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In Vivo Supervised Analysis of Stent Reendothelialization From Optical Coherence Tomography.

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Title: In Vivo Supervised Analysis of Stent Reendothelialization From Optical Coherence Tomography.
Authors: Kauffmann, Claude1, Motreff, Pascal2,3, Sarry, Laurent1 laurent.sarry@u-clermont1.fr
Source: IEEE Transactions on Medical Imaging. Mar2010, Vol. 29 Issue 3, p807-818. 12p.
Subjects: Optical coherence tomography, Medical imaging systems, Diagnostic imaging, Coronary artery surgery, Surgical stents, Image processing
Abstract: The aim of this study is to interactively assess reendothelialization of stents at an accuracy of down to a few micrometer by analyzing endovascular optical coherence tomography (OCT) sequences. Vessel wall and stent struts are automatically detected by using morphological, gradient, and symmetry operators coupled with active contour models; alerts are issued to ask for user supervision over some extreme irregular geometries caused by thrombotic lesions or dissections. A complete distance map is then computed from sparse distances measured between wall and struts. Missing values are interpolated by thin-plate spline (TPS) functions. Accuracy and robustness are increased by taking into account the inhomogeneity of data points and integrating in the same framework orthogonalized forward selection of support points, optimal selection of regularization parameters by generalized cross-validation, and rejection of detection outliers. Validation is performed on simulated data, phantom acquisitions and 11 typical in vivo OCT sequences. The comparison against manual expert measurements demonstrates a bias of the order of OCT resolution (less than 10 m) and a standard deviation of the order of the strut width (less than 150 μm). [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: <searchLink fieldCode="JN" term="%22IEEE+Transactions+on+Medical+Imaging%22">IEEE Transactions on Medical Imaging</searchLink>. Mar2010, Vol. 29 Issue 3, p807-818. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Optical+coherence+tomography%22">Optical coherence tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Medical+imaging+systems%22">Medical imaging systems</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnostic+imaging%22">Diagnostic imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Coronary+artery+surgery%22">Coronary artery surgery</searchLink><br /><searchLink fieldCode="DE" term="%22Surgical+stents%22">Surgical stents</searchLink><br /><searchLink fieldCode="DE" term="%22Image+processing%22">Image processing</searchLink>
– Name: Abstract
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  Data: The aim of this study is to interactively assess reendothelialization of stents at an accuracy of down to a few micrometer by analyzing endovascular optical coherence tomography (OCT) sequences. Vessel wall and stent struts are automatically detected by using morphological, gradient, and symmetry operators coupled with active contour models; alerts are issued to ask for user supervision over some extreme irregular geometries caused by thrombotic lesions or dissections. A complete distance map is then computed from sparse distances measured between wall and struts. Missing values are interpolated by thin-plate spline (TPS) functions. Accuracy and robustness are increased by taking into account the inhomogeneity of data points and integrating in the same framework orthogonalized forward selection of support points, optimal selection of regularization parameters by generalized cross-validation, and rejection of detection outliers. Validation is performed on simulated data, phantom acquisitions and 11 typical in vivo OCT sequences. The comparison against manual expert measurements demonstrates a bias of the order of OCT resolution (less than 10 m) and a standard deviation of the order of the strut width (less than 150 μm). [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Medical Imaging is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1109/TMI.2009.2037755
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      – SubjectFull: Diagnostic imaging
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      – SubjectFull: Surgical stents
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              Text: Mar2010
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