucial role in radiation transport studies for dose calculations within computational phantoms. This paper presents a set of absorbed doses calculated using computational phantom Zubal and Monte Carlo GATE code version 9.3, based on two radiation exposure configurations: external (radiological accident) and internal (using <math> <mrow><mmultiscripts><mrow></mrow> <mrow></mrow> <mn>131</mn></mmultiscripts> <mi>I</mi></mrow> </math> radionuclide). The results were validated through comparison with previous studies employing different Monte Carlo codes (MCNP, EGS4), and the reference female computational model proposed by ICRP report 110. The findings demonstrate good agreement between GATE results for Zubal phantom and published data with MCNP and EGS4, as well as alignment with ICRP 110 reference phantom results, for both external and internal irradiation scenarios. In addition, the comparison between Zubal and ICRP 110 phantom reveals a minimal variation, attributed to inherent anatomical and geometrical disparities.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"514-522"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144034339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A CT-free deep-learning-based attenuation and scatter correction for copper-64 PET in different time-point scans.","authors":"Zahra Adeli, Seyed Abolfazl Hosseini, Yazdan Salimi, Nasim Vahidfar, Peyman Sheikhzadeh","doi":"10.1007/s12194-025-00905-2","DOIUrl":"10.1007/s12194-025-00905-2","url":null,"abstract":"<p><p>This study aimed to develop and evaluate a deep-learning model for attenuation and scatter correction in whole-body 64Cu-based PET imaging. A swinUNETR model was implemented using the MONAI framework. Whole-body PET-nonAC and PET-CTAC image pairs were used for training, where PET-nonAC served as the input and PET-CTAC as the output. Due to the limited number of Cu-based PET/CT images, a model pre-trained on 51 Ga-PSMA PET images was fine-tuned on 15 Cu-based PET images via transfer learning. The model was trained without freezing layers, adapting learned features to the Cu-based dataset. For testing, six additional Cu-based PET images were used, representing 1-h, 12-h, and 48-h time points, with two images per group. The model performed best at the 12-h time point, with an MSE of 0.002 ± 0.0004 SUV<sup>2</sup>, PSNR of 43.14 ± 0.08 dB, and SSIM of 0.981 ± 0.002. At 48 h, accuracy slightly decreased (MSE = 0.036 ± 0.034 SUV<sup>2</sup>), but image quality remained high (PSNR = 44.49 ± 1.09 dB, SSIM = 0.981 ± 0.006). At 1 h, the model also showed strong results (MSE = 0.024 ± 0.002 SUV<sup>2</sup>, PSNR = 45.89 ± 5.23 dB, SSIM = 0.984 ± 0.005), demonstrating consistency across time points. Despite the limited size of the training dataset, the use of fine-tuning from a previously pre-trained model yielded acceptable performance. The results demonstrate that the proposed deep learning model can effectively generate PET-DLAC images that closely resemble PET-CTAC images, with only minor errors.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"523-533"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144054069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Comparative analysis of radiotherapy modalities and techniques for left breast cancer: dose coverage, setup accuracy, with patient-specific selection criteria for applying deep inspiration breath hold.","authors":"Masud Parvej, Cristina Cappelletto, Angela Caroli, Lorenzo Vinante, Annalisa Drigo, Paola Chiovati","doi":"10.1007/s12194-025-00891-5","DOIUrl":"10.1007/s12194-025-00891-5","url":null,"abstract":"<p><p>To compare dosimetric outcomes between Free Breath (FB) and Deep Inspiration Breath Hold (DIBH) across different radiotherapy modalities, establish patient selection criteria for DIBH, and optimizing the setup margin (SM) in left breast cancer treatment. 26 patients with left breast cancer were studied at CRO, Aviano in Italy. FB and DIBH simulations were done using CT with a real-time position management system. 3DCRT and IMRT plans were prepared for both simulations of each patient. The setup margin was measured by Van Herk's formula and compared with residual uncertainties. The dose coverage of PTV and spare OARs were better with DIBH. The distance of more than 1.6 cm between (Left Anterior Descending artery) LAD and PTV was no significantly different for FB and DIBH. The setup margin by Van Herk's formula was calculated as 0.9 cm for DIBH_IMRT. The average duration of DIBH per respiration was 19 ± 4 s. So, holding one breath at least 19 s would be the criteria for choosing a patient to apply DIBH. DIBH enhances PTV dose coverage and OAR sparing in both 3DCRT and IMRT. When the distance between the LAD and PTV exceeds 1.6 cm, the application of DIBH depends on the availability of a LINAC with RPM and the patient's breathholding ability.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"417-424"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143543066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A multiple regression model for peak skin dose using principal component analysis in interventional radiology.","authors":"Noriyuki Kuga, Katsutoshi Shirieda, Yumi Hirabara, Yusuke Kurogi, Ryohei Fujisaki, Lue Sun, Koichi Morota, Takashi Moritake, Hajime Ohta","doi":"10.1007/s12194-025-00893-3","DOIUrl":"10.1007/s12194-025-00893-3","url":null,"abstract":"<p><p>This study addresses the growing concerns of increased radiation doses to patients resulting from the increased complexity of interventional radiology procedures. Despite the importance of dose management, few facilities use dosimetry systems to measure and control patient radiation doses. To aid in patient exposure control, this research aimed to predict the peak skin dose (PSD) using dose parameters from digital imaging and communication in medicine-radiation dose structured reports. The study focused on air kerma (K<sub>a,r</sub>) and air kerma area product (KAP) values categorized into fixed dose (radiography and fluoroscopy) and motion dose (rotational digital subtraction angiography) for frontal and lateral biplane devices. Using single and multiple regression analysis, model equations for PSD were developed based on data from a radio-photoluminescence glass dosimeter and five dose parameters. Principal component analysis (PCA) was applied to consolidate the data, and multiple regression models were created using principal component scores. The results showed that rotational digital subtraction angiography had a minimal impact on PSD, whereas the K<sub>a,r</sub> value demonstrated higher accuracy in predicting PSD than KAP. The inclusion of PCA in the multiple regression model further improved accuracy, with a root mean squared error of 226, confirming that PCA-enhanced models are more effective in predicting PSD.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"439-450"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143639648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Human phantom applicability of 3D-printed polylactic acid for X-ray dose analysis: simulation and measurement studies.","authors":"Donghee Han, Toshioh Fujibuchi","doi":"10.1007/s12194-025-00909-y","DOIUrl":"10.1007/s12194-025-00909-y","url":null,"abstract":"<p><p>In recent years, significant research has focused on the fabrication of human phantoms and the evaluation of radiological imaging using advanced 3D printing technologies and diverse filament materials. This study investigates the absorbed dose due to the physical attenuation of polylactic acid phantoms within the diagnostic X-ray energy range, utilizing Monte Carlo simulations and a radiophotoluminescence glass dosimetry system. The phantoms were fabricated with infill percentages ranging from 20 to 100%, which were visually verified through radiographic imaging, and the reference dosimetry depths varied from 10 to 110 mm. Monte Carlo simulations were performed using the Geant4 Application for Tomographic Emission and the Particle and Heavy Ion Transport code System, demonstrating good agreement with experimental results. The average differences between simulations and measurements were 2.6, 2.7, and 3.1% at 80, 100, and 120 kVp, respectively, with uncertainties of approximately 1% under consistent experimental conditions. The energy dependence of absorbed dose as a function of depth was also examined. For the dosimetry system, the absorbed dose exhibited a more pronounced decrease at lower tube voltages and with reduced infill percentages, resulting in an average error of 6.2% compared to simulation results. These findings provide valuable insights into the development of fully filament-based, human-equivalent phantoms and their potential applications in radiation dosimetry using high-density filament materials for various radiation-related devices.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"556-569"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143990265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Investigation of patient radiation exposure reduction through optimization of imaging conditions for stent enhancement processing in percutaneous coronary intervention: a retrospective study.","authors":"Kazuya Mori, Toru Negishi, Kouhei Makabe, Kazuhisa Matsukura","doi":"10.1007/s12194-025-00915-0","DOIUrl":"10.1007/s12194-025-00915-0","url":null,"abstract":"<p><p>Several percutaneous coronary intervention (PCI) support technologies have been developed to improve procedural outcomes. We retrospectively investigated whether a real-time stent enhancement processing system (Stent View; SV) can effectively reduce radiation dose during PCI. The control group comprised individuals subjected to PCI using SV under standard imaging conditions, whereas the evaluation group included those subjected to PCI using SV under reduced dose (68% of the standard dose). We evaluated the balloon marker detection accuracy of SV ( <math> <msub><mrow><mi>SV</mi></mrow> <mrow><mi>accuracy</mi></mrow> </msub> </math> ) and calculated the cumulative air kerma (K<sub>a,r</sub>) when SV was used. The mean <math> <msub><mrow><mi>SV</mi></mrow> <mrow><mi>accuracy</mi></mrow> </msub> </math> in the control and evaluation groups were 94.03 ± 14.52% and 94.62 ± 13.98%, respectively (p = 0.26), whereas the K<sub>a,r</sub> were 111.15 ± 79.62 mGy and 65.22 ± 47.35 mGy, respectively. On average, appropriate optimization of the SV imaging conditions reduced patient radiation dose during SV imaging by 41.32% without affecting the accuracy of SV image reconstruction.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"615-621"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144019993","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Retaking assessment system based on the inspiratory state of chest X-ray image.","authors":"Naoki Matsubara, Atsushi Teramoto, Manabu Takei, Yoshihiro Kitoh, Satoshi Kawakami","doi":"10.1007/s12194-025-00888-0","DOIUrl":"10.1007/s12194-025-00888-0","url":null,"abstract":"<p><p>When taking chest X-rays, the patient is encouraged to take maximum inspiration and the radiological technologist takes the images at the appropriate time. If the image is not taken at maximum inspiration, retaking of the image is required. However, there is variation in the judgment of whether retaking is necessary between the operators. Therefore, we considered that it might be possible to reduce variation in judgment by developing a retaking assessment system that evaluates whether retaking is necessary using a convolutional neural network (CNN). To train the CNN, the input chest X-ray image and the corresponding correct label indicating whether retaking is necessary are required. However, chest X-ray images cannot distinguish whether inspiration is sufficient and does not need to be retaken, or insufficient and retaking is required. Therefore, we generated input images and labels from dynamic digital radiography (DDR) and conducted the training. Verification using 18 dynamic chest X-ray cases (5400 images) and 48 actual chest X-ray cases (96 images) showed that the VGG16-based architecture achieved an assessment accuracy of 82.3% even for actual chest X-ray images. Therefore, if the proposed method is used in hospitals, it could possibly reduce the variability in judgment between operators.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"384-398"},"PeriodicalIF":1.7,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12103368/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143450583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}