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dc.contributor.authorUghi, Giovanni J.
dc.contributor.authorVerjans, Johan
dc.contributor.authorFard, Ali M.
dc.contributor.authorWang, Hao
dc.contributor.authorOsborn, Eric
dc.contributor.authorHara, Tetsuya
dc.contributor.authorMauskapf, Adam
dc.contributor.authorJaffer, Farouc A.
dc.contributor.authorTearney, Guillermo J.
dc.date.accessioned2016-12-09T22:15:28Z
dc.date.available2016-12-09T22:15:28Z
dc.date.issued2014-10
dc.date.submitted2014-03
dc.identifier.issn1569-5794
dc.identifier.issn1573-0743
dc.identifier.urihttp://hdl.handle.net/1721.1/105793
dc.description.abstractIntravascular optical coherence tomography (IVOCT) is a well-established method for the high-resolution investigation of atherosclerosis in vivo. Intravascular near-infrared fluorescence (NIRF) imaging is a novel technique for the assessment of molecular processes associated with coronary artery disease. Integration of NIRF and IVOCT technology in a single catheter provides the capability to simultaneously obtain co-localized anatomical and molecular information from the artery wall. Since NIRF signal intensity attenuates as a function of imaging catheter distance to the vessel wall, the generation of quantitative NIRF data requires an accurate measurement of the vessel wall in IVOCT images. Given that dual modality, intravascular OCT–NIRF systems acquire data at a very high frame-rate (>100 frames/s), a high number of images per pullback need to be analyzed, making manual processing of OCT–NIRF data extremely time consuming. To overcome this limitation, we developed an algorithm for the automatic distance-correction of dual-modality OCT–NIRF images. We validated this method by comparing automatic to manual segmentation results in 180 in vivo images from six New Zealand White rabbit atherosclerotic after indocyanine-green injection. A high Dice similarity coefficient was found (0.97 ± 0.03) together with an average individual A-line error of 22 µm (i.e., approximately twice the axial resolution of IVOCT) and a processing time of 44 ms per image. In a similar manner, the algorithm was validated using 120 IVOCT clinical images from eight different in vivo pullbacks in human coronary arteries. The results suggest that the proposed algorithm enables fully automatic visualization of dual modality OCT–NIRF pullbacks, and provides an accurate and efficient calibration of NIRF data for quantification of the molecular agent in the atherosclerotic vessel wall.en_US
dc.description.sponsorshipNational Institutes of Health (U.S.) (NIH R01HL093717)en_US
dc.description.sponsorshipMerck & Co., Inc.en_US
dc.publisherSpringer Netherlandsen_US
dc.relation.isversionofhttp://dx.doi.org/10.1007/s10554-014-0556-zen_US
dc.rightsArticle 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.en_US
dc.sourceSpringer Netherlandsen_US
dc.titleDual modality intravascular optical coherence tomography (OCT) and near-infrared fluorescence (NIRF) imaging: a fully automated algorithm for the distance-calibration of NIRF signal intensity for quantitative molecular imagingen_US
dc.typeArticleen_US
dc.identifier.citationUghi, Giovanni J., Johan Verjans, Ali M. Fard, Hao Wang, Eric Osborn, Tetsuya Hara, Adam Mauskapf, Farouc A. Jaffer, and Guillermo J. Tearney. “Dual Modality Intravascular Optical Coherence Tomography (OCT) and Near-Infrared Fluorescence (NIRF) Imaging: a Fully Automated Algorithm for the Distance-Calibration of NIRF Signal Intensity for Quantitative Molecular Imaging.” Int J Cardiovasc Imaging 31, no. 2 (October 24, 2014): 259–268.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technologyen_US
dc.contributor.mitauthorTearney, Guillermo J.
dc.relation.journalThe International Journal of Cardiovascular Imagingen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2016-08-18T15:20:17Z
dc.language.rfc3066en
dc.rights.holderSpringer Science+Business Media Dordrecht
dspace.orderedauthorsUghi, Giovanni J.; Verjans, Johan; Fard, Ali M.; Wang, Hao; Osborn, Eric; Hara, Tetsuya; Mauskapf, Adam; Jaffer, Farouc A.; Tearney, Guillermo J.en_US
dspace.embargo.termsNen
mit.licensePUBLISHER_POLICYen_US
mit.metadata.statusComplete


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