Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images
Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorb...
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| Format: | Article |
| Language: | English |
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SPIE-Intl Soc Optical Eng
2020
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| Online Access: | https://hdl.handle.net/1721.1/126553 |
| _version_ | 1811079311306784768 |
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| author | Edelman, Elazer R |
| author2 | Massachusetts Institute of Technology. Institute for Medical Engineering & Science |
| author_facet | Massachusetts Institute of Technology. Institute for Medical Engineering & Science Edelman, Elazer R |
| author_sort | Edelman, Elazer R |
| collection | MIT |
| description | Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging - they are relatively invisible via angiography - and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R 2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images. |
| first_indexed | 2024-09-23T11:13:11Z |
| format | Article |
| id | mit-1721.1/126553 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T11:13:11Z |
| publishDate | 2020 |
| publisher | SPIE-Intl Soc Optical Eng |
| record_format | dspace |
| spelling | mit-1721.1/1265532022-09-27T17:58:38Z Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images Edelman, Elazer R Massachusetts Institute of Technology. Institute for Medical Engineering & Science Massachusetts Institute of Technology. Center for Biomedical Engineering Polymeric endovascular implants are the next step in minimally invasive vascular interventions. As an alternative to traditional metallic drug-eluting stents, these often-erodible scaffolds present opportunities and challenges for patients and clinicians. Theoretically, as they resorb and are absorbed over time, they obviate the long-term complications of permanent implants, but in the short-term visualization and therefore positioning is problematic. Polymeric scaffolds can only be fully imaged using optical coherence tomography (OCT) imaging - they are relatively invisible via angiography - and segmentation of polymeric struts in OCT images is performed manually, a laborious and intractable procedure for large datasets. Traditional lumen detection methods using implant struts as boundary limits fail in images with polymeric implants. Therefore, it is necessary to develop an automated method to detect polymeric struts and luminal borders in OCT images; we present such a fully automated algorithm. Accuracy was validated using expert annotations on 1140 OCT images with a positive predictive value of 0.93 for strut detection and an R 2 correlation coefficient of 0.94 between detected and expert-annotated lumen areas. The proposed algorithm allows for rapid, accurate, and automated detection of polymeric struts and the luminal border in OCT images. National Institutes of Health (U.S.) (Grant GM 49039) 2020-08-13T13:59:54Z 2020-08-13T13:59:54Z 2018-03 2018-02 2019-10-09T18:06:27Z Article http://purl.org/eprint/type/JournalArticle 1083-3668 https://hdl.handle.net/1721.1/126553 Amrute, Junedh M. et al. “Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images.” Journal of biomedical optics, vol. 23, no. 3, 2018, article 036010 © 2018 The Author(s) en 10.1117/1.JBO.23.3.036010 Journal of biomedical optics Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf SPIE-Intl Soc Optical Eng SPIE |
| spellingShingle | Edelman, Elazer R Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images |
| title | Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images |
| title_full | Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images |
| title_fullStr | Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images |
| title_full_unstemmed | Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images |
| title_short | Polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images |
| title_sort | polymeric endovascular strut and lumen detection algorithm for intracoronary optical coherence tomography images |
| url | https://hdl.handle.net/1721.1/126553 |
| work_keys_str_mv | AT edelmanelazerr polymericendovascularstrutandlumendetectionalgorithmforintracoronaryopticalcoherencetomographyimages |