Paper
11 February 2011 Lateral resonant Doppler imaging for quantitative flow extraction in spectral domain optical coherence tomography
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Abstract
In spectral domain Doppler OCT, any transverse motion component of the obliquely moving sample relative to the incident sample beam causes a damping of the correlation between subsequent backscattering signals or even the loss of it making a phase-resolved Doppler flow analysis difficult because of the strong mean error of the Doppler phase shift. To circumvent this effect, a new method for resonant Doppler flow imaging and quantification in spectral domain OCT is proposed where the scanner movement velocity is approximately matched to the transverse velocity component of the oblique sample motion similar to a tracking shot where the camera is moved with respect to the sample. As a result, the backscattering signals corresponding to the moving sample will be highly correlated whereas those of static sample structures and slowly moving scatterers will be less correlated and damped depending on the scanner velocity. Advantageously, for the exact flow velocity quantification the new Doppler relationship of phase shift and sample velocity has not to be applied and the linear relation of the classic Doppler model can still be used. In the present work, first results of the lateral resonant Doppler imaging are shown for an 1 percent Intralipid flow phantom study.
© (2011) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Julia Walther, Peter Cimalla, and Edmund Koch "Lateral resonant Doppler imaging for quantitative flow extraction in spectral domain optical coherence tomography", Proc. SPIE 7889, Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XV, 788914 (11 February 2011); https://doi.org/10.1117/12.874522
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Cited by 1 scholarly publication.
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KEYWORDS
Doppler tomography

Doppler effect

Optical coherence tomography

Phase shifts

Capillaries

Scanners

Backscatter

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