{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T02:47:11Z","timestamp":1783478831807,"version":"3.55.0"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"12","license":[{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T00:00:00Z","timestamp":1689120000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur Radiol"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Objectives<\/jats:title>\n                <jats:p>\nThis study investigated the technical feasibility of focused view CTA for the selective visualization of stroke related arteries.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>A total of 141 CTA examinations for acute ischemic stroke evaluation were divided into a set of 100 cases to train a deep learning algorithm (dubbed \u201cfocused view CTA\u201d) that selectively extracts brain (including intracranial arteries) and extracranial arteries, and a test set of 41 cases. The visibility of anatomic structures at focused view and unmodified CTA was assessed using the following scoring system: 5\u2009=\u2009completely visible, diagnostically sufficient; 4\u2009=\u2009nearly completely visible, diagnostically sufficient; 3\u2009=\u2009incompletely visible, barely diagnostically sufficient; 2\u2009=\u2009hardly visible, diagnostically insufficient; 1\u2009=\u2009not visible, diagnostically insufficient.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>At focused view CTA, median scores for the aortic arch, subclavian arteries, common carotid arteries, C1, C6, and C7 segments of the internal carotid arteries, V4 segment of the vertebral arteries, basilar artery, cerebellum including cerebellar arteries, cerebrum including cerebral arteries, and dural venous sinuses, were all 4. Median scores for the C2 to C5 segments of the internal carotid arteries, and V1 to V3 segments of the vertebral arteries ranged between 3 and 2. At unmodified CTA, median score for all above-mentioned anatomic structures was 5, which was significantly higher (<jats:italic>p<\/jats:italic>\u2009&lt;\u20090.0001) than that at focused view CTA.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Focused view CTA shows promise for the selective visualization of stroke-related arteries. Further improvements should focus on more accurately visualizing the smaller and tortuous internal carotid and vertebral artery segments close to bone.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Clinical relevance<\/jats:title>\n                <jats:p>Focused view CTA may speed up image interpretation time for LVO detection and may potentially be used as a tool to study the clinical relevance of incidental findings in future prospective long-term follow-up studies.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Key Points<\/jats:title>\n                <jats:p>\u2022 <jats:italic>A deep learning\u2013based algorithm (\u201cfocused view CTA\u201d) was developed to selectively visualize relevant structures for acute ischemic stroke evaluation at CTA.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>The elimination of unrequested anatomic background information was complete in all cases.<\/jats:italic><\/jats:p>\n                <jats:p>\u2022 <jats:italic>Focused view CTA may be used to study the clinical relevance of incidental findings.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-023-09904-6","type":"journal-article","created":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T16:01:53Z","timestamp":1689177713000},"page":"9099-9108","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Focused view CT angiography for selective visualization of stroke related arteries: technical feasibility"],"prefix":"10.1007","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3575-4465","authenticated-orcid":false,"given":"Christian","family":"Roest","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Reina W.","family":"Kloet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria J.","family":"Lamers","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Derya","family":"Yakar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas C.","family":"Kwee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,7,12]]},"reference":[{"key":"9904_CR1","unstructured":"GBD 2019 Stroke Collaborators (2019) Global, regional, and national burden of stroke and its risk factors, 1990\u20132019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol 20(10):795\u2013820"},{"issue":"6","key":"9904_CR2","doi-asserted-by":"publisher","first-page":"1717","DOI":"10.1148\/rg.2019190142","volume":"39","author":"CA Potter","year":"2019","unstructured":"Potter CA, Vagal AS, Goyal M, Nunez DB, Leslie-Mazwi TM, Lev MH (2019) CT for treatment selection in acute ischemic stroke: a code stroke primer. Radiographics 39(6):1717\u20131738","journal-title":"Radiographics"},{"key":"9904_CR3","doi-asserted-by":"publisher","first-page":"105783","DOI":"10.1016\/j.clineuro.2020.105783","volume":"193","author":"M Rositzka","year":"2020","unstructured":"Rositzka M, Hoffmann KT, Meyer HJ, Surov A (2020) Incidental findings on CT angiography of the head and neck in stroke patients. Evaluation of frequency and detection rate. Clin Neurol Neurosurg 193:105783","journal-title":"Clin Neurol Neurosurg"},{"issue":"9","key":"9904_CR4","doi-asserted-by":"publisher","first-page":"4968","DOI":"10.1007\/s00330-020-06844-3","volume":"30","author":"A Brady","year":"2020","unstructured":"Brady A (2020) Incidentalomas, SPEW, and VOMIT-radiological dyspepsia? Eur Radiol 30(9):4968\u20134973","journal-title":"Eur Radiol"},{"key":"9904_CR5","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1016\/j.ejmp.2021.05.003","volume":"85","author":"Y Fu","year":"2021","unstructured":"Fu Y, Lei Y, Wang T, Curran WJ, Liu T, Yang X (2021) A review of deep learning based methods for medical image multi-organ segmentation. Phys Med 85:107\u2013122","journal-title":"Phys Med"},{"key":"9904_CR6","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1148\/radiol.2473070436","volume":"247","author":"R Manniesing","year":"2008","unstructured":"Manniesing R, Viergever MA, van der Lugt A, Niessen WJ (2008) Cerebral arteries: fully automated segmentation from CT angiography\u2013a feasibility study. Radiology 247:841\u2013846","journal-title":"Radiology"},{"key":"9904_CR7","unstructured":"ITK-SNAP. Available via http:\/\/www.itksnap.org. Accessed 1 Mar 2022"},{"key":"9904_CR8","doi-asserted-by":"crossref","unstructured":"Milletari F, Navab N, Ahmadi SA (2016) V-net: fully convolutional neural networks for volumetric medical image segmentation. Proceedings of the 2016 Fourth International Conference on 3D Vision (3DV), pp. 565\u2013571, IEEE, Stanford, CA, USA","DOI":"10.1109\/3DV.2016.79"},{"key":"9904_CR9","first-page":"425","volume":"38","author":"A Bouthillier","year":"1996","unstructured":"Bouthillier A, van Loveren HR, Keller JT (1996) Segments of the internal carotid artery: a new classification. Neurosurgery 38:425\u2013432","journal-title":"Neurosurgery"},{"key":"9904_CR10","doi-asserted-by":"publisher","DOI":"10.1177\/15910199211063275","author":"P Gailloud","year":"2021","unstructured":"Gailloud P (2021) The segmentation of the vertebral artery: an ambiguous anatomical concept. Interv Neuroradiol. https:\/\/doi.org\/10.1177\/15910199211063275","journal-title":"Interv Neuroradiol"},{"key":"9904_CR11","doi-asserted-by":"publisher","first-page":"758","DOI":"10.1161\/STROKEAHA.121.034786","volume":"53","author":"FAV Pirson","year":"2022","unstructured":"Pirson FAV, Boodt N, Brouwer J et al (2022) Endovascular treatment for posterior circulation stroke in routine clinical practice: results of the multicenter randomized clinical trial of endovascular treatment for acute ischemic stroke in the Netherlands registry. Stroke 53:758\u2013768","journal-title":"Stroke"},{"key":"9904_CR12","doi-asserted-by":"publisher","first-page":"889","DOI":"10.1007\/s0330-005-0032-1","volume":"16","author":"M Lell","year":"2006","unstructured":"Lell M, Anders K, Klotz E, Ditt H, Bautz W, Tomandl BF (2006) Clinical evaluation of bone-subtraction CT angiography (BSCTA) in head and neck imaging. Eur Radiol 16:889\u2013897","journal-title":"Eur Radiol"},{"key":"9904_CR13","doi-asserted-by":"publisher","first-page":"1019","DOI":"10.1007\/s00330-008-1213-5","volume":"19","author":"Y Watanabe","year":"2009","unstructured":"Watanabe Y, Uotani K, Nakazawa T et al (2009) Dual-energy direct bone removal CT angiography for evaluation of intracranial aneurysm or stenosis: comparison with conventional digital subtraction angiography. Eur Radiol 19:1019\u20131024","journal-title":"Eur Radiol"},{"key":"9904_CR14","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1016\/j.ejrad.2019.01.028","volume":"113","author":"S Lindgren Belal","year":"2019","unstructured":"Lindgren Belal S, Sadik M, Kaboteh R et al (2019) Deep learning for segmentation of 49 selected bones in CT scans: first step in automated PET\/CT-based 3D quantification of skeletal metastases. Eur J Radiol 113:89\u201395","journal-title":"Eur J Radiol"},{"key":"9904_CR15","first-page":"357","volume":"19","author":"H Shim","year":"2005","unstructured":"Shim H, Yun ID, Lee KM, Lee SU (2005) Partition-based extraction of cerebral arteries from CT angiography with emphasis on adaptive tracking. Inf Process Med Imaging 19:357\u2013368","journal-title":"Inf Process Med Imaging"},{"key":"9904_CR16","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1016\/j.jns.2006.11.019","volume":"253","author":"YC Huang","year":"2007","unstructured":"Huang YC, Chen CM, Lai SL et al (2007) Spontaneous intrapetrous internal carotid artery dissection: a case report and literature review. J Neurol Sci 253:90\u201393","journal-title":"J Neurol Sci"},{"key":"9904_CR17","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/S0887-2171(03)90011-4","volume":"24","author":"D Green","year":"2003","unstructured":"Green D, Parker D (2003) CTA and MRA: visualization without catheterization. Semin Ultrasound CT MR 24:185\u2013191","journal-title":"Semin Ultrasound CT MR"},{"key":"9904_CR18","doi-asserted-by":"publisher","first-page":"2956","DOI":"10.1118\/1.3397813","volume":"37","author":"A Mendrik","year":"2010","unstructured":"Mendrik A, Vonken EJ, van Ginneken B et al (2010) Automatic segmentation of intracranial arteries and veins in four-dimensional cerebral CT perfusion scans. Med Phys 37:2956\u20132966","journal-title":"Med Phys"},{"key":"9904_CR19","doi-asserted-by":"publisher","first-page":"1009","DOI":"10.1016\/j.acra.2013.04.008","volume":"20","author":"Q Li","year":"2013","unstructured":"Li Q, Lv F, Wei Y, Luo T, Xie P (2013) Automated subtraction CT angiography for visualization of the whole brain vasculature: a feasibility study. Acad Radiol 20:1009\u20131014","journal-title":"Acad Radiol"},{"key":"9904_CR20","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190178","volume":"2","author":"M Meijs","year":"2020","unstructured":"Meijs M, Pegge SAH, Vos MHE et al (2020) Cerebral artery and vein segmentation in four-dimensional CT angiography using convolutional neural networks. Radiol Artif Intell 2:e190178","journal-title":"Radiol Artif Intell"}],"container-title":["European Radiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09904-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00330-023-09904-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00330-023-09904-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,23]],"date-time":"2023-11-23T17:10:34Z","timestamp":1700759434000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00330-023-09904-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,12]]},"references-count":20,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2023,12]]}},"alternative-id":["9904"],"URL":"https:\/\/doi.org\/10.1007\/s00330-023-09904-6","relation":{},"ISSN":["1432-1084"],"issn-type":[{"value":"1432-1084","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,12]]},"assertion":[{"value":"7 October 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 April 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 May 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 July 2023","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The scientific guarantor of this publication is C. Roest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Guarantor"}},{"value":"The authors of this manuscript declare relationships with the following companies: C.R.: Grant recipient Siemens Healthineers.Derya Yakar is a member of the <b>European Radiology<\/b> Scientific Editorial Board and has therefore not taken part in review and selection of this article. The authors of this manuscript declare no relationships with any companies whose products or services may be related to the subject matter of the article.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"No complex statistical methods were necessary for this paper.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Statistics and biometry"}},{"value":"Written informed consent was waived by the Institutional Review Board.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}},{"value":"Institutional Review Board approval was obtained.","order":6,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"NA","order":7,"name":"Ethics","group":{"name":"EthicsHeading","label":"Study subjects or cohorts overlap"}},{"value":"\u2022 Retrospective\u2022 Experimental\u2022 performed at one institution","order":8,"name":"Ethics","group":{"name":"EthicsHeading","label":"Methodology"}}]}}