iRadiology最新文献

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 2","pages":"147-156"},"PeriodicalIF":0.0,"publicationDate":"2026-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148073262","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 2","pages":"127-136"},"PeriodicalIF":0.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148061817","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 2","pages":"113-126"},"PeriodicalIF":0.0,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148097811","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 2","pages":"182-183"},"PeriodicalIF":0.0,"publicationDate":"2026-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148097810","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":"Giant Cell Reparative Granuloma of the Left Skull Base and Infratemporal Fossa","authors":"Pinshan Zhang","doi":"10.1002/ird3.70053","DOIUrl":"https://doi.org/10.1002/ird3.70053","url":null,"abstract":"<p>A 56-year-old woman presented with a 1-year history of unexplained left facial swelling and pain associated with a self-palpated, firm, and fixed mass. Noncontrast and contrast-enhanced computed tomography (CT) of the mastoid revealed a slightly hyperdense mass measuring approximately 3.7 cm × 6.4 cm × 3.4 cm in the left cranial base–infratemporal fossa, with heterogeneous internal density and irregular, lobulated morphology. The mass encircled the left temporomandibular joint during growth and showed indistinct borders with the masseter, medial pterygoid, and lateral pterygoid muscles. Expansile bone destruction involved the left mandible, condyle, zygomatic arch, sphenoid bone, temporal bone, and medial and lateral pterygoid plates, suggesting invasiveness; delayed heterogeneous enhancement was seen postcontrast. Noncontrast and contrast-enhanced magnetic resonance imaging (MRI) demonstrated a patchy low-signal intensity lesion in the left cranial base–temporal fossa. Low signal intensity was observed on T1-weighted imaging, T2-weighted imaging, T2-fluid-attenuated inversion recovery, and diffusion-weighted imaging sequences, with no postcontrast enhancement. The patient underwent left cranial base–maxillofacial mass resection under general anesthesia. Postoperative pathology confirmed giant cell reparative granuloma. Giant cell reparative granuloma is a rare benign bone lesion with local invasiveness, most commonly affecting the jawbones. In this case, CT showed soft tissue mass formation with expansile bone destruction, whereas MRI demonstrated uniformly low signal intensity across all sequences. These findings reflect the fibrous matrix and hemosiderin deposition characteristic of the lesion and provide important radiological clues. Recognition of these imaging features aids differentiation from malignant lesions; however, definitive diagnosis requires histopathological examination (Figure 1).</p><p><b>Pinshan Zhang:</b> conceptualization, writing – original draft, writing – review and editing.</p><p>The author has nothing to report.</p><p>The author has nothing to report.</p><p>The patient provided written informed consent at the time of entering this study.</p><p>The author declares no conflicts of interest.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 1","pages":"66-67"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70053","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637139","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":"β-Galactosidase Based Molecular Imaging for Visualization of Cellular Senescence","authors":"Jiani Huang,&nbsp;Peili Cen,&nbsp;Hong Zhang,&nbsp;Yan Zhong","doi":"10.1002/ird3.70051","DOIUrl":"https://doi.org/10.1002/ird3.70051","url":null,"abstract":"&lt;p&gt;Cellular senescence is a distinct and irreversible biological process characterized by cell cycle arrest. It can be triggered by various stressors, including DNA damage, oxidative stress, telomere dysfunction, oncogenic activation, or extensive replication as well as by physiological stimuli such as developmental and repair signals [&lt;span&gt;1&lt;/span&gt;]. Cellular senescence plays dual roles in physiology and pathology, contributing to processes such as tissue regeneration, wound healing, tumor suppression, fibrosis, aging, and age-related diseases [&lt;span&gt;2&lt;/span&gt;]. Beyond cell cycle arrest, cellular senescence entails multiple hallmarks of phenotypic changes, including DNA damage response, upregulated antiapoptotic pathways, senescence-associated secretory phenotype, and increased lysosomal content [&lt;span&gt;1&lt;/span&gt;]. Among these features, senescence-associated β-galactosidase (SA-β-gal) is highly enriched in lysosomes and has become the most widely used biomarker for detecting senescent cells. Therefore, sensitive and specific detection of SA-β-gal is critical not only for elucidating the biological mechanisms of aging but also for guiding therapeutic strategies against senescence-associated diseases.&lt;/p&gt;&lt;p&gt;Molecular imaging offers a powerful noninvasive approach for real-time in vivo monitoring of senescent cells. It encompasses diverse modalities such as optical imaging, ultrasound, computed tomography, magnetic resonance imaging (MRI), single photon emission computed tomography (SPECT), and positron emission tomography (PET). These techniques enable dynamic visualization and quantification of cellular and molecular events, providing insight into the biological behavior and accumulation of senescent cells in aging and disease [&lt;span&gt;3&lt;/span&gt;]. Given the elevated activity of SA-β-gal in senescent cells, it has been extensively explored as a molecular target for probe development, with fluorescence and PET emerging as the most actively investigated strategies.&lt;/p&gt;&lt;p&gt;Fluorescence imaging has garnered significant attention due to its high sensitivity, specificity, spatial temporal resolution, and real-time feedback. To date, fluorescent β-gal probes have been the most extensively studied modality for cellular senescence detection both in vitro and in vivo, enabling dynamic tracking of SA-β-gal activity [&lt;span&gt;4&lt;/span&gt;]. These probes are typically designed as enzyme-specific substrates that undergo β-gal-mediated cleavage, thereby activating fluorescence in senescent cell microenvironments. For example, Hu et al. designed a near-infrared (NIR) fluorescent probe (XZ1208) with high sensitivity and selectivity against β-gal, which enabled the visualization of senescence in irradiated, naturally aged, and prematurely aged mouse models, as well as in fibrosis and wound-healing contexts [&lt;span&gt;5&lt;/span&gt;]. Rojas-Vázquez et al. reported sulfonic-Cy7Gal, a renal-cleared β-gal-responsive probe allowing noninvasive urinary monitoring of senescence, which correlate","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 1","pages":"63-65"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70051","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637177","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":"Transforming Skin Cancer Detection With AI-Based Convolutional and Transformer Models","authors":"Selorm Adablanu,&nbsp;Utpal Barman,&nbsp;Dulumani Das,&nbsp;Tuward Jade Dweh","doi":"10.1002/ird3.70058","DOIUrl":"https://doi.org/10.1002/ird3.70058","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Skin cancer is a major cause of mortality, and early detection is vital for effective treatment. Diagnosis is challenging because of lesion variability. This study adapts VINCE-NET, a hybrid deep-learning model originally designed for stroke detection, to classify melanoma using dermoscopic images.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>VINCE-NET combines vision transformer layers for global context, convolutional neural networks for local features, and long short-term memory for spatial sequence modeling. During preprocessing, Gaussian blur, normalization, and augmentation were applied to reduce noise and handle class imbalance. During training, the public HAM10000 dataset was used in a central processing unit-only Google Colab environment (12.72 GB random access memory, 107.7 GB disk) with an AdamW optimizer, a batch size of 12, learning-rate scheduling, and early stopping (patience = 50). VINCE-NET's performance was compared with those of a convolutional neural networks, long short-term memory, residual network with 50 layers (ResNet-50), visual geometry group network with 16 and 19 layers (VGG-16/19), and densely connected convolutional network with 121 and 201 layers (DenseNet-121/201) under identical preprocessing conditions.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>VINCE-NET achieved 94.1% accuracy, 95.5% precision, 90.4% recall, a 92.9% F1-score, and an area under the receiver operating characteristic curve of 0.98 at a training time of 34,308.42 s. Benchmarks showed that VINCE-NET outperformed baselines while being computationally efficient.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>VINCE-NET provides competitive performance for melanoma classification and feasibility in resource-limited settings. Although promising, VINCE-NET has not been clinically validated yet. Future work will address resolution, ablation studies, interpretability, and external validation.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 1","pages":"51-62"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70058","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147636970","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":"Exploring a Novel Conv-Transformer Network for Multi-Modality Heart Segmentation","authors":"Youyou Ding,&nbsp;Hao Dang,&nbsp;Jiayi Luo,&nbsp;Xiaoyu Zhuo,&nbsp;Ningyu Huang,&nbsp;Junsheng Xiao,&nbsp;Zongwang Lv","doi":"10.1002/ird3.70028","DOIUrl":"https://doi.org/10.1002/ird3.70028","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>In recent years, deep convolutional neural networks (CNNs) have achieved great successes in medical imaging. However, it is difficult to obtain accurate pathological information for clinical diagnosis and treatment by leveraging single-modality medical images. This study aims to provide an efficient multimodality whole heart segmentation method for the diagnosis of coronary heart disease.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We propose SFAM-TransUnet for multimodality whole heart segmentation, a novel deep learning framework combining CNNs and transformers. Primarily, the method integrates CNNs and visual transformers (Vits) into a unified fusion framework. Specifically, the shallow feature fusion module is designed to connect MRI and CT images, thereby providing a powerful and efficient multimodality fusion backbone for semantic segmentation. Furthermore, we propose a fusion ViT (FViT) module including self-attention (SA) and adaptive mutual boost attention (Ada-MBA) to enhance contextual information within and across modalities. The Ada-MBA module assigns attention to semantic perception regions by calculating SA and cross-attention, which improves the ability to understand context from the different modalities. Extensive experiments are conducted on the clinical Multi-Modality Whole Heart Segmentation datasets.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>We successfully improved the whole heart segmentation DSCs to 0.902 (AA), 0.920 (LV-blood), 0.863 (LA-blood), and 0.837 (LV-myo), the HDs to 9.886 (AA), 9.947 (LV-blood), 11.911 (LA-blood), and 13.599 (LV-myo), the PSNR values to 33.577 (AA), 30.091 (LV-blood), 32.055 (LA-blood), and 29.837 (LV-myo), SSMI values to 0.901 (AA), 0.818 (LV-blood), 0.765 (LA-blood), and 0.743 (LV-myo). This demonstrate SFAM-TransUnet outperforms various alternative methods.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>We propose SFAM-TransUnet, an efficient framework tailored for whole heart segmentation that combines CNNs and transformers. It provides a powerful multimodality fusion network to improve the performance of whole heart semantic segmentation. These results demonstrate the efficacy of SFAM-TransUnet in integrating relevant information between different modalities in multimodal tasks.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 1","pages":"13-22"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637037","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":"Relationship Between Blood Pressure Changes and Evolution of White Matter Hyperintensity Volume on Magnetic Resonance Imaging","authors":"Chenzhen Shen,&nbsp;Hui Xu,&nbsp;Jianfeng Qiu","doi":"10.1002/ird3.70052","DOIUrl":"https://doi.org/10.1002/ird3.70052","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Hypertension may be a cause of white matter hyperintensity (WMH), but how changes in blood pressure relate to changes in WMH remains unclear. This study aims to clarify the relationship between them.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Clinical data were retrospectively collected from 466 patients who underwent two cranial magnetic resonance imaging examinations with the lesion segmentation toolbox at 11- to 14-month intervals at the Second Affiliated Hospital of Shandong First Medical University. Patients were categorized according to baseline clinical data, including sex, systolic blood pressure (SBP), age, and Fazekas score. WMH volume (WMHv) was measured on T1-FLAIR and T2-FLAIR images using MATLAB R2019b software. WMHv before and after magnetic resonance imaging, along with related clinical data, were analyzed using IBM SPSS version 26. Nonparametric independent-samples tests were used to assess the relationship between WMHv and SBP within each group.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>WMH was associated with initial age (<i>r</i> = 0.22, <i>p</i> &lt; 0.05), number of lesions (<i>r</i> = 0.62, <i>p</i> &lt; 0.05), and Fazekas score (<i>r</i> = 0.70, <i>p</i> &lt; 0.05); the second age (<i>r</i> = 0.25, <i>p</i> &lt; 0.05), number of lesions (<i>r</i> = 0.67, <i>p</i> &lt; 0.05), and Fazekas score (<i>r</i> = 0.70, <i>p</i> &lt; 0.05) were also associated with WMH whereas sex showed no significant effect (<i>d</i> ≈ −0.095, <i>p</i> &gt; 0.05). Further analysis revealed that the initial number of lesions (<i>r</i> = 0.16, <i>p</i> &lt; 0.05) and Fazekas score (<i>r</i> = 0.09, <i>p</i> &lt; 0.05) were positively associated with WMHv change; the second number of lesions (<i>r</i> = 0.31, <i>p</i> &lt; 0.05) and Fazekas score (<i>r</i> = 0.22, <i>p</i> &lt; 0.05) were also associated with WMHv change. With fluctuating blood pressure, WMHv changes followed a consistent trend (<i>p</i> &gt; 0.05). Only when the Fazekas score was 1 or 2, did blood pressure changes significantly affect WMHv (Adj. <i>p</i> &lt; 0.05). Pairwise comparative analysis showed that only Fazekas score 1 was statistically significant.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>At Fazekas score 1, adjusting SBP may help regulate WMHv. At higher Fazekas scores, SBP change showed no effect on WMHv.</p>\u0000 </section>\u0000 </div>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 1","pages":"40-50"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70052","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637138","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":"Integrating Nuclear Medicine Services Into Health Care Systems in Low- and Middle-Income Countries: A Review of Challenges and Innovations","authors":"Biruk Demisse Ayalew,&nbsp;Saim Mahmood Khan,&nbsp;Helina K. Teklehaimanot,&nbsp;Zebeaman Tibebu Gorfu,&nbsp;Biniyam Jemaneh Batu,&nbsp;Zemichael Getu Alemayehu,&nbsp;Brook Lelisa Sime,&nbsp;Eyerusalem Kebede Zewde,&nbsp;Temesgen Mamo Sharew,&nbsp;Muhidin Ibrahim Hundisa,&nbsp;Yeamlak Tariku Tewodros,&nbsp;Jawairya Muhammad Hussain,&nbsp;Barina Khan","doi":"10.1002/ird3.70054","DOIUrl":"https://doi.org/10.1002/ird3.70054","url":null,"abstract":"<p>The burden of noncommunicable diseases is increasing rapidly in low- and middle-income countries creating a growing need for advanced diagnostic and therapeutic modalities. Nuclear medicine offers great potential in disease detection, treatment planning, and monitoring, yet its integration into resource-limited health systems remains challenging. This review synthesizes evidence from peer-reviewed publications and relevant reports from international agencies to examine barriers to, and enablers of, nuclear medicine adoption in these settings. We found that key obstacles include financial constraints, restricted access to essential materials, insufficient regulatory frameworks, and shortages of skilled professionals. These gaps contribute to safety concerns, inadequate waste management, and delays in service delivery. Although global initiatives have strengthened workforce training and promoted regulatory harmonization, persistent issues in financial sustainability and retention of trained staff hinder progress. Technological advances, such as novel imaging and therapeutic approaches, present opportunities; however, their successful implementation requires context-specific strategies that align with local infrastructure and policy realities. Integrating nuclear medicine into health systems in low-resource environments can address multiple health care priorities simultaneously, but this will require targeted investment, sustainable financing mechanisms, and strengthened institutional capacity. Collaborative international support, coupled with locally adapted policies, could accelerate equitable access and improve patient outcomes. Expanding the role of nuclear medicine in these regions has the potential to significantly enhance health care delivery and contribute to closing the global disparity in advanced medical services.</p>","PeriodicalId":73508,"journal":{"name":"iRadiology","volume":"4 1","pages":"3-12"},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ird3.70054","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147637154","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}