{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,24]],"date-time":"2025-03-24T09:12:15Z","timestamp":1742807535412,"version":"3.37.3"},"reference-count":20,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:00:00Z","timestamp":1710288000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100010890","name":"Chinese Government Scholarship","doi-asserted-by":"publisher","award":["202108310010"],"award-info":[{"award-number":["202108310010"]}],"id":[{"id":"10.13039\/501100010890","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int J CARS"],"abstract":"<jats:title>Abstract<\/jats:title><jats:sec>\n                <jats:title>Purpose<\/jats:title>\n                <jats:p>The assessment of vulnerable plaque characteristics and distribution is important to stratify cardiovascular risk in a patient. Computed tomography angiography (CTA) offers a promising alternative to invasive imaging but is limited by the fact that the range of Hounsfield units (HU) in lipid-rich areas overlaps with the HU range in fibrotic tissue and that the HU range of calcified plaques overlaps with the contrast within the contrast-filled lumen. This paper is to investigate whether lipid-rich and calcified plaques can be detected more accurately on cross-sectional CTA images using deep learning methodology.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>Two deep learning (DL) approaches are proposed, a 2.5D Dense U-Net and 2.5D Mask-RCNN, which separately perform the cross-sectional plaque detection in the Cartesian and polar domain. The spread-out view is used to evaluate and show the prediction result of the plaque regions. The accuracy and F1-score are calculated on a lesion level for the DL and conventional plaque detection methods.\n<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>For the lipid-rich plaques, the median and mean values of the F1-score calculated by the two proposed DL methods on 91 lesions were approximately 6 and 3 times higher than those of the conventional method. For the calcified plaques, the F1-score of the proposed methods was comparable to those of the conventional method. The median F1-score of the Dense U-Net-based method was 3% higher than that of the conventional method.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>The two methods proposed in this paper contribute to finer cross-sectional predictions of lipid-rich and calcified plaques compared to studies focusing only on longitudinal prediction. The angular prediction performance of the proposed methods outperforms the convincing conventional method for lipid-rich plaque and is comparable for calcified plaque.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s11548-024-03086-2","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T08:55:41Z","timestamp":1710320141000},"page":"971-981","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Cross-sectional angle prediction of lipid-rich and calcified tissue on computed tomography angiography images"],"prefix":"10.1007","volume":"19","author":[{"given":"Xiaotong","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Alexander","family":"Broersen","sequence":"additional","affiliation":[]},{"given":"Hessam","family":"Sokooti","sequence":"additional","affiliation":[]},{"given":"Anantharaman","family":"Ramasamy","sequence":"additional","affiliation":[]},{"given":"Pieter","family":"Kitslaar","sequence":"additional","affiliation":[]},{"given":"Ramya","family":"Parasa","sequence":"additional","affiliation":[]},{"given":"Medeni","family":"Karaduman","sequence":"additional","affiliation":[]},{"given":"Amear Souded Ali Jan","family":"Mohammed","sequence":"additional","affiliation":[]},{"given":"Christos V.","family":"Bourantas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8666-3731","authenticated-orcid":false,"given":"Jouke","family":"Dijkstra","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,3,13]]},"reference":[{"key":"3086_CR1","doi-asserted-by":"publisher","DOI":"10.5772\/67196","author":"M-J Bertrand","year":"2017","unstructured":"Bertrand M-J, Lavoie-L\u2019Allier P, Tardif J-C (2017) Near-infrared spectroscopy (NIRS): a novel tool for intravascular coronary imaging. Dev Near-Infrared Spectrosc. https:\/\/doi.org\/10.5772\/67196","journal-title":"Dev Near-Infrared Spectrosc"},{"issue":"3","key":"3086_CR2","doi-asserted-by":"publisher","first-page":"249","DOI":"10.2214\/AJR.14.13760","volume":"204","author":"F Saremi","year":"2015","unstructured":"Saremi F, Achenbach S (2015) Coronary plaque characterization using CT. Am J Roentgenol 204(3):249\u2013260. https:\/\/doi.org\/10.2214\/AJR.14.13760","journal-title":"Am J Roentgenol"},{"issue":"19","key":"3086_CR3","doi-asserted-by":"publisher","first-page":"10003","DOI":"10.3390\/ijerph181910003","volume":"18","author":"A Gudigar","year":"2021","unstructured":"Gudigar A, Nayak S, Samanth J, Raghavendra U, Ashwal AJ, Barua PD, Hasan MN, Ciaccio EJ, Tan R-S, Rajendra Acharya U (2021) Recent trends in artificial intelligence-assisted coronary atherosclerotic plaque characterization. Int J Environ Res Public Health 18(19):10003. https:\/\/doi.org\/10.3390\/ijerph181910003","journal-title":"Int J Environ Res Public Health"},{"issue":"7","key":"3086_CR4","doi-asserted-by":"publisher","first-page":"1282","DOI":"10.1161\/ATVBAHA.108.179739","volume":"30","author":"AV Finn","year":"2010","unstructured":"Finn AV, Nakano M, Narula J, Kolodgie FD, Virmani R (2010) Concept of vulnerable\/unstable plaque. Arterioscler Thromb Vasc Biol 30(7):1282\u20131292. https:\/\/doi.org\/10.1161\/ATVBAHA.108.179739","journal-title":"Arterioscler Thromb Vasc Biol"},{"issue":"1","key":"3086_CR5","doi-asserted-by":"publisher","first-page":"133","DOI":"10.4244\/eijv5i1a21","volume":"5","author":"S Sathyanarayana","year":"2009","unstructured":"Sathyanarayana S, Carlier S, Li W, Thomas L (2009) Characterisation of atherosclerotic plaque by spectral similarity of radiofrequency intravascular ultrasound signals. EuroIntervention 5(1):133\u2013139. https:\/\/doi.org\/10.4244\/eijv5i1a21","journal-title":"EuroIntervention"},{"issue":"1","key":"3086_CR6","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1161\/01.cir.0000044384.41037.43","volume":"107","author":"GJ Tearney","year":"2003","unstructured":"Tearney GJ, Yabushita H, Houser SL, Aretz HT, Jang I-K, Schlendorf KH, Kauffman CR, Shishkov M, Halpern EF, Bouma BE (2003) Quantification of macrophage content in atherosclerotic plaques by optical coherence tomography. Circulation 107(1):113\u2013119. https:\/\/doi.org\/10.1161\/01.cir.0000044384.41037.43","journal-title":"Circulation"},{"issue":"4","key":"3086_CR7","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1093\/eurheartj\/ehx247","volume":"39","author":"A-S Schuurman","year":"2018","unstructured":"Schuurman A-S, Vroegindewey M, Kardys I, Oemrawsingh RM, Cheng JM, Boer S, Garcia-Garcia HM, Geuns R-J, Regar ES, Daemen J, Mieghem NM, Serruys PW, Boersma E, Akkerhuis KM (2018) Near-infrared spectroscopy-derived lipid core burden index predicts adverse cardiovascular outcome in patients with coronary artery disease during long-term follow-up. Eur Heart J 39(4):295\u2013302. https:\/\/doi.org\/10.1093\/eurheartj\/ehx247","journal-title":"Eur Heart J"},{"key":"3086_CR8","doi-asserted-by":"publisher","DOI":"10.3389\/fcvm.2021.597568","volume":"8","author":"H Liu","year":"2021","unstructured":"Liu H, Wingert A, Wang J, Zhang J, Wang X, Sun J, Chen F, Khalid SG, Jiang J, Zheng D (2021) Extraction of coronary atherosclerotic plaques from computed tomography imaging: a review of recent methods. Front Cardiovasc Med 8:597568. https:\/\/doi.org\/10.3389\/fcvm.2021.597568","journal-title":"Front Cardiovasc Med"},{"issue":"4","key":"3086_CR9","doi-asserted-by":"publisher","first-page":"1055","DOI":"10.1016\/j.jcmg.2019.03.033","volume":"13","author":"M Daghem","year":"2020","unstructured":"Daghem M, Bing R, Fayad ZA, Dweck MR (2020) Noninvasive imaging to assess atherosclerotic plaque composition and disease activity: coronary and carotid applications. JACC Cardiovasc Imaging 13(4):1055\u20131068. https:\/\/doi.org\/10.1016\/j.jcmg.2019.03.033","journal-title":"JACC Cardiovasc Imaging"},{"issue":"1","key":"3086_CR10","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10554-015-0741-8","volume":"32","author":"B Szilveszter","year":"2016","unstructured":"Szilveszter B, Celeng C, Maurovich-Horvat P (2016) Plaque assessment by coronary CT. Int J Cardiovasc Imaging 32(1):161\u201372. https:\/\/doi.org\/10.1007\/s10554-015-0741-8","journal-title":"Int J Cardiovasc Imaging"},{"issue":"7","key":"3086_CR11","doi-asserted-by":"publisher","first-page":"1588","DOI":"10.1109\/TMI.2018.2883807","volume":"38","author":"M Zreik","year":"2019","unstructured":"Zreik M, Hamersvelt RW, Wolterink JM, Leiner T, Viergever MA, I\u0161gum I (2019) A recurrent CNN for automatic detection and classification of coronary artery plaque and stenosis in coronary CT angiography. IEEE Trans Med Imaging 38(7):1588\u20131598. https:\/\/doi.org\/10.1109\/TMI.2018.2883807","journal-title":"IEEE Trans Med Imaging"},{"key":"3086_CR12","doi-asserted-by":"publisher","unstructured":"Liu J, Jin C, Feng J, Du Y, Lu J, Zhou J (2019) A vessel-focused 3D convolutional network for automatic segmentation and classification of coronary artery plaques in cardiac CTA. In: Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges, vol. 11395, (pp. 131\u2013141). https:\/\/doi.org\/10.1007\/978-3-030-12029-0_15","DOI":"10.1007\/978-3-030-12029-0_15"},{"issue":"12","key":"3086_CR13","doi-asserted-by":"publisher","first-page":"6216","DOI":"10.1002\/mp.14391","volume":"47","author":"MH Vu","year":"2020","unstructured":"Vu MH, Grimbergen G, Nyholm T, L\u00f6fstedt T (2020) Evaluation of multislice inputs to convolutional neural networks for medical image segmentation. Med Phys 47(12):6216\u20136231. https:\/\/doi.org\/10.1002\/mp.14391","journal-title":"Med Phys"},{"key":"3086_CR14","doi-asserted-by":"publisher","unstructured":"Huang G, Liu Z, Van Der\u00a0Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 2261\u20132269). https:\/\/doi.org\/10.1109\/CVPR.2017.243","DOI":"10.1109\/CVPR.2017.243"},{"key":"3086_CR15","doi-asserted-by":"publisher","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), (pp. 2980\u20132988). https:\/\/doi.org\/10.1109\/ICCV.2017.322","DOI":"10.1109\/ICCV.2017.322"},{"issue":"5","key":"3086_CR16","doi-asserted-by":"publisher","first-page":"1177","DOI":"10.1007\/s10554-013-0194-x","volume":"29","author":"MA De Graaf","year":"2013","unstructured":"De Graaf MA, Broersen A, Kitslaar PH, Roos CJ, Dijkstra J, Lelieveldt BP, Jukema JW, Schalij MJ, Delgado V, Bax JJ, Johan HCR, Arthur JS (2013) Automatic quantification and characterization of coronary atherosclerosis with computed tomography coronary angiography: cross-correlation with intravascular ultrasound virtual histology. Int J Cardiovasc Imaging 29(5):1177\u20131190. https:\/\/doi.org\/10.1007\/s10554-013-0194-x","journal-title":"Int J Cardiovasc Imaging"},{"key":"3086_CR17","doi-asserted-by":"publisher","unstructured":"O\u2019Brien A, LaCombe A, Stickland A, Madder RD (2016) Intracoronary near-infrared spectroscopy: an overview of the technology, histologic validation, and clinical applications. Global Cardiol Sci Pract. https:\/\/doi.org\/10.21542\/gcsp.2016.18","DOI":"10.21542\/gcsp.2016.18"},{"issue":"10","key":"3086_CR18","doi-asserted-by":"publisher","first-page":"3073","DOI":"10.1007\/s00330-015-3698-z","volume":"25","author":"H-B Park","year":"2015","unstructured":"Park H-B, Lee BK, Shin S, Heo R, Arsanjani R, Kitslaar PH, Broersen A, Dijkstra J, Ahn SG, Min JK, Chang H-J, Hong M-K, Jang Y, Chung N (2015) Clinical feasibility of 3D automated coronary atherosclerotic plaque quantification algorithm on coronary computed tomography angiography: comparison with intravascular ultrasound. Eur Radiol 25(10):3073\u201383. https:\/\/doi.org\/10.1007\/s00330-015-3698-z","journal-title":"Eur Radiol"},{"key":"3086_CR19","doi-asserted-by":"publisher","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 770\u2013778). https:\/\/doi.org\/10.1109\/CVPR.2016.90","DOI":"10.1109\/CVPR.2016.90"},{"key":"3086_CR20","doi-asserted-by":"publisher","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision (ICCV), (pp. 618\u2013626). https:\/\/doi.org\/10.1109\/ICCV.2017.74","DOI":"10.1109\/ICCV.2017.74"}],"container-title":["International Journal of Computer Assisted Radiology and Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03086-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11548-024-03086-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11548-024-03086-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,16]],"date-time":"2024-05-16T13:16:00Z","timestamp":1715865360000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11548-024-03086-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,3,13]]},"references-count":20,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,5]]}},"alternative-id":["3086"],"URL":"https:\/\/doi.org\/10.1007\/s11548-024-03086-2","relation":{},"ISSN":["1861-6429"],"issn-type":[{"type":"electronic","value":"1861-6429"}],"subject":[],"published":{"date-parts":[[2024,3,13]]},"assertion":[{"value":"4 July 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 February 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 March 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Author Pieter Kitslaar and author Hessam Sokooti are employed by Medis medical imaging systems bv.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}