Radiological Physics and Technology最新文献

{"title":"Does quantitative susceptibility mapping elucidate age-related alterations in deep gray matter iron deposition? A systematic review.","authors":"Maryam Helfi, Haniyeh Baniasadipour, Ali Kharazmi, Zeinab Mohammadpour Moghadam, Erfan Hasanpour Khalesi","doi":"10.1007/s12194-026-01131-0","DOIUrl":"https://doi.org/10.1007/s12194-026-01131-0","url":null,"abstract":"<p><p>Age-related iron accumulation in the brain is linked to neurodegenerative processes, contributing to neuronal damage and functional decline. Quantitative Susceptibility Mapping (QSM), an advanced MRI technique, provides superior sensitivity for assessing iron deposition in vivo compared to traditional methods like R2* and susceptibility-weighted imaging (SWI). This systematic review evaluates QSM's ability to detect age-related iron changes in healthy aging populations, focusing on technical and methodological considerations. Following PRISMA guidelines, we searched Embase, MEDLINE, Scopus, and Web of Science for studies (2015-2025) using QSM to assess brain iron in healthy aging. Included studies reported susceptibility changes in brain nuclei. Data on study characteristics, QSM values, reference regions, and processing methods were extracted. Quality was assessed using the Newcastle-Ottawa Scale. A narrative synthesis was conducted due to methodological heterogeneity. From 110 records, 12 studies with 2,178 participants were included. Consistent increases in magnetic susceptibility, indicating iron accumulation, were observed in the caudate nucleus and putamen. The red nucleus, substantia nigra, and dentate nucleus showed increased susceptibility in most studies, while the hippocampus and thalamus exhibited variable, age-dependent patterns. Methodological diversity in QSM acquisition and processing (phase unwrapping, background field removal, dipole inversion) contributed to variability. QSM could effectively detect age-related cerebral iron deposition, especially in deep gray matter nuclei, with implications for understanding brain aging and neurodegenerative risk. Standardized protocols and longitudinal studies are needed to improve comparability and clarify temporal dynamics. QSM's sensitivity makes it a valuable biomarker for distinguishing normal aging from pathological processes, informing future diagnostic and therapeutic strategies.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148892377","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":"RadTechBERT: a domain-specific language model for radiological technology evaluated using Japanese national examination-derived cloze questions.","authors":"Ayako Yagahara, Noriya Yokohama, Masahito Uesugi, Mitsuhiro Aizawa, Daisuke Ando, Tomoki Ishikawa, Masaru Sudo, Naoki Nishimoto, Takumi Tanikawa, Yousuke Aoki, Yuji Tani, Takaaki Banno, Minoru Kawamata","doi":"10.1007/s12194-026-01133-y","DOIUrl":"https://doi.org/10.1007/s12194-026-01133-y","url":null,"abstract":"<p><p>Specialized language models can improve performance in expert domains; however, in radiological technology, evaluation datasets and models tailored to radiological technologists' practice remain limited and are not widely available. In this study, RadTechBERT was developed by pretraining a Bidirectional Encoder Representations from Transformers on a radiological technology-specific corpus from the initial pretraining stage, without relying on continued pretraining of existing general models. To enable systematic evaluation, a new cloze question dataset was constructed from items derived from the Japanese national examination for radiological technologists, and subject labels were added to support subject-level analyses. To develop the model, Unigram and Byte Pair Encoding (BPE) tokenizers were compared with vocabulary sizes of 32 K, 50 K, and 100 K. RadTechBERT was trained under two settings: using only the domain-specific corpus and using a mixed corpus that additionally included Wikipedia. For benchmarking, baseline models pretrained on general corpora such as Wikipedia, as well as existing and medical textbook-based models, were also evaluated. Performance was assessed using Top-5 accuracy on the cloze task, both overall and by subject. RadTechBERT with BPE_32K outperformed baselines in many subjects, with more than a twofold improvement in radiation safety management and radiation measurement relative to the strongest baseline. In contrast, gains were smaller in subjects having substantial overlap with general medicine, and Wikipedia mixing did not yield consistent improvements. The optimal tokenizer and vocabulary size were subject-dependent.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889096","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":"Three-dimensional image metric maps for characterizing local image changes associated with AI-based motion correction in coronary CT.","authors":"Kozo Shimizu, Tetsuya Tachiiri, Masaki Yoshida, Tsubasa Shimoguchi, Kengo Konishi, Takeshi Inoue, Yuya Yamatani, Hideki Kunichika, Ryosuke Taiji","doi":"10.1007/s12194-026-01132-z","DOIUrl":"https://doi.org/10.1007/s12194-026-01132-z","url":null,"abstract":"<p><p>To investigate whether three-dimensional image metric maps can describe the extent and characteristics of local image changes associated with AI-based motion correction using CLEAR Motion in coronary CT. This retrospective single-center study included 24 coronary CT cases reconstructed from the same raw data with and without CLEAR Motion. Three-dimensional maps of structural similarity index (SSIM), peak signal-to-noise ratio (PSNR), and deformation vector field (DVF) magnitude were generated after resampling to 0.5-mm isotropic voxels and intensity normalization. Without a true motion-free reference, the anatomical correctness of motion correction could not be directly verified. Therefore, the maps were assessed using spatial congruence analysis with Precision and Recall, patch-wise Spearman correlation analysis with bootstrap confidence intervals, and visual assessment by two readers using a 5-point scale. The three-dimensional image metric maps depicted local image changes predominantly near the coronary arteries, in a distribution consistent with the intended design of CLEAR Motion. Under the main analysis condition, Precision was 90.1% for SSIM, 90.5% for PSNR, 84.6% for DVF magnitude, and 89.2% for absolute difference. Recall values were low, indicating localized rather than diffuse changes. SSIM and PSNR showed a strong positive correlation, whereas both showed negative correlations with DVF magnitude and absolute difference. Visual assessment supported the spatial localization shown by the numerical analysis. Three-dimensional image metric maps based on SSIM, PSNR, and DVF magnitude may be useful for characterizing local image changes associated with AI-based motion correction in coronary CT.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":""},"PeriodicalIF":1.6,"publicationDate":"2026-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148889102","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":"Agreement between ventricular stroke volumes and great vessel flow using cine and phase-contrast MRI in healthy subjects.","authors":"Ai Kawaguchi, Wataru Kawaguchi, Miyuki Hayashi, Kenji Yasuda, Yoichi Ohashi","doi":"10.1007/s12194-026-01084-4","DOIUrl":"10.1007/s12194-026-01084-4","url":null,"abstract":"<p><p>Indirect quantification of valvular regurgitation using cardiovascular magnetic resonance assumes agreement among left ventricular stroke volume (LV-SV), right ventricular stroke volume (RV-SV), and forward flow in the ascending aorta (Ao) and pulmonary artery (PA). This study evaluated agreement among these parameters in healthy subjects. In 15 healthy volunteers, LV-SV and RV-SV were calculated from LV short-axis cine images, and Ao and PA flows were measured using phase-contrast MRI. Agreement among six parameter pairs was assessed using Bland-Altman analysis and intraclass correlation coefficients (ICC). Bland-Altman analysis demonstrated negative additive bias in RV-PA, RV-Ao, and RV-LV comparisons, indicating underestimation of RV-SV. Complementary ICC analysis showed moderate agreement in all RV-related pairs. Indirect regurgitant volume estimation based on LV-Ao appears robust, whereas RV-PA may underestimate regurgitant volume when RV-SV is derived from LV short-axis cine imaging.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"1407-1412"},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148259332","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 reproducible framework for constructing a longitudinal low-dose CT screening image database: implementation using 11 years of real-world data.","authors":"Junji Shiraishi, Rie Tanaka, Tetsuo Matsunaga, Tetsuya Minami, Satoshi Kobayashi","doi":"10.1007/s12194-026-01082-6","DOIUrl":"10.1007/s12194-026-01082-6","url":null,"abstract":"<p><p>This study presents a reproducible methodological framework for constructing a longitudinal low-dose computed tomography (LDCT) screening image database and demonstrates its implementation using 11 years of real-world data from a single region in Japan. The framework integrates hierarchical identifier design, deterministic metadata linkage, structured anonymization, and a minimal metadata specification to enable consistent organization of longitudinal imaging data. The implemented database comprises 45,337 LDCT examinations from 23,065 examinees, with 47.0% undergoing repeated screening. All examinations were acquired using a standardized CT scanner and protocol and were linked to screening assessment categories and smoking exposure information. The resulting structure supports reproducible subject-level aggregation, temporal tracking, and quantitative image analysis. This framework provides a transferable model for institutions seeking to construct longitudinal screening imaging repositories.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"1394-1406"},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148278993","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":"Establishing discard criteria for lead aprons using deep learning-based quantification of defect area on X-ray fluoroscopic video.","authors":"Koichi Hanada, Haruka Suzuki, Masato Takahashi, Hiraku Fuse, Manamu Kimura, Kota Sasaki, Kenji Yasue, Hiroki Nosaka, Shin Miyakawa, Norikazu Koori","doi":"10.1007/s12194-026-01086-2","DOIUrl":"10.1007/s12194-026-01086-2","url":null,"abstract":"<p><p>To establish an objective discard framework for lead aprons, defect areas quantified from X-ray fluoroscopy videos were linked to their corresponding dosimetric impacts. A fine-tuned YOLOv8 instance segmentation model, achieving an average precision at an intersection-over-union threshold of 0.5 (AP@0.5) of 0.457, was employed to analyze fluoroscopic videos of 18 aprons with 0.25 mmPb equivalence. The model enabled automated defect detection and area estimation with a video processing time of 15 s. Dosimetry experiments mimicking endoscopic retrograde cholangiopancreatography (ERCP) conditions were conducted using an anthropomorphic phantom and apron samples with slit defects of varying widths. The results indicated that the transmitted dose increased linearly with defect area under all tested conditions. Based on weighted least-squares analysis of covariance (WLS-ANCOVA), slit widths of 1.0, 2.0, and 3.5 mm were pooled for modeling, as they exhibited similar dosimetric behavior. A linear model was applied to relate defect area to the increase in transmitted dose, defining the discard threshold as the minimum area at which the upper one-sided 95% prediction limit reached a specified dose limit. Given a baseline dose of 2.38 mSv after transmission through the apron without defects and a regulatory dose limit of 5 mSv per 3 months, the discard threshold for slit-like defects was determined to be 2.87 cm². The proposed framework enables automated and facility-tailorable discard decisions by linking quantified defects to radiation protection objectives using conservative, prediction-based criteria.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"1089-1097"},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148279024","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 radiology-aware fuzzy deep learning framework with entropy-guided feature selection for robust multi-disease chest X-ray classification across multiple magnifications.","authors":"Mohammad Mahdi Ershadi, Zeinab Rahimi Rise, Seyed Taghi Akhavan Niaki","doi":"10.1007/s12194-026-01110-5","DOIUrl":"10.1007/s12194-026-01110-5","url":null,"abstract":"<p><p>Chest X-ray (CXR) imaging remains the most widely used and cost-effective modality for diagnosing thoracic diseases, yet automated multi-disease interpretation remains challenging due to acquisition variability, subtle overlapping pathologies, and multi-class classification complexity. Existing deep learning approaches often lack uncertainty modeling, interpretability, and robustness across heterogeneous image resolutions, limiting clinical adoption. We propose a fuzzy deep learning framework for multi-disease CXR classification, integrating: (i) radiology-aware augmentation to enhance generalization while preserving diagnostic fidelity; (ii) a grayscale-optimized ResNet-50 backbone with spatial-channel attention for improved feature extraction of subtle abnormalities; (iii) entropy-guided recursive feature elimination (RFE) achieving > 85% dimensionality reduction with minimal information loss; and (iv) a hybrid fuzzy-neural classifier with confidence-weighted defuzzification for explicit uncertainty estimation and reliable handling of borderline cases. The framework was evaluated on four public datasets-COVID-19 Radiography, Tuberculosis CXR, CXR Pneumonia, and CXR COVID-19 Pneumonia-across four magnification levels (×1, ×2, ×5, ×20). At ×20 magnification, accuracies reached 0.9593, 0.9859, 0.9831, and 0.9576, with F1-scores up to 0.9889 and recalls up to 0.9755. Even at ×1, performance remained high (accuracy 0.9401; F1-score 0.9564). Compared with the strongest baseline (CNN), the proposed model improved accuracy by 3.5-8.6%, recall by 2.3-6.7%, and F1-score by 3.8-10.4%. The fuzzy-neural integration stabilized borderline predictions, while confidence-weighted defuzzification reduced false positives. Collectively, radiology-aware augmentation, entropy-guided feature selection, and fuzzy-deep integration enable high accuracy, robustness across resolutions, and interpretable predictions, demonstrating the framework's potential for deployment in heterogeneous clinical and portable imaging environments.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"1299-1323"},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148621815","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":"Dnq-unet: a two-level fusion framework for few-shot domain adaptation in cervical cancer CTV segmentation.","authors":"Boying Li, Yihui Liu, Yanfei Pan, Yuhui Zhang","doi":"10.1007/s12194-026-01072-8","DOIUrl":"10.1007/s12194-026-01072-8","url":null,"abstract":"<p><p>To develop and validate a two-level hierarchical fusion architecture enabling efficient few-shot domain adaptation for cervical cancer clinical target volume (CTV) auto-segmentation across different institutional imaging and contouring settings. We propose DNQ-UNet, a dual-encoder architecture with two fusion levels: spatially adaptive normalization (SPADE) for shallow appearance alignment and cross-attention for deep semantic transfer. A progressive two-stage training strategy with grouped learning rates was employed. Few-shot adaptation used 3, 5, 10, 20, 40, and 60 fine-tuning cases, and additional 3D fine-tuning baselines, including nnU-Net V2, SwinUNETR, and MedNeXt, were evaluated under identical target-domain test settings. Ablation studies assessed each fusion component. On the target-domain test set, zero-shot DSC was [Formula: see text]. With 3, 5, and 10 fine-tuning cases, DSC improved to [Formula: see text], [Formula: see text], and [Formula: see text], respectively. DNQ-UNet achieved the highest DSC among the compared 3D baselines at 5-shot (0.830 vs. 0.805-0.817) and 10-shot (0.838 vs. 0.823-0.833) settings. Ablation showed significant DSC reductions after removing either fusion level or using a single U-Net (all [Formula: see text]). The proposed framework enables efficient few-shot domain adaptation for cervical cancer CTV segmentation using 5-10 annotated cases, providing a locally adapted contour-initialization approach that may reduce annotation and repeated correction burden during cross-institutional deployment.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"998-1008"},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148151689","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":"Towards integrating domain knowledge, AutoML and few-shot learning for medical image analysis: a mini review of current trends and research gaps.","authors":"Nitiyaa Ragu, Jason Teo","doi":"10.1007/s12194-026-01077-3","DOIUrl":"10.1007/s12194-026-01077-3","url":null,"abstract":"<p><p>Medical image analysis is essential for modern diagnostics, as it enables accurate and rapid disease detection. However, traditional deep learning models require large, annotated datasets, which are frequently inaccessible in medical scenarios due to data scarcity, privacy constraints, and excessive labeling costs. Few-Shot Learning (FSL) and Automated Machine Learning (AutoML) have appeared as effective techniques to address these issues. FSL utilizes meta-learning and metric-based strategies enabling models to learn from small samples, while AutoML automates model design and optimization, reducing reliance on expert intervention. Additionally, the integration of domain-specific knowledge, including anatomical priors and clinically relevant features, has demonstrated enhancements in interpretability and diagnostic significance. This mini review offers a structured analysis of FSL, AutoML, and domain-specific knowledge in medical image analysis, emphasizing their potential integration. A critical evaluation of existing literature reveals that most studies use these approaches independently. The review further examines methodological limitations, dataset constraints, and clinical applicability challenges across current studies. Based on these findings, key research gaps are identified, such as the need for domain-informed architecture search, standardized evaluation protocols, and pipelines that use less computer power. Notably, metric-based FSL approaches were more widely used than gradient-based methods due to their stability under limited data conditions. However, the literature is still methodologically fragmented, with FSL, AutoML, and domain-specific knowledge mainly studied separately or in partial combinations. The paper concludes by outlining future research directions toward the development of AutoML-enhanced FSL frameworks integrated with domain-specific knowledge to improve performance, interpretability, and clinical reliability in data-constrained environments.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"910-925"},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148176973","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":"Evaluation of a bolus-tracking method based on a pitch-correction formula for lower-extremity CT angiography.","authors":"Ryota Abe, Takanori Masuda, Hiroyuki Kurokawa, Yuma Takechi","doi":"10.1007/s12194-026-01064-8","DOIUrl":"10.1007/s12194-026-01064-8","url":null,"abstract":"<p><p>To assess the clinical utility of a novel scan-timing protocol Pitch Optimized Bolus Tracking (POBT) for lower-extremity CT angiography (CTA). The POBT method individualizes the helical pitch on the basis of patient-specific cardiac output, thereby maximizing arterial enhancement and limiting venous contamination. In this retrospective study, 75 patients who underwent lower-extremity CTA were divided into two groups. The POBT group (n = 39) employed a patient-specific pitch factor derived from echocardiographic stroke volume and heart rate, whereas the conventional bolus-tracking (BT) group (n = 36) used a fixed pitch. Quantitative and qualitative evaluations of arterial and venous contrast enhancement were performed at predefined anatomical levels. Relative to the conventional BT group, the POBT group exhibited significantly greater distal arterial enhancement (median, 302 HU vs. 265 HU; p < 0.05) and substantially lower venous contamination (5.1% vs. 31%; p < 0.05). Arterial image quality was rated \"excellent\" more frequently in the POBT group, with pronounced suppression of venous overlap. Contrast-medium volume did not differ between groups. The POBT method improves diagnostic performance in lower-extremity CTA by adapting the helical pitch to individual hemodynamics, thereby enhancing distal arterial visualization and mitigating venous contamination without increasing patient dose or contrast load.</p>","PeriodicalId":46252,"journal":{"name":"Radiological Physics and Technology","volume":" ","pages":"1473-1484"},"PeriodicalIF":1.6,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147965027","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}