European Radiology Experimental最新文献

{"title":"Flexible and wireless metasurface coils for knee and elbow MRI.","authors":"Daniel M Düx, Robert Kowal, Lucas Knull, Simon Schröer, Othmar Belker, Dominik Horstmann, Moritz Gutt, Holger Maune, Oliver Speck, Frank Wacker, Bennet Hensen, Marcel Gutberlet","doi":"10.1186/s41747-024-00549-8","DOIUrl":"10.1186/s41747-024-00549-8","url":null,"abstract":"<p><strong>Background: </strong>Metasurface coils (MCs) are a promising magnetic resonance imaging (MRI) technology. Aiming to evaluate the image quality of MCs for knee and elbow imaging, we compared signal-to-noise ratio (SNRs) obtained in standard clinical setups.</p><p><strong>Methods: </strong>Knee and elbow MRI routine sequences were applied at 1.5 T, implementing four coil scenarios: (1) 15-channel transmit/receive knee coil; (2) four-channel multipurpose coil (flex coil); (3) MC + spine coil; and (4) MC + multipurpose coil. Three regions of interest (ROIs) at different anatomical depths were compared.</p><p><strong>Results: </strong>Seven participants (aged 28 ± 2 years; 6 males) were enrolled. In elbow MRI, the MC + spine coil demonstrated the highest SNR across all ROIs (superficial-anterior: +114%, p = 0.008; middle: +147%, p = 0.008; deep-posterior: +24%, p = 0.039) compared to the flex coil and all ROIs, except the deepest from the MC, compared to the knee coil (superficial-anterior: +28%, p = 0.016; middle: +104%, p = 0.008; deep-posterior: -1%, p = 0.531). In knee MRI, the MC + spine coil provided higher SNR compared to the flex coil, except posterior (superficial-anterior: +69%, p = 0.008; middle: +288%, p = 0.008; deep-posterior: -12%, p = 0.148) versus the knee coil, the MC + spine coil was superior in the middle but non-different in superficial pre-patellar areas and less in deep-posterior areas (superficial-anterior: -8%, p = 0.188; middle: +44%, p = 0.008; deep-posterior: -36%, p = 0.016).</p><p><strong>Conclusion: </strong>Wireless MCs exhibited great potential for knee and elbow MRI outperforming the flex coil. Future developments will improve the posterior illumination to increase its clinical value.</p><p><strong>Relevance statement: </strong>MCs offer enhanced versatility, flexibility, and patient comfort. If universal MC designs can achieve image quality comparable to those of standard coils and simultaneously be utilized across multiple body areas, the technology may revolutionize future musculoskeletal MRIs.</p><p><strong>Key points: </strong>MCs are promising in MRI, but homogeneity is challenging depending on the design. Signal-to-noise-ratio was improved for knee and elbow imaging with slight inhomogeneous illumination. MCs could match the image quality of standard coils in both knee and elbow imaging.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"13"},"PeriodicalIF":3.7,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11782736/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068578","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":"Photon-counting detector CTA to assess intracranial stents and flow diverters: an in vivo study with ultrahigh-resolution spectral reconstructions.","authors":"Frederic De Beukelaer, Sophie De Beukelaer, Laura L Wuyts, Omid Nikoubashman, Mohammed El Halal, Iliana Kantzeli, Martin Wiesmann, Hani Ridwan, Charlotte S Weyland","doi":"10.1186/s41747-025-00550-9","DOIUrl":"10.1186/s41747-025-00550-9","url":null,"abstract":"<p><strong>Background: </strong>To define optimal parameters for the evaluation of vessel visibility in intracranial stents (ICS) and flow diverters (FD) using photon-counting detector computed tomography angiography (PCD-CTA) with spectral reconstructions.</p><p><strong>Methods: </strong>We retrospectively analyzed consecutive patients with implanted ICS or FD, who received a PCD-CTA between April 2023 and March 2024. Polyenergetic, virtual monoenergetic, pure lumen, and iodine reconstructions with different keV levels (40, 60, and 80) and reconstruction kernels (body vascular [Bv]48, Bv56, Bv64, Bv72, and Bv76) were evaluated by two radiologists with regions of interests and Likert scales. Reconstructions were compared in descriptive analysis.</p><p><strong>Results: </strong>In total, twelve patients with nine FDs and six ICSs were analyzed. In terms of quantitative image quality, sharper kernels as Bv64 and Bv72 yielded increased image noise and decreased signal-to-noise and contrast-to-noise ratios compared to the smoothest kernel Bv48 (p = 0.001). Among the different keV levels and kernels, readers selected the 40 keV level (p = 0.001) and sharper kernels (in the majority of cases Bv72) as the best to visualize the in-stent vessel lumen. Assessing the different spectral reconstructions virtual monoenergetic and iodine reconstructions proved to be best to evaluate in-stent vessel lumen (p = 0.001).</p><p><strong>Conclusion: </strong>PCD-CTA and spectral reconstructions with sharper reconstruction kernels and a low keV level of 40 seem to be beneficial to achieve optimal image quality for the evaluation of ICS and FD. Iodine and virtual monoenergetic reconstructions were superior to pure lumen and polyenergetic reconstructions to evaluate in-stent vessel lumen.</p><p><strong>Relevance statement: </strong>PCD-CTA offers the opportunity to reduce the need for invasive angiography serving as follow-up examination after intracranial stent (ICS) or flow diverter (FD) implantation.</p><p><strong>Key points: </strong>Neuroimaging of intracranial vessels with implanted stents and flow diverters is limited by artifacts. Twelve patients with nine flow diverters and six intracranial stents underwent photon-counting detector computed tomography angiography (PCD-CTA). In-stent vessel lumen visibility improved using sharp reconstruction kernels and a low keV level. Virtual monoenergetic and iodine reconstructions were best to evaluate in-stent vessel lumen.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"10"},"PeriodicalIF":3.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780015/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068496","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":"CompositIA: an open-source automated quantification tool for body composition scores from thoraco-abdominal CT scans.","authors":"Raffaella Fiamma Cabini, Andrea Cozzi, Svenja Leu, Benedikt Thelen, Rolf Krause, Filippo Del Grande, Diego Ulisse Pizzagalli, Stefania Maria Rita Rizzo","doi":"10.1186/s41747-025-00552-7","DOIUrl":"10.1186/s41747-025-00552-7","url":null,"abstract":"<p><strong>Background: </strong>Body composition scores allow for quantifying the volume and physical properties of specific tissues. However, their manual calculation is time-consuming and prone to human error. This study aims to develop and validate CompositIA, an automated, open-source pipeline for quantifying body composition scores from thoraco-abdominal computed tomography (CT) scans.</p><p><strong>Methods: </strong>A retrospective dataset of 205 contrast-enhanced thoraco-abdominal CT examinations was used for training, while 54 scans from a publicly available dataset were used for independent testing. Two radiology residents performed manual segmentation, identifying the centers of the L1 and L3 vertebrae and segmenting the corresponding axial slices. MultiResUNet was used to identify CT slices intersecting the L1 and L3 vertebrae, and its performance was evaluated using the mean absolute error (MAE). Two U-nets were used to segment the axial slices, with performance evaluated through the volumetric Dice similarity coefficient (vDSC). CompositIA's performance in quantifying body composition indices was assessed using mean percentage relative error (PRE), regression, and Bland-Altman analyses.</p><p><strong>Results: </strong>On the independent dataset, CompositIA achieved a MAE of about 5 mm in detecting slices intersecting the L1 and L3 vertebrae, with a MAE < 10 mm in at least 85% of cases and a vDSC greater than 0.85 in segmenting axial slices. Regression and Bland-Altman analyses demonstrated a strong linear relationship and good agreement between automated and manual scores (p values < 0.001 for all indices), with mean PREs ranging from 5.13% to 15.18%.</p><p><strong>Conclusion: </strong>CompositIA facilitated the automated quantification of body composition scores, achieving high precision in independent testing.</p><p><strong>Relevance statement: </strong>CompositIA is an automated, open-source pipeline for quantifying body composition indices from CT scans, simplifying clinical assessments, and expanding their applicability.</p><p><strong>Key points: </strong>Manual body composition assessment from CTs is time-consuming and prone to errors. CompositIA was trained on 205 CT scans and tested on 54 scans. CompositIA demonstrated mean percentage relative errors under 15% compared to manual indices. CompositIA simplifies body composition assessment through an artificial intelligence-driven and open-source pipeline.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"12"},"PeriodicalIF":3.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780042/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068572","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":"Feasibility of generating sagittal radiographs from coronal views using GAN-based deep learning framework in adolescent idiopathic scoliosis.","authors":"Tito Bassani, Andrea Cina, Fabio Galbusera, Andrea Cazzato, Maria Elena Pellegrino, Domenico Albano, Luca Maria Sconfienza","doi":"10.1186/s41747-025-00553-6","DOIUrl":"10.1186/s41747-025-00553-6","url":null,"abstract":"<p><strong>Background: </strong>Minimizing radiation exposure is crucial in monitoring adolescent idiopathic scoliosis (AIS). Generative adversarial networks (GANs) have emerged as valuable tools being able to generate high-quality synthetic images. This study explores the use of GANs to generate synthetic sagittal radiographs from coronal views in AIS patients.</p><p><strong>Methods: </strong>A dataset of 3,935 AIS patients who underwent spine and pelvis radiographic examinations using the EOS system, which simultaneously acquires coronal and sagittal images, was analyzed. The dataset was divided into training-set (85%, n = 3,356) and test-set (15%, n = 579). GAN model was trained to generate sagittal images from coronal views, with real sagittal views as reference standard. To assess accuracy, 100 subjects from the test-set were randomly selected for manual measurement of lumbar lordosis (LL), sacral slope (SS), pelvic incidence (PI), and sagittal vertical axis (SVA) by two radiologists in both synthetic and real images.</p><p><strong>Results: </strong>Sixty-nine synthetic images were considered assessable. The intraclass correlation coefficient ranged 0.93-0.99 for measurements in real images, and from 0.83 to 0.88 for synthetic images. Correlations between parameters of real and synthetic images were 0.52 (LL), 0.17 (SS), 0.18 (PI), and 0.74 (SVA). Measurement errors showed minimal correlation with scoliosis severity. Mean ± standard deviation absolute errors were 7 ± 7° (LL), 9 ± 7° (SS), 9 ± 8° (PI), and 1.1 ± 0.8 cm (SVA).</p><p><strong>Conclusion: </strong>While the model generates sagittal images visually consistent with reference images, their quality is not sufficient for clinical parameter assessment, except for promising results in SVA.</p><p><strong>Relevance statement: </strong>AI can generate synthetic sagittal radiographs from coronal views to reduce radiation exposure in monitoring adolescent idiopathic scoliosis (AIS). However, while these synthetic images appear visually consistent with real ones, their quality remains insufficient for accurate clinical assessment.</p><p><strong>Key points: </strong>AI can be exploited to generate synthetic sagittal radiographs from coronal views. Dataset of 3,935 subjects was used to train and test AI-model; spinal parameters from synthetic and real images were compared. Synthetic images were visually consistent with real ones, but quality was generally insufficient for accurate clinical assessment.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"11"},"PeriodicalIF":3.7,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11780070/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143068575","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":"CLINTERVENTIONAL protocol: a randomized controlled trial to evaluate clinical consultations and audiovisual tools for interventional radiology.","authors":"Pedro Blas García Jurado, Juan José Espejo Herrero, María Sagrario Lombardo Galera, María Eugenia Pérez Montilla, Sara Barranco Acosta, José García-Revillo, Pilar Font Ugalde, Marina Álvarez Benito","doi":"10.1186/s41747-024-00545-y","DOIUrl":"10.1186/s41747-024-00545-y","url":null,"abstract":"<p><p>Interventional radiology (IR) has evolved rapidly, but the clinical integration of interventional radiologists has not kept pace with technical advancements. This trial will address a gap in the literature by providing a robust investigation into specific measures for enhancing the clinical role of interventional radiologists, with potential implications for improving patient experiences and outcomes. The single-center randomized controlled trial will include 428 patients undergoing IR procedures. The control group will receive information about the procedure from the ordering physician, while the experimental group will have an additional consultation with an interventional radiologist and be shown procedure-specific explanatory videos. The primary outcomes are patients' knowledge, satisfaction with the information and communication, and anxiety. Data collection will involve specific questionnaires and scales. This trial is designed to investigate the importance of proactive clinical roles in patient care within IR. The study explores the potential of consultations and audiovisual tools, highlighting their role in educating patients about procedures. The results may help foster a more widespread acceptance of clinical responsibilities in IR and underscore the pivotal role of audiovisual aids in patient education and satisfaction.</p><p><strong>Trial registration: </strong>NCT05461482 at clinicaltrials.gov.</p><p><strong>Relevance statement: </strong>This randomized controlled trial will assess the impact of clinical consultations and explanatory audiovisual tools on patient understanding, satisfaction, and anxiety in interventional radiology. The findings could help establish a more proactive clinical role for interventional radiologists and improve the overall quality of patient-centered care.</p><p><strong>Key points: </strong>We describe the protocol of an interventional radiology randomized clinical trial. The control group will receive procedure information from the referring physician and the experimental group receives additional consultation with interventionalists and views a video. Knowledge, satisfaction with information, and patient anxiety will be evaluated. This study will provide insights about the benefits of consultations and videos in interventional radiology.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"6"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735821/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985079","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":"Can ChatGPT4-vision identify radiologic progression of multiple sclerosis on brain MRI?","authors":"Brendan S Kelly, Sophie Duignan, Prateek Mathur, Henry Dillon, Edward H Lee, Kristen W Yeom, Pearse A Keane, Aonghus Lawlor, Ronan P Killeen","doi":"10.1186/s41747-024-00547-w","DOIUrl":"10.1186/s41747-024-00547-w","url":null,"abstract":"<p><strong>Background: </strong>The large language model ChatGPT can now accept image input with the GPT4-vision (GPT4V) version. We aimed to compare the performance of GPT4V to pretrained U-Net and vision transformer (ViT) models for the identification of the progression of multiple sclerosis (MS) on magnetic resonance imaging (MRI).</p><p><strong>Methods: </strong>Paired coregistered MR images with and without progression were provided as input to ChatGPT4V in a zero-shot experiment to identify radiologic progression. Its performance was compared to pretrained U-Net and ViT models. Accuracy was the primary evaluation metric and 95% confidence interval (CIs) were calculated by bootstrapping. We included 170 patients with MS (50 males, 120 females), aged 21-74 years (mean 42.3), imaged at a single institution from 2019 to 2021, each with 2-5 MRI studies (496 in total).</p><p><strong>Results: </strong>One hundred seventy patients were included, 110 for training, 30 for tuning, and 30 for testing; 100 unseen paired images were randomly selected from the test set for evaluation. Both U-Net and ViT had 94% (95% CI: 89-98%) accuracy while GPT4V had 85% (77-91%). GPT4V gave cautious nonanswers in six cases. GPT4V had precision (specificity), recall (sensitivity), and F1 score of 89% (75-93%), 92% (82-98%), 91 (82-97%) compared to 100% (100-100%), 88 (78-96%), and 0.94 (88-98%) for U-Net and 94% (87-100%), 94 (88-100%), and 94 (89-98%) for ViT.</p><p><strong>Conclusion: </strong>The performance of GPT4V combined with its accessibility suggests has the potential to impact AI radiology research. However, misclassified cases and overly cautious non-answers confirm that it is not yet ready for clinical use.</p><p><strong>Relevance statement: </strong>GPT4V can identify the radiologic progression of MS in a simplified experimental setting. However, GPT4V is not a medical device, and its widespread availability highlights the need for caution and education for lay users, especially those with limited access to expert healthcare.</p><p><strong>Key points: </strong>Without fine-tuning or the need for prior coding experience, GPT4V can perform a zero-shot radiologic change detection task with reasonable accuracy. However, in absolute terms, in a simplified \"spot the difference\" medical imaging task, GPT4V was inferior to state-of-the-art computer vision methods. GPT4V's performance metrics were more similar to the ViT than the U-net. This is an exploratory experimental study and GPT4V is not intended for use as a medical device.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"9"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735712/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985076","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":"Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!","authors":"Ioanna Chouvarda, Sara Colantonio, Ana S C Verde, Ana Jimenez-Pastor, Leonor Cerdá-Alberich, Yannick Metz, Lithin Zacharias, Shereen Nabhani-Gebara, Maciej Bobowicz, Gianna Tsakou, Karim Lekadir, Manolis Tsiknakis, Luis Martí-Bonmati, Nikolaos Papanikolaou","doi":"10.1186/s41747-024-00543-0","DOIUrl":"10.1186/s41747-024-00543-0","url":null,"abstract":"<p><p>Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validation strategies, examining existing practices that are common or variable across technical groups (TGs) and clinical groups (CGs). The work is based on a set of structured questions encompassing several AI validation topics, addressed to professionals working in AI for medical imaging. A total of 49 responses were obtained and analysed to identify trends and patterns. While TGs valued transparency and traceability the most, CGs pointed out the importance of explainability. Among the topics where TGs may benefit from further exposure are stability and robustness checks, and mitigation of fairness issues. On the other hand, CGs seemed more reluctant towards synthetic data for validation and would benefit from exposure to cross-validation techniques, or segmentation metrics. Topics emerging from the open questions were utility, capability, adoption and trustworthiness. These findings on current trends in AI validation strategies may guide the creation of guidelines necessary for training the next generation of professionals working with AI in healthcare and contribute to bridging any technical-clinical gap in AI validation. RELEVANCE STATEMENT: This study recognised current gaps in understanding and applying AI validation strategies in cancer imaging and helped promote trust and adoption for interdisciplinary teams of technical and clinical researchers. KEY POINTS: Clinical and technical researchers emphasise interpretability, external validation with diverse data, and bias awareness in AI validation for cancer imaging. In cancer imaging AI research, clinical researchers prioritise explainability, while technical researchers focus on transparency and traceability, and see potential in synthetic datasets. Researchers advocate for greater homogenisation of AI validation practices in cancer imaging.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"7"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735720/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985088","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":"Deep learning of noncontrast CT for fast prediction of hemorrhagic transformation of acute ischemic stroke: a multicenter study.","authors":"Huanhuan Ren, Haojie Song, Shaoguo Cui, Hua Xiong, Bangyuan Long, Yongmei Li","doi":"10.1186/s41747-024-00535-0","DOIUrl":"10.1186/s41747-024-00535-0","url":null,"abstract":"<p><strong>Background: </strong>Hemorrhagic transformation (HT) is a complication of reperfusion therapy following acute ischemic stroke (AIS). We aimed to develop and validate a model for predicting HT and its subtypes with poor prognosis-parenchymal hemorrhage (PH), including PH-1 (hematoma within infarcted tissue, occupying < 30%) and PH-2 (hematoma occupying ≥ 30% of the infarcted tissue)-in AIS patients following intravenous thrombolysis (IVT) based on noncontrast computed tomography (NCCT) and clinical data.</p><p><strong>Methods: </strong>In this six-center retrospective study, clinical and imaging data from 445 consecutive IVT-treated AIS patients were collected (01/2018-06/2023). The training cohort comprised 344 patients from five centers, and the test cohort included 101 patients from the sixth center. A clinical model was developed using eXtreme Gradient Boosting, an NCCT-based imaging model was created using deep learning, and an ensemble model integrated both models. Comparison with existing clinical scores (MSS, SEDAN, GRASPS) was performed using the DeLong test.</p><p><strong>Results: </strong>Of the 445 individuals, 202 (45.4%) had HT, 79 (17.8%) had hemorrhagic infarction, and 123 (27.6%) had PH. In the test cohort, the area under the receiver operating characteristic curve (AUROC) of the clinical, imaging, and ensemble model for HT prediction was 0.877, 0.920, and 0.937, respectively. The ensemble model for HT prediction outperformed MSS, SEDAN, and GRASPS scores (p ≤ 0.023). The ensemble model predicted PH and PH-2 with AUROC of 0.858 and 0.806, respectively.</p><p><strong>Conclusion: </strong>Developing and validating an integrated model that can predict HT and its subtypes in AIS patients following IVT based on NCCT and clinical data is feasible.</p><p><strong>Relevance statement: </strong>The clinical, imaging, and ensemble models based on noncontrast CT and clinical data outperformed existing clinical scores in predicting hemorrhagic transformation of AIS and its subtypes with poor prognosis, facilitating personalized treatment decisions.</p><p><strong>Key points: </strong>The models demonstrated the capability to predict hemorrhagic transformation of acute ischemic stroke quickly, accurately, and reliably. The proposed models outperformed existing clinical scores in predicting hemorrhagic transformation. The ensemble model provided risk assessment of parenchymal hemorrhage and parenchymal hemorrhage-2 outperforming existing clinical scores.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"8"},"PeriodicalIF":3.7,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11735721/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142985081","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":"Automated characterisation of cerebral microbleeds using their size and spatial distribution on brain MRI.","authors":"Vaanathi Sundaresan, Giovanna Zamboni, Robert A Dineen, Dorothee P Auer, Stamatios N Sotiropoulos, Nikola Sprigg, Mark Jenkinson, Ludovica Griffanti","doi":"10.1186/s41747-024-00544-z","DOIUrl":"10.1186/s41747-024-00544-z","url":null,"abstract":"<p><p>Cerebral microbleeds (CMBs) are small, hypointense hemosiderin deposits in the brain measuring 2-10 mm in diameter. As one of the important biomarkers of small vessel disease, they have been associated with various neurodegenerative and cerebrovascular diseases. Hence, automated detection, and subsequent extraction of clinically useful metrics (e.g., size and spatial distribution) from CMBs are essential for investigating their clinical impact, especially in large-scale studies. While some work has been done for CMB segmentation, extraction of clinically relevant information is not yet explored. Herein, we propose the first automated method to characterise CMBs using their size and spatial distribution, i.e., CMB count in three regions (and their substructures) used in Microbleed Anatomical Rating Scale (MARS): infratentorial, deep, and lobar. Our method uses structural atlases of the brain for determining individual regions. On an intracerebral haemorrhage study dataset, we achieved a mean absolute error of 2.5 mm for size estimation and an overall accuracy > 90% for automated rating. The code and the atlas of MARS regions in Montreal Neurological Institute-MNI space are publicly available. RELEVANCE STATEMENT: Our method to automatically characterise cerebral microbleeds (size and location) showed a mean absolute error of 2.5 mm for size estimation and an over 90% accuracy for rating of infratentorial, deep and lobar regions. This is a promising approach to automatically provide clinically relevant cerebral microbleeds metrics. KEY POINTS: We present a method to automatically characterise cerebral microbleeds according to size and location. The method achieved a mean absolute error of 2.5 mm for size estimation. Automated rating for infratentorial, deep, and lobar regions achieved an over 90% overall accuracy. We made the code and atlas of Microbleed Anatomical Rating Scale regions publicly available.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"5"},"PeriodicalIF":3.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11730040/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972493","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":"Evaluation of a content-based image retrieval system for radiologists in high-resolution CT of interstitial lung diseases.","authors":"Benjamin Böttcher, Marly van Assen, Roberto Fari, Philipp L von Knebel Doeberitz, Eun Young Kim, Eugene A Berkowitz, Felix G Meinel, Carlo N De Cecco","doi":"10.1186/s41747-024-00539-w","DOIUrl":"10.1186/s41747-024-00539-w","url":null,"abstract":"<p><strong>Background: </strong>This retrospective study aims to evaluate the impact of a content-based image retrieval (CBIR) application on diagnostic accuracy and confidence in interstitial lung disease (ILD) assessment using high-resolution computed tomography CT (HRCT).</p><p><strong>Methods: </strong>Twenty-eight patients with verified pattern-based ILD diagnoses were split into two equal datasets (1 and 2). The images were assessed by two radiology residents (3rd and 5th year) and one expert radiologist in four sessions. Dataset 1 was used for sessions A and C, assessing diagnostic accuracy and confidence with mandatory and without CBIR software. Dataset 2 was used for sessions B and D with optional CBIR use, assessing time spending and frequency of CBIR usage. Accuracy was assessed on the CT pattern level, comparing readers' diagnoses with reference diagnoses and CBIR results with region-of-interest (ROI) patterns.</p><p><strong>Results: </strong>Diagnostic accuracy and confidence of readers showed an increasing trend with CBIR use compared to no CBIR use (53.6% versus 35.7% and 50.0% versus 32.2%, respectively). Time for reading significantly decreased in both datasets (A versus C: 104 s versus 54 s, p < 0.001; B versus D: 88.5 s versus 70 s, p = 0.009), whereas time for research increased with CBIR software use (A versus C: 31 s versus 81 s, p = 0.040). CBIR results showed a high pattern-based accuracy of overall 73.4%. Comparison between readers indicates a slightly higher accuracy of CBIR results when more than one ROI was used as input (77.7% versus 70.1%).</p><p><strong>Conclusion: </strong>CBIR software improves in-training radiologist diagnostic accuracy and confidence while reducing interpretation time in ILD assessment.</p><p><strong>Relevance statement: </strong>Content-based image retrieval software improves the assessment of interstitial lung diseases (ILD) in high-resolution CT, especially for radiology residents, by increasing diagnostic accuracy and confidence while reducing interpretation time. This can provide educational benefits and more time-efficient management of complex cases.</p><p><strong>Key points: </strong>A content-based image retrieval (CBIR) software improves diagnostic accuracy and confidence for in-training radiologists for interstitial lung disease (ILD) assessment on computed tomography (CT). A CBIR application provides condensed information about similar HRCT cases reducing time for ILD assessment. CBIR algorithms benefit from the input of multiple regions of interest per ILD case.</p>","PeriodicalId":36926,"journal":{"name":"European Radiology Experimental","volume":"9 1","pages":"4"},"PeriodicalIF":3.7,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11729592/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142972494","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}