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The approach integrates aneurysm identification via automated segmentation, hemodynamic visualization, and quantification of derived parameters, including wall shear stress (WSS). Using nnU-Net trained on 3D cine bSSFP MRI, performance is evaluated with cross-validation and validated against manual segmentations, demonstrating robustness to anatomical variability and strong agreement for WSS.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/automated-identification-hemodynamic-visualisation-and-quantification-of-abdominal-aortic-aneurysms-with-4d-flow-magnetic-resonance-imaging/457315/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/automated-identification-hemodynamic-visualisation-and-quantification-of-abdominal-aortic-aneurysms-with-4d-flow-magnetic-resonance-imaging/457315.png","ImageObject",300,407,{"name":92,"@type":93},"Cipher","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What problem does the study address for 4D flow MRI in abdominal aortic aneurysms?","Question",{"text":112,"@type":113},"Clinical implementation is limited by time-consuming, cumbersome post-processing and lack of standardisation, especially for AAA applications in the abdominal region.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How is the aneurysm segmentation model trained?",{"text":117,"@type":113},"A nnU-Net is trained using 3D cine bSSFP MRI, with 5-fold cross-validation and segmentation of the aorta.",{"name":119,"@type":110,"acceptedAnswer":120},"What metrics are used to evaluate segmentation quality and agreement?",{"text":121,"@type":113},"Segmentation performance is assessed using the Dice similarity coefficient (DSC), 95% percentile Hausdorff distance (HD95), and Pearson correlations of WSS values between manual and nnU-Net segmentations.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},457315,1790794816,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},687208528416,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","Original Article  \nAutomated identification, hemodynamic visualisation and quantification of abdominal aortic aneurysms with 4D flow magnetic resonance imaging  \nEva Aalbregt 1,2,3^, Wilhelm Stehling2,3^, Renske Merton2,3^, Aart Nederveen2^, Kak Khee Yeung1,3^, Pim van Ooij2,3^, Eric Schrauben2,3^  \n1Department of Surgery, Amsterdam University Medical Centers, Location University of Amsterdam, Amsterdam, The Netherlands; 2Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location University of Amsterdam, Amsterdam, The Netherlands; 3Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Syndromes, Amsterdam, The Netherlands  \nContributions: (I) Conception and design: A Nederveen, KK Yeung, P van Ooij, E Schrauben; (II) Administrative support: E Aalbregt; (III) Provision of study materials or patients: E Aalbregt, R Merton, A Nederveen; (IV) Collection and assembly of data: E Aalbregt, W Stehling; (V) Data analysis and interpretation: E Aalbregt, E Schrauben, P van Ooij; (VI) Manuscript writing: All authors; (VII) Final approval of manuscript: All authors.  \nCorrespondence to: Eric Schrauben, PhD. Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location University of Amsterdam, Meibergdreef 9, Room Z0-178, 1105 AZ Amsterdam, The Netherlands; Amsterdam Cardiovascular Sciences, Atherosclerosis and Aortic Syndromes, Amsterdam, The Netherlands. Email: [e.m.schrauben@amsterdamumc.nl](e.m.schrauben@amsterdamumc.nl).  \nBackground: Four dimensional (4D) flow magnetic resonance imaging (MRI) enables both visualisation and quantification of hemodynamics in abdominal aortic aneurysms (AAAs) . However, its clinical implementation remains limited due to time-consuming and cumbersome post-processing, as well as a lack of standardisation. While previous work has proposed automated solutions for thoracic and intracranial cases, applications in the abdominal region, particularly for AAAs, remain underexplored. The aim of this study was to develop an automated post-processing pipeline for 4D flow MRI in AAA patients, incorporating aneurysm identification, via automated segmentation, blood flow visualisation and hemodynamic parameter  \nquantification.  \nMethods: 4D flow MRI and three dimensional (3D) cine balanced steady state free precession (bSSFP) scans were acquired from 16 patients with an AAA in the same field of view and with an isotropic resolution of 1 mm. Using 5-fold cross-validation, an nnU-Net was trained to segment the aorta based on 3D cine bSSFP MRI. The automated post-processing pipeline was built using MATLAB. Wall shear stress (WSS) values were assessed within the extracted aneurysm. nnU-Net performance was assessed with the Dicesimilarity coefficient (DSC), 95% percentile Hausdorff distance (HD95) and by calculating the Pearson correlation coefficients between WSS values obtained in the aneurysm based on segmentation with both  \nmethods.  \nResults: The resulting post-processing pipeline demonstrated robustness to anatomical variability, including tortuosity and intraluminal thrombus (ILT), both characteristic of AAA, and consistently produced output without user-interference. The nnU-Net performance was excellent for lumen segmentation and good for ILT segmentation, with DSCs of 0.93 (0.05) and 0.80 (0.26) respectively. The HD95 was 4.87 (8.59) mm and 6.04 (3.71) mm for respectively lumen and thrombus segmentation. Significant correlations (ρ =0.92  \nor higher) were found between WSS values derived using the manual and nnU-Net segmentations.  \n^ ORCID: Eva Aalbregt, 0000-0002-1882-5399; Wilhelm Stehling, 0009-0009-5950-7539; Renske Merton, 0000-0003-0147-0939; Aart Nederveen, 0000-0002-5477-973X; Kak Khee Yeung, 0000-0002-8455-286X; Pim van Ooij, 0000-0001-8755-0192; Eric Schrauben, 0000- 0002-7922-3615.  \n© AME Publishing Company. Quant Imaging Med Surg 2026;16(1):8 | [https://dx.doi.org/10.21037/qims-2025-1335](https://dx.doi.org/10.21037/qims-2025-","cbCaigkjsmoNr1rJ","https://ap.wps.com/l/cbCaigkjsmoNr1rJ","pdf",1585542,14,"English","# Background\n# Methods\n# Results\n# Conclusions\n# Introduction","[{\"question\":\"What problem does the study address for 4D flow MRI in abdominal aortic aneurysms?\",\"answer\":\"Clinical implementation is limited by time-consuming, cumbersome post-processing and lack of standardisation, especially for AAA applications in the abdominal region.\"},{\"question\":\"How is the aneurysm segmentation model trained?\",\"answer\":\"A nnU-Net is trained using 3D cine bSSFP MRI, with 5-fold cross-validation and segmentation of the aorta.\"},{\"question\":\"What metrics are used to evaluate segmentation quality and agreement?\",\"answer\":\"Segmentation performance is assessed using the Dice similarity coefficient (DSC), 95% percentile Hausdorff distance (HD95), and Pearson correlations of WSS values between manual and nnU-Net segmentations.\"}]","Automated identification, hemodynamic visualisation and quantification of abdominal aortic aneurysms with 4D flow magnetic resonance imaging | PDF",1790748858,35]