A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls
•A new architecture of motion tracking has been proposed.•Deep learning method for displacement tracking of the vessel wall from the ultrasound RF signals.•It improves the accuracy in vessel wall motion tracking in comparsion with traditional methods. It is necessary to monitor the mechanical proper...
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| Veröffentlicht in: | Computerized medical imaging and graphics 2021-01, Vol.87, p.101819-101819, Article 101819 |
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| container_title | Computerized medical imaging and graphics |
| container_volume | 87 |
| creator | Xiao, Chenhui Li, Zhenzhou Lu, Jianfeng Wang, Jinyan Zheng, Haoteng Bi, Zuyue Chen, Mengyang Mao, Rui Lu, Minhua |
| description | •A new architecture of motion tracking has been proposed.•Deep learning method for displacement tracking of the vessel wall from the ultrasound RF signals.•It improves the accuracy in vessel wall motion tracking in comparsion with traditional methods.
It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics. In comparison with traditional method, both results on simulation and experimental carotid artery data demonstrated that the DL method has higher accuracy for motion tracking of artery walls. Hence, the DL method can be widely applied so that can predict the early pathology of cardiovascular system. |
| doi_str_mv | 10.1016/j.compmedimag.2020.101819 |
| format | Article |
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It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics. In comparison with traditional method, both results on simulation and experimental carotid artery data demonstrated that the DL method has higher accuracy for motion tracking of artery walls. Hence, the DL method can be widely applied so that can predict the early pathology of cardiovascular system.</description><identifier>ISSN: 0895-6111</identifier><identifier>EISSN: 1879-0771</identifier><identifier>DOI: 10.1016/j.compmedimag.2020.101819</identifier><identifier>PMID: 33341465</identifier><language>eng</language><publisher>United States: Elsevier Ltd</publisher><subject>Deep learning ; Displacement tracking ; Pulse wave imaging ; Single target block matching ; Vascular biomechanics</subject><ispartof>Computerized medical imaging and graphics, 2021-01, Vol.87, p.101819-101819, Article 101819</ispartof><rights>2020</rights><rights>Copyright © 2020. Published by Elsevier Ltd.</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c377t-27aefdd2e0719f46b7464491f31c4f27d8fdee7440733a3a6ba506b2e3a28c6d3</citedby><cites>FETCH-LOGICAL-c377t-27aefdd2e0719f46b7464491f31c4f27d8fdee7440733a3a6ba506b2e3a28c6d3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://www.sciencedirect.com/science/article/pii/S0895611120301142$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,776,780,3537,27901,27902,65306</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33341465$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Xiao, Chenhui</creatorcontrib><creatorcontrib>Li, Zhenzhou</creatorcontrib><creatorcontrib>Lu, Jianfeng</creatorcontrib><creatorcontrib>Wang, Jinyan</creatorcontrib><creatorcontrib>Zheng, Haoteng</creatorcontrib><creatorcontrib>Bi, Zuyue</creatorcontrib><creatorcontrib>Chen, Mengyang</creatorcontrib><creatorcontrib>Mao, Rui</creatorcontrib><creatorcontrib>Lu, Minhua</creatorcontrib><title>A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls</title><title>Computerized medical imaging and graphics</title><addtitle>Comput Med Imaging Graph</addtitle><description>•A new architecture of motion tracking has been proposed.•Deep learning method for displacement tracking of the vessel wall from the ultrasound RF signals.•It improves the accuracy in vessel wall motion tracking in comparsion with traditional methods.
It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics. In comparison with traditional method, both results on simulation and experimental carotid artery data demonstrated that the DL method has higher accuracy for motion tracking of artery walls. Hence, the DL method can be widely applied so that can predict the early pathology of cardiovascular system.</description><subject>Deep learning</subject><subject>Displacement tracking</subject><subject>Pulse wave imaging</subject><subject>Single target block matching</subject><subject>Vascular biomechanics</subject><issn>0895-6111</issn><issn>1879-0771</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNqNkMlu2zAQQIkiReMsvxCwt1zkcjMpHQ2jaQsEKBCkZ4Iih64cSlRJyUb-vnSdBjnmNODMm4UPoc-ULCmh8stuaWM_9uC63myXjLB_-Zo2H9CC1qqpiFL0DC1I3awqSSk9Rxc57wgppKKf0DnnXFAhVwtk1niAA3YAIw5g0tANW9zD9Ds67GPCrstjMBZ6GCY8JWOfjoBPscdzKO8c58Hhhzucu-1gQsbR473Jdg4m4YMJIV-hj74U4PolXqJfd18fN9-r-5_ffmzW95XlSk0VUwa8cwzKhY0XslVCCtFQz6kVnilX-3KkEoIozg03sjUrIlsG3LDaSscv0e1p7pjinxnypPsuWwjBDBDnrJlQdMUl46SgzQm1KeacwOsxFZXpWVOij4b1Tr8xrI-G9clw6b15WTO3pf7a-V9pATYnAMpn9x0knW0Hgy2zEthJu9i9Y81fmrOTaQ</recordid><startdate>202101</startdate><enddate>202101</enddate><creator>Xiao, Chenhui</creator><creator>Li, Zhenzhou</creator><creator>Lu, Jianfeng</creator><creator>Wang, Jinyan</creator><creator>Zheng, Haoteng</creator><creator>Bi, Zuyue</creator><creator>Chen, Mengyang</creator><creator>Mao, Rui</creator><creator>Lu, Minhua</creator><general>Elsevier Ltd</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>202101</creationdate><title>A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls</title><author>Xiao, Chenhui ; Li, Zhenzhou ; Lu, Jianfeng ; Wang, Jinyan ; Zheng, Haoteng ; Bi, Zuyue ; Chen, Mengyang ; Mao, Rui ; Lu, Minhua</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c377t-27aefdd2e0719f46b7464491f31c4f27d8fdee7440733a3a6ba506b2e3a28c6d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Deep learning</topic><topic>Displacement tracking</topic><topic>Pulse wave imaging</topic><topic>Single target block matching</topic><topic>Vascular biomechanics</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Xiao, Chenhui</creatorcontrib><creatorcontrib>Li, Zhenzhou</creatorcontrib><creatorcontrib>Lu, Jianfeng</creatorcontrib><creatorcontrib>Wang, Jinyan</creatorcontrib><creatorcontrib>Zheng, Haoteng</creatorcontrib><creatorcontrib>Bi, Zuyue</creatorcontrib><creatorcontrib>Chen, Mengyang</creatorcontrib><creatorcontrib>Mao, Rui</creatorcontrib><creatorcontrib>Lu, Minhua</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Computerized medical imaging and graphics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Xiao, Chenhui</au><au>Li, Zhenzhou</au><au>Lu, Jianfeng</au><au>Wang, Jinyan</au><au>Zheng, Haoteng</au><au>Bi, Zuyue</au><au>Chen, Mengyang</au><au>Mao, Rui</au><au>Lu, Minhua</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls</atitle><jtitle>Computerized medical imaging and graphics</jtitle><addtitle>Comput Med Imaging Graph</addtitle><date>2021-01</date><risdate>2021</risdate><volume>87</volume><spage>101819</spage><epage>101819</epage><pages>101819-101819</pages><artnum>101819</artnum><issn>0895-6111</issn><eissn>1879-0771</eissn><abstract>•A new architecture of motion tracking has been proposed.•Deep learning method for displacement tracking of the vessel wall from the ultrasound RF signals.•It improves the accuracy in vessel wall motion tracking in comparsion with traditional methods.
It is necessary to monitor the mechanical properties of arteries which directly related to cardiovascular diseases (CVDs) in the early stages. In this study, we proposed a new method based on deep learning (DL) to track the displacement of the vessel wall from the ultrasound radio-frequency (RF) signals, which is a key technique to achieve quantitative measurement of vascular biomechanics. In comparison with traditional method, both results on simulation and experimental carotid artery data demonstrated that the DL method has higher accuracy for motion tracking of artery walls. Hence, the DL method can be widely applied so that can predict the early pathology of cardiovascular system.</abstract><cop>United States</cop><pub>Elsevier Ltd</pub><pmid>33341465</pmid><doi>10.1016/j.compmedimag.2020.101819</doi><tpages>1</tpages></addata></record> |
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| ispartof | Computerized medical imaging and graphics, 2021-01, Vol.87, p.101819-101819, Article 101819 |
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| language | eng |
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| source | Elsevier ScienceDirect Journals |
| subjects | Deep learning Displacement tracking Pulse wave imaging Single target block matching Vascular biomechanics |
| title | A new deep learning method for displacement tracking from ultrasound RF signals of vascular walls |
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