Tissue perfusion modelling in optical coherence tomography

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dc.contributor.authorŠtohanzlová, Petracs
dc.contributor.authorKolář, Radimcs
dc.coverage.issue1cs
dc.coverage.volume16cs
dc.date.accessioned2020-08-04T11:00:16Z
dc.date.available2020-08-04T11:00:16Z
dc.date.issued2017-02-08cs
dc.description.abstractBackground Optical coherence tomography (OCT) is a well established imaging technique with different applications in preclinical research and clinical practice. The main potential for its application lies in the possibility of noninvasively performing “optical biopsy”. Nevertheless, functional OCT imaging is also developing, in which perfusion imaging is an important approach in tissue function study. In spite of its great potential in preclinical research, advanced perfusion imaging using OCT has not been studied. Perfusion analysis is based on administration of a contrast agent (nanoparticles in the case of OCT) into the bloodstream, where during time it specifically changes the image contrast. Through analysing the concentration-intensity curves we are then able to find out further information about the examined tissue. Methods We have designed and manufactured a tissue mimicking phantom that provides the possibility of measuring dilution curves in OCT sequence with flow rates 200, 500, 1000 and 2000 L/min. The methodology comprised of using bolus of 50 L of gold nanorods as a contrast agent (with flow rate 5000 L/min) and continuous imaging by an OCT system. After data acquisition, dilution curves were extracted from OCT intensity images and were subjected to a deconvolution method using an input–output system description. The aim of this was to obtain impulse response characteristics for our model phantom within the tissue mimicking environment. Four mathematical tissue models were used and compared: exponential, gamma, lagged and LDRW. Results We have shown that every model has a linearly dependent parameter on flow (R2 values from 0.4914 to 0.9996). We have also shown that using different models can lead to a better understanding of the examined model or tissue. The lagged model surpassed other models in terms of the minimisation criterion and R2 value. Conclusions We used a tissue mimicking phantom in our study and showed that OCT can be used for advanced perfusion analysis using mathematical model and deconvolution approach. The lagged model with three parameters is the most appropriate model. Nevertheless, further research have to be performed, particularly with real tissue.en
dc.formattextcs
dc.format.extent1-16cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationBIOMED ENG ONLINE. 2017, vol. 16, issue 1, p. 1-16.en
dc.identifier.doi10.1186/s12938-017-0320-4cs
dc.identifier.issn1475-925Xcs
dc.identifier.other133044cs
dc.identifier.urihttp://hdl.handle.net/11012/63816
dc.language.isoencs
dc.publisherBioMed Centralcs
dc.relation.ispartofBIOMED ENG ONLINEcs
dc.relation.urihttp://www.biomedical-engineering-online.com/content/16/1/27cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/1475-925X/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectOptical coherence tomographyen
dc.subjectPerfusion analysisen
dc.subjectDeconvolutionen
dc.subjectModel Phantomen
dc.subjectImpulse responseen
dc.titleTissue perfusion modelling in optical coherence tomographyen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-133044en
sync.item.dbtypeVAVen
sync.item.insts2020.08.04 13:00:15en
sync.item.modts2020.08.04 12:25:32en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav biomedicínského inženýrstvícs
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