Abstract
Carotid artery plaques can cause arterial vascular diseases such as stroke and myocardial infarction, posing a severe threat to human life. However, the current clinical examination mainly relies on a direct assessment by physicians of patients’ clinical indicators and medical images, lacking an integrated visualization tool for analyzing the influencing factors and composition of carotid artery plaques. We have designed an intelligent carotid artery plaque visual analysis system for vascular surgery experts to comprehensively analyze the clinical physiological and imaging indicators of carotid artery diseases. The system mainly includes two functions: First, it displays the correlation between carotid artery plaque and various factors through a series of information visualization methods and integrates the analysis of patient physiological indicator data. Second, it enhances the interface guidance analysis of the inherent correlation between the components of carotid artery plaque through machine learning and displays the spatial distribution of the plaque on medical images. Additionally, we conducted two case studies on carotid artery plaques using real data obtained from a hospital, and the results indicate that our designed carotid artery plaque analysis system can effectively assist clinical vascular surgeons in gaining new insights into the disease.
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Bauer D, Wu Q, Ma K-L (2023) Fovolnet: fast volume rendering using foveated deep neural networks. IEEE Trans Vis Comput Graph 29(1):515–525
Bonanno L, Sottile F, Ciurleo R, Di Lorenzo G, Bruschetta D, Bramanti A, Ascenti G, Bramanti P, Marino S (2017) Automatic algorithm for segmentation of atherosclerotic carotid plaque. J Stroke Cerebrovas Dis 26(2):411–416
Bonati LH, Jansen O, de Borst GJ, Brown MM (2022) Management of atherosclerotic extracranial carotid artery stenosis. Lancet Neurol 21(3):273–283
Eggers KM, Lind L, Ahlström H, Bjerner T, Barbier CE, Larsson A, Venge P, Lindahl B (2008) Prevalence and pathophysiological mechanisms of elevated cardiac troponin i levels in a population-based sample of elderly subjects. Eur Heart J 29(18):2252–2258
Fu F, Shan Y, Yang G, Zheng C, Zhang M, Rong D, Wang X, Lu J (2023) Deep learning for head and neck ct angiography: stenosis and plaque classification. Radiology 307(3):e220996
Kumar A, Zhang X, Xin HL, Yan H, Huang X, Xu W, Mueller K (2023) Radvolviz: an information display-inspired transfer function editor for multivariate volume visualization. IEEE Trans Vis Comput Graph 1–16
Liu J, Zhou X, Lin H, Lu X, Zheng J, Xu E, Jiang D, Zhang H, Yang X, Zhong J et al. (2022) Deep learning based on carotid transverse b-mode scan videos for the diagnosis of carotid plaque: a prospective multicenter study. Eur Radiol 1–10
Loizou CP, Pattichis CS, Pantziaris M, Nicolaides A (2007) An integrated system for the segmentation of atherosclerotic carotid plaque. IEEE Trans Inf Technol Biomed 11(6):661–667
Loizou CP, Petroudi S, Pantziaris M, Nicolaides AN, Pattichis CS (2014) An integrated system for the segmentation of atherosclerotic carotid plaque ultrasound video. IEEE Trans Ultrason Ferroelectr Freq Control 61(1):86–101
Ma W, Zhou R, Zhao Y, Xia Y, Fenster A, Ding M (2019) Plaque recognition of carotid ultrasound images based on deep residual network. In 2019 IEEE 8th Joint international information technology and artificial intelligence conference (ITAIC). IEEE, 931–934
Moshe CB, Haratz S, Ravona-Springer R, Heymann A, Hung-Mo L, Beeri MS, Tanne D (2020) Long-term trajectories of bmi predict carotid stiffness and plaque volume in type 2 diabetes older adults: a cohort study. Cardiovas Diabetol 19:1–10
Omland T, Ueland T, Jansson AM, Persson A, Karlsson T, Smith C, Herlitz J, Aukrust P, Hartford M, Caidahl K (2008) Circulating osteoprotegerin levels and long-term prognosis in patients with acute coronary syndromes. J Am Coll Cardiol 51(6):627–633
Pandey A, Shukla H, Young GS, Qin L, Zamani AA, Hsu L, Huang R, Dunne C, Borkin MA (2020) Cerebrovis: designing an abstract yet spatially contextualized cerebral artery network visualization. IEEE Trans Vis Comput Graph 26(1):938–948
Preim B, Baer A, Cunningham D, Isenberg T, Ropinski T (2016) A survey of perceptually motivated 3d visualization of medical image data. In: Computer graphics forum, vol. 35, No. 3. Wiley Online Library, pp 501–525
Saba L, Sanagala SS, Gupta SK, Koppula VK, Johri AM, Khanna NN, Mavrogeni S, Laird JR, Pareek G, Miner M et al. (2021) Multimodality carotid plaque tissue characterization and classification in the artificial intelligence paradigm: a narrative review for stroke application. Ann Transl Med 9(14)
Shih M, Rozhon C, Ma K-L (2019) A declarative grammar of flexible volume visualization pipelines. IEEE Trans Vis Comput Graph 25(1):1050–1059
Song H, Lee J, Kim TJ, Lee KH, Kim B, Seo J (2017) Gazedx: interactive visual analytics framework for comparative gaze analysis with volumetric medical images. IEEE Trans Vis Comput Graph 23(1):311–320
Tao J, Imre M, Wang C, Chawla NV, Guo H, Sever G, Kim SH (2019) Exploring time-varying multivariate volume data using matrix of isosurface similarity maps. IEEE Trans Vis Comput Graph 25(1):1236–1245
Wang C, Lv G, Zang D (2017) Risk factors of carotid plaque and carotid common artery intima-media thickening in a high-stroke-risk population. Brain Behav 7(11):e00847
Wang Y, Zhong Z, Hua J (2020) Deeporgannet: on-the-fly reconstruction and visualization of 3d/4d lung models from single-view projections by deep deformation network. IEEE Trans Vis Comput Graph 26(1):960–970
Wang Y, Yan G, Zhu H, Buch S, Wang Y, Haacke EM, Hua J, Zhong Z (2021) Vc-net: deep volume-composition networks for segmentation and visualization of highly sparse and noisy image data. IEEE Trans Vis Comput Graph 27(2):1301–1311
Xu C, Neuroth T, Fujiwara T, Liang R, Ma K-L (2023) A predictive visual analytics system for studying neurodegenerative disease based on DTI fiber tracts. IEEE Trans Vis Comput Graph 29(4):2020–2035
Yinyue D (2011) A thesis submitted in partial fulfillment of the requirements for the degree of master of engineering. In Huazhong University of Science and Technology
Zhou R, Guo F, Azarpazhooh MR, Hashemi S, Cheng X, Spence JD, Ding M, Fenster A (2021) Deep learning-based measurement of total plaque area in b-mode ultrasound images. IEEE J Biomed Health Inform 25(8):2967–2977
Zhou L, Fan M, Hansen C, Johnson CR, Weiskopf D (2022) A review of three-dimensional medical image visualization. Health Data Science
Zhou L, Hansen C (2013) Transfer function design based on user selected samples for intuitive multivariate volume exploration. In 2013 IEEE pacific visualization symposium (PacificVis). IEEE, pp 73–80
Zhou L, Hansen C (2014) Guideme: slice-guided semiautomatic multivariate exploration of volumes. In: Computer graphics forum, vol. 33, No. 3. Wiley Online Library, pp 151–160
Acknowledgements
This research is sponsored in part by Zhejiang Provincial Natural Science Foundation of China under Grant No. LQ24F020018 and the Nature Science Foundation of China through Grant 62302431.
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Xu, C., Zheng, Z., Fu, Y. et al. An integrated visual analytics system for studying clinical carotid artery plaques. J Vis (2024). https://doi.org/10.1007/s12650-024-00983-1
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DOI: https://doi.org/10.1007/s12650-024-00983-1