Frontiers in radiology最新文献

{"title":"Editorial: Emerging fast medical imaging techniques in radiology.","authors":"Zhifeng Chen, Zhicheng Peng, Mingfeng Jiang, Jianfeng Zhou, Lijun Lu, Xiaoyun Liang","doi":"10.3389/fradi.2026.1948195","DOIUrl":"https://doi.org/10.3389/fradi.2026.1948195","url":null,"abstract":"","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1948195"},"PeriodicalIF":4.0,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13485880/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148802623","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":"Case Report: A large right atrial hemangioma: multimodal imaging and postoperative follow-up.","authors":"Tianhe Ye, Cong Liu","doi":"10.3389/fradi.2026.1821324","DOIUrl":"https://doi.org/10.3389/fradi.2026.1821324","url":null,"abstract":"<p><p>Primary cardiac tumors are extremely rare, and hemangiomas represent only a small subset, with right atrial involvement being particularly uncommon, posing diagnostic challenges due to non-specific symptoms and overlapping imaging features. We present a 55-year-old male with an incidentally discovered right atrial mass initially suspected to be a myxoma. Multimodal imaging with transthoracic echocardiography (TTE) and cardiac magnetic resonance (CMR) was performed: TTE identified a large mobile mass causing partial tricuspid obstruction, while CMR with quantitative diffusion-weighted imaging/apparent diffusion coefficient (DWI/ADC) provided detailed tissue characterization and anatomical mapping, revealing typical features of a hemangioma. Surgical resection was performed via median sternotomy under cardiopulmonary bypass. The mass, arising from the right atrial posterior wall and interatrial septum, was completely excised, and the atrial defect was repaired with a pericardial patch. Histopathology and immunohistochemistry confirmed a benign capillary hemangioma. Postoperative echocardiography demonstrated reduced right atrial diameter, resolution of obstruction, and mild stable tricuspid regurgitation, with no major complications. Combined TTE and CMR multimodal imaging is crucial for accurate preoperative diagnosis and surgical planning of right atrial hemangiomas. Complete surgical resection achieves favorable outcomes, and long-term multimodal imaging follow-up is recommended to exclude recurrence.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1821324"},"PeriodicalIF":4.0,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13467978/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765287","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":"Portal vein recanalization and transjugular intrahepatic portosystemic shunt in chronic portal vein thrombosis: a narrative review.","authors":"Valentina Mejía-Quiñones, José Gabriel Lotero-Robledo, José Miguel Hidalgo-Oviedo, Juan S Toro-Gutiérrez, Jorge Mario Sastoque-Gómez","doi":"10.3389/fradi.2026.1880540","DOIUrl":"https://doi.org/10.3389/fradi.2026.1880540","url":null,"abstract":"<p><p>Portal vein thrombosis (PVT) is a heterogeneous and clinically significant complication of liver cirrhosis, primary hepatobiliary malignancy, and various systemic prothrombotic states. It is associated with advanced portal hypertension, increased surgical complexity during liver transplantation, and higher mortality. The aim of this manuscript is to provide a narrative review of PVT and portal vein recanalization combined with transjugular intrahepatic portosystemic shunt (PVR-TIPS), detailing epidemiology, pathophysiology, and diagnostic classifications while exploring evolving medical and interventional management strategies. A comprehensive narrative review of the existing literature was conducted, incorporating clinical practice guidelines from major societies (AASLD, Baveno VII, EASL, and AGA) and recent prospective and retrospective studies regarding the management of non-malignant PVT in both cirrhotic and non-cirrhotic patients. PVT occurs in approximately 1% of the general population but affects up to 44% of candidates for liver transplantation (LT). Its development is governed by Virchow's triad, with reduced portal flow velocity (<15 cm/s) being the primary independent predictor in cirrhosis. Diagnosis relies on Doppler ultrasound as a first-line screening tool, followed by contrast-enhanced computed tomography or magnetic resonance imaging to define the degree of occlusion, extent, and chronicity using standardized nomenclature. Medical management with anticoagulation is the cornerstone for recent PVT (<6 months), achieving recanalization in 40%-75% of cases. For chronic obliterative PVT or refractory complications (variceal bleeding or refractory ascites), PVR-TIPS has emerged as a safe and highly successful intervention, with technical success rates ranging from 75% to 100% in expert centers. PVR-TIPS facilitates physiologic end-to-end portal anastomosis during LT in up to 96%-100% of cases, significantly improving transplant feasibility and outcomes. The management of PVT has transitioned toward highly individualized strategies. Standardized classification remains essential for guiding therapy. While medical management is effective for recent-onset disease, PVR-TIPS represents a transformative interventional advancement for chronic obliterative PVT, restoring physiologic flow and optimizing candidates for curative liver transplantation.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1880540"},"PeriodicalIF":4.0,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13469255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148763887","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":"Pericardial lipoma encasing the right coronary artery: a multimodality imaging case report.","authors":"Xiaoyang Liu, Ziming Liu, Xiuyun Luo, Wei Miao","doi":"10.3389/fradi.2026.1914611","DOIUrl":"https://doi.org/10.3389/fradi.2026.1914611","url":null,"abstract":"<p><strong>Background: </strong>Pericardial lipomas are rare, benign cardiac tumors. Encasement of a coronary artery by such a tumor is extremely uncommon and significantly elevates surgical risk. We report the first case in which multimodality imaging clearly demonstrates a pericardial lipoma enveloping the right coronary artery (RCA) without luminal stenosis, adding a novel anatomical observation to the literature.</p><p><strong>Case presentation: </strong>A 32-year-old woman presented with a one-year history of exertional chest tightness and dyspnea that had worsened over three days. Physical examination and laboratory tests were unremarkable except for elevated thyroid-stimulating hormone (13.26 μIU/mL). Echocardiography revealed a 7.8 cm hypoechoic mass above the aortic root with a segment of the RCA coursing within it; color Doppler showed no flow disturbance. Contrast-enhanced cardiac computed tomography (CT) and cardiac magnetic resonance (CMR) confirmed a homogeneous fat-density mass (CT attenuation -85 HU, no enhancement, complete signal suppression on STIR) consistent with a pericardial lipoma. The tumor measured 5.5 cm in maximum transverse diameter and was seen to completely encase the RCA without stenosis. A diagnosis of pericardial lipoma with RCA encasement and concomitant subclinical hypothyroidism was made. After multidisciplinary discussion, levothyroxine was initiated and a staged plan including preoperative coronary angiography followed by surgical resection with RCA protection was formulated. The patient's symptoms resolved at discharge. At one- and three-month follow-ups she remained asymptomatic, but she subsequently declined all further contact and was lost to follow-up; no adverse cardiac events were recorded during the observation period.</p><p><strong>Conclusion: </strong>Pericardial lipomas can encase a coronary artery while maintaining luminal patency due to the compliance of adipose tissue. When a pericardial mass is detected on echocardiography, the coronary course must be actively traced. Multimodality imaging with CT and cardiac magnetic resonance (CMR) is essential for delineating the tumor-artery relationship and guiding surgical strategy. Coexisting endocrine disorders should be corrected preoperatively. Although the loss of long-term follow-up limits our conclusions, the imaging findings themselves provide an important cautionary lesson.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1914611"},"PeriodicalIF":4.0,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13470255/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148765589","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":"Development and multicenter external validation of a deep learning model for early screening of thoracic ossification of the ligamentum flavum on routine chest radiographs.","authors":"Zichuan Wu, Juehan Wang, Xuhong Zhang, Baifeng Sun, Min Qi, Yang Liu","doi":"10.3389/fradi.2026.1867740","DOIUrl":"10.3389/fradi.2026.1867740","url":null,"abstract":"<p><strong>Background: </strong>Thoracic ossification of the ligamentum flavum (TOLF) is frequently underrecognized in its early stage because radiographic abnormalities on routine chest radiographs are often subtle. We aimed to develop and externally validate a deep learning model for opportunistic screening of TOLF using routine chest radiographs.</p><p><strong>Methods: </strong>This retrospective multicenter diagnostic study included an internal development cohort from Changzheng Hospital and an independent external validation cohort from South China Hospital. The internal cohort comprised 250 patients with TOLF and 250 control subjects collected between January 2017 and January 2023. The external cohort comprised 150 patients with TOLF and 150 control subjects. TOLF status was established on CT using predefined radiological criteria, whereas frontal and lateral chest radiographs were used only as model inputs. We evaluated multiple backbone architectures, including ResNet101, DenseNet169, Vision Transformer, and Swin Transformer, and additionally explored three dual-view fusion strategies. Model development was performed using 10-fold cross-validation in the internal cohort, and performance was summarized using bootstrap-derived 95% confidence intervals. Human-reader comparison was conducted in the internal cohort.</p><p><strong>Results: </strong>In backbone screening within the internal cohort, ResNet101 emerged as the best-performing architecture. After subsequent input-resolution optimization, the final lateral-view ResNet101 model achieved an accuracy of 97.0%, sensitivity of 94.0%, specificity of 100.0%, and an AUC of 0.970. None of the evaluated dual-view fusion strategies outperformed the best single lateral-view model, and the poorer performance of posterior-fusion models was mainly attributable to reduced sensitivity. Compared with experienced spine surgeons and imaging physicians, the internal ResNet101 model showed significantly higher sensitivity and overall accuracy (both <i>p</i> < 0.001). In the external validation cohort, the locked model maintained robust discrimination, with an AUC of 0.954 for frontal radiographs and 0.995 for lateral radiographs. The corresponding accuracy/sensitivity/specificity values were 90.0%/84.7%/95.3% for frontal radiographs and 93.7%/89.3%/98.0% for lateral radiographs.</p><p><strong>Conclusion: </strong>A deep learning model based on routine chest radiographs may provide accurate and generalizable screening for TOLF across institutions. The lateral-view model showed the most consistent diagnostic performance, supporting its potential role as an opportunistic screening tool to prompt confirmatory CT evaluation.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1867740"},"PeriodicalIF":4.0,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13461901/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148724287","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":"FedAD: adaptive federated disentanglement for domain generalization in unlabeled medical imaging.","authors":"Swetha Kodhandaraman, Hemalatha K, Anandita Prabhakar, Shreyas Kempapuram","doi":"10.3389/fradi.2026.1829701","DOIUrl":"10.3389/fradi.2026.1829701","url":null,"abstract":"<p><p>Three barriers significantly hinder the use of deep learning in medical imaging: poor generalization to new clinical domain shifts, label scarcity, and data privacy. A unified framework that learns from unlabeled, decentralized data while optimizing for generalization is desperately needed, even if Federated Learning (FL), Self-Supervised Learning (SSL), and Domain Generalization (DG) provide partial solutions that often operate under contradictory assumptions. We present FedAD: Adaptive Federated Disentanglement, a unique framework that uses two key ideas to handle these problems in a synergistic way. First, a federated semantic disentanglement objective (FedSD) explicitly distinguishes between the domain-invariant semantic characteristics and domain-specific variants using a non-adversarial orthogonality constraint. Second, in order to prevent premature convergence and enhance resilience, an adaptive teacher-student alignment (ATSA) curriculum dynamically modifies the generalization pressure based on the stability of the global model. This dual technique creates a strong feature encoder by forcing the model to learn what it sees as opposed to where it sees it. FedAD outperforms current approaches in terms of generalization to unseen target domains, as demonstrated by its validation on publicly available medical datasets. Our strategy concurrently addresses privacy, label scarcity, and domain change, paving the road for useful, reliable, and fair medical AI.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1829701"},"PeriodicalIF":4.0,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13464539/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148726613","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":"Artificial intelligence for lung ultrasound interpretation: a systematic review.","authors":"Julia López-Canay, Alberto Fernández-Villar, Cristina Ramos-Hernández, Alberto Fernández-García, Maribel Botana-Rial, Manuel Casal-Guisande","doi":"10.3389/fradi.2026.1899103","DOIUrl":"10.3389/fradi.2026.1899103","url":null,"abstract":"<p><strong>Context and objectives: </strong>Lung ultrasound (LUS) is a safe low-cost tool that enables diagnosis, monitoring and guidance for interventional procedures at the patient's bedside. However, its expansion is hindered by a lack of training programs and the inherent difficulty of interpreting ultrasound images. In this context, Artificial Intelligence (AI) is emerging as a supportive tool for LUS interpretation, ensuring diagnostic efficacy and mitigating the shortage of experts. This systematic review aims to summarize and analyze recent advances in AI-based tools to support LUS interpretation.</p><p><strong>Methods: </strong>A systematic literature search was conducted across Web of Science, IEEE Xplore, and PubMed databases to identify peer-reviewed original journal articles published between 2015 and November 2025 that employed AI for the identification and localization of lung artifacts, anatomical structures, and pathological findings. Risk of bias was assessed using PROBAST + AI.</p><p><strong>Results: </strong>Twenty-four studies were included, identifying three main strategies: segmentation (10 studies), object detection (4 studies), and the generation of visual explanations through saliency maps (10 studies). All employed CNN-based architectures. The evaluation metrics used were heterogeneous. The PROBAST + AI assessment showed relevant risk-of-bias concerns, mainly concentrated in the participants and analysis domains.</p><p><strong>Conclusions: </strong>The development of AI systems to support LUS interpretation shows high potential; however, current studies exhibit significant heterogeneity in their objectives, methodologies, and evaluation metrics. It is necessary to move towards solutions designed for specific clinical environments and to adopt standardized protocols and evaluations that facilitate their implementation in clinical practice.</p><p><strong>Systematic review registration: </strong>https://www.crd.york.ac.uk/PROSPERO/view/CRD420261322517, PROSPERO CRD420261322517.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1899103"},"PeriodicalIF":4.0,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13461599/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148725585","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":"Swin-Qwen3: a three-stage vision-language framework for automated radiology report generation with multi-agent verification.","authors":"Hamida Abdaoui, Sabri Barbaria, Ahmed Al Kuwaiti, Noureddine Raouafi, Ridha Ben Salah, Hanene Boussi Rahmouni","doi":"10.3389/fradi.2026.1875905","DOIUrl":"10.3389/fradi.2026.1875905","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Automated chest x-ray reporting could substantially reduce the burden on radiology services worldwide; however, the implementation of current vision-language models (VLMs) in clinical workflows remains limited by factual errors, hallucinations, and inadequate clinical reliability. Bridging this implementation gap requires frameworks that are not only technically sound but also designed for safe integration into real-world healthcare settings.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;This study aimed to improve the clinical accuracy and factual consistency of radiology report generation by introducing an agent-inspired, three-stage reasoning framework and evaluating its feasibility as research prototype for potential implementation in resource-constrained and high-throughput clinical environments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We propose the Swin-Qwen3 vision-language architecture, which integrates a Swin Transformer visual encoder with a Q-Former-style query-driven cross-attention module aligned with a large language model (Qwen3-0.6B). A three-stage generation strategy-comprising initial report drafting, clinical verification, and structured refinement-was guided by role-specific prompts to simulate distinct clinical reasoning behaviours. Parameter-efficient fine-tuning via low-rank adaptation (LoRA) enabled training within standard GPU constraints. The framework was evaluated on the full IU x-Ray test set (321 Samples) using lexical, semantic, clinical, and factuality metrics and compared with one- and two-stage ablations. Computational feasibility and inference overhead were assessed for offline or batch processing contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;The three-stage framework showed modest but consistent improvements over ablation baselines. CheXpert-F1 reached 0.7038 (compared to 0.7154 for one-stage and 0.7123 for two-stage), Clinical-F1 reached 0.5401 (compared to 0.5478 for one-stage and 0.5449 for two-stage), and RadGraph-F1 scored 0.5467 (compared to 0.5478 for one-stage and 0.5445 for two-stage). The factuality score reached 0.843 (compared to 0.845 for one-stage and 0.845 for two-stage), while the hallucination rate remained at 0.6116 (compared to 0.6109 for one-stage and 0.6109 for two-stage). Clinical recall decreased from 0.8361 (one-stage) and 0.8337 (two-stage) to 0.8044 (three-stage), reflecting a trade-off between sensitivity and precision. However, the clinical verifier demonstrated substantial effectiveness, correcting 85.6% of identified hallucinations. Lexical quality improved: BLEU-4 was 0.0628 (+10.9% vs. one-stage; +13.1% vs. two-stage), ROUGE-2-F was 0.0957 (+10.2% vs. one-stage; +13.1% vs. two-stage), and METEOR was 0.3728 (+4.5% vs. one-stage; +6.3% vs. two-stage). Improvements in METEOR (&lt;i&gt;p&lt;/i&gt; &lt; 0.001) and ROUGE-2-F (&lt;i&gt;p&lt;/i&gt; &lt; 0.05) were statistically significant. A 14.8 ×  inference overhead was identified, indicating suitability for offline reporting contexts.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Concl","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1875905"},"PeriodicalIF":4.0,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13458744/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148714440","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":"Image-conditioned latent rectified flow models for 3D medical anomaly localisation.","authors":"Matthew Baugh, Johanna P Müller, Sarah Cechnicka, Kexin Gu Baugh, John Bonnici, James Myles, Bernhard Kainz","doi":"10.3389/fradi.2026.1847193","DOIUrl":"10.3389/fradi.2026.1847193","url":null,"abstract":"<p><strong>Introduction: </strong>Reconstruction-based methods offer a promising solution for unsupervised anomaly detection in medical imaging tasks. These methods train generative models on healthy data alone and identify anomalies as deviations between an input image and its pseudo-healthy reconstruction. The downfall of these methods is their dependence on two assumptions that often fail in practice: that models cannot reproduce unseen pathologies, yet can faithfully reconstruct healthy tissue. A recent image-conditioned diffusion approach explicitly addresses these issues by training a model to restore synthetic anomalies inserted into healthy images. However, it operates in 2D pixel space, discarding inter-slice context and incurring high computational cost.</p><p><strong>Methods: </strong>We address both limitations by performing image-conditioned restoration in a 3D latent space using a pretrained VAE and rectified flow, capturing volumetric context whilst drastically reducing computational overhead. To mitigate false positives introduced by VAE compression, we propose using the restoration change which measures the difference between the pseudo-healthy latent restoration and the VAE reconstruction of the original, rather than the standard reconstruction error. We further experiment with applying the synthetic anomaly training task directly in latent space to improve sensitivity to low-contrast anomalies.</p><p><strong>Results: </strong>We perform extensive experiments across various medical imaging benchmarks, including brain MRI and the newly released AADD dataset, comparing against reconstruction-based, feature-modelling, attention-based and self-supervised anomaly detection methods. Our image-conditioned rectified flow models establish a new state-of-the-art, with an ensemble of models trained with pixel-space and latent-space anomalies yielding the strongest overall performance.</p><p><strong>Discussion: </strong>These results demonstrate how incorporating 3D context enables better anomaly detection performance whilst also being ∼10 times faster. The difference in performance between models trained using latent-space and pixel-space anomalies suggests that further broadening of the anomaly imputation process could continue to improve the robustness of these models. Such improvements are certainly necessary, as the AADD benchmark is far from being saturated. Code is available at https://github.com/matt-baugh/img-cond-latent-rflow-model-ad.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1847193"},"PeriodicalIF":4.0,"publicationDate":"2026-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13447378/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148690519","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":"Case Report: Mechanical thrombectomy of cerebral venous sinuses using the NeVa stent retriever.","authors":"María Estefanía Peña Giraldo, Andres Hernando Tascon Barona, Ana María Granados Sánchez, Edgar Andres Folleco","doi":"10.3389/fradi.2026.1875234","DOIUrl":"10.3389/fradi.2026.1875234","url":null,"abstract":"<p><p>Cerebral venous sinus thrombosis (CVST) accounts for less than 1% of cerebrovascular events worldwide and is associated with significant morbidity; in severe or anticoagulation-refractory cases, mechanical thrombectomy is considered, yet no consensus exists on optimal device selection. We report the case of a 32-year-old woman with a history of migraine and oral contraceptive use who presented with sudden-onset headache on awakening, disorientation, and right hemiparesis, followed by focal seizures and National Institutes of Health Stroke Scale (NIHSS) progression from 3 to 7. Magnetic resonance venography confirmed extensive CVST involving the superior sagittal sinus and bilateral transverse and sigmoid sinuses. Mechanical thrombectomy was performed on hospital day 1 via a right jugular approach using a combined aspiration and NeVa stent retriever (5.5 × 37 mm) technique with a 6-second integration time; after six passes, near-complete recanalization of the superior sagittal sinus was achieved with restoration of antegrade venous drainage and no intraprocedural complications. The post-procedural course included neurological improvement (Medical Research Council grade 4/5) and secondary intracranial hypertension (opening pressure 33 cmH₂O), which was managed conservatively. The patient was discharged after 12 days of hospitalization, with a modified Rankin Scale score of 0 at the 34-day follow-up visit. To our knowledge, this represents the first report of off-label NeVa stent retriever use for thrombectomy in the cerebral venous system; the device's higher mesh density may theoretically offer advantages for organized venous thrombi in large-caliber sinuses, expanding therapeutic options where consensus on optimal technique remains elusive.</p>","PeriodicalId":73101,"journal":{"name":"Frontiers in radiology","volume":"6 ","pages":"1875234"},"PeriodicalIF":4.0,"publicationDate":"2026-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13442897/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686777","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}