Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?

Baumann, Bernhard; Merkle, Conrad W.; Leitgeb, Rainer A.; Augustin, Marco; Wartak, Andreas; Pircher, Michael; Hitzenberger, Christoph K.

doi:10.1364/boe.10.005755

Cited by 56 publications

(27 citation statements)

References 46 publications

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“…We note that for practical imaging of the human retina, value can sometimes be very small. It has been shown that the noise floor can bias measurement of when it is sufficiently small [28]. Empirically, we found this threshold for bias was near 10 dB.…”

Section: Quantifying Bd Improvementsupporting

confidence: 51%

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Balanced-detection visible-light optical coherence tomography

Rubinoff

Miller

Kuranov

et al. 2021

Preprint

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Increases in speed and sensitivity enabled rapid clinical adoption of optical coherence tomography (OCT) in ophthalmology. Recently visible-light OCT (vis-OCT) achieved ultrahigh axial resolution, improved tissue contrast, and new functional imaging capabilities, demonstrating the potential to improve clincal care further. However, limited speed and sensitivity caused by the high relative intensity noise (RIN) in supercontinuum lasers impeded the clinical adoption of vis-OCT. To overcome these limitations, we developed balanced-detection vis-OCT (BD-vis-OCT), which uses two calibrated spectrometers to cancel noises common to sample and reference arms, including RIN. We analyzed the RIN to achieve a robust pixel-to-pixel calibration between the two spectrometers and showed that BD-vis-OCT enhanced system sensitivity by up to 22.2 dB. We imaged healthy volunteers at an A-line rate of 125 kHz and a field-of-view as large as 10 mm × 4 mm. We found that BD-vis-OCT revealed retinal anatomical features previously obscured by the noise floor.

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Section: Quantifying Bd Improvementsupporting

confidence: 51%

“…between 0 and 1). With a higher gain, it takes less reference power to saturate the camera, thereby reducing the contribution of RIN to the SD A-line [28]. Fig.…”

Section: Imaging Phantom Eyeballmentioning

confidence: 99%

Balanced-detection visible-light optical coherence tomography

Rubinoff

Miller

Kuranov

et al. 2021

Preprint

View full text Add to dashboard Cite

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“…As SNR used here refers to the ratio of pure signal (i.e. only signal without noise floor) and the standard deviation (SD) of the noise signal:

SNR = \frac{μ_{1}^{2} + μ_{2}^{2}}{2 σ^{2}},

we denote it as SNR similar to our recent work [16]. The complex signals in both channels were then computed as Beckmann random noise distributions, that is, binormally distributed complex signals with identical SD σ along both the real axis and the imaginary axis, offset by the relative signal strengths μ 1,2 .…”

Section: Methodsmentioning

confidence: 99%

Improved accuracy of quantitative birefringence imaging by polarization sensitive OCT with simple noise correction and its application to neuroimaging

Baumann

Harper

Eugui

et al. 2021

Journal of Biophotonics

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Polarization‐sensitive optical coherence tomography (PS‐OCT) enables three‐dimensional imaging of biological tissues based on the inherent contrast provided by scattering and polarization properties. In fibrous tissue such as the white matter of the brain, PS‐OCT allows quantitative mapping of tissue birefringence. For the popular PS‐OCT layout using a single circular input state, birefringence measurements are based on a straight‐forward evaluation of phase retardation data. However, the accuracy of these measurements strongly depends on the signal‐to‐noise ratio (SNR) and is prone to mapping artifacts when the SNR is low. Here we present a simple yet effective approach for improving the accuracy of PS‐OCT phase retardation and birefringence measurements. By performing a noise bias correction of the detected OCT signal amplitudes, the impact of the noise floor on retardation measurements can be markedly reduced. We present simulation data to illustrate the influence of the noise bias correction on phase retardation measurements and support our analysis with real‐world PS‐OCT image data.

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“…The numerical correction for the dispersion mismatch between the reference and sample arm is done with a fourth-order polynomial, whose coefficients are obtained from a reference measurement of a single reflector [28]. After processing, the A-scans belonging to the same scan line are complex averaged to improve the SNR [29]. For segmentation, the absolute value of the complex averaged scan lines is used directly.…”

Section: A Experimental Oct Setupmentioning

confidence: 99%

Quantification of plant morphology and leaf thickness with optical coherence tomography

et al. 2020

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Optical coherence tomography (OCT) can be a valuable imaging tool for in vivo and label-free digital plant phenotyping. However, for imaging leaves, air-filled cavities limit the penetration depth and reduce the image quality. Moreover, up to now quantification of leaf morphology with OCT has been done in one-dimensional or twodimensional images only, and has often been limited to relative measurements. In this paper, we demonstrate a significant increase in OCT imaging depth and image quality by infiltrating the leaf air spaces with water. In the obtained high-quality OCT images the top and bottom surface of the leaf are digitally segmented. Moreover, high-quality en face images of the leaf are obtained from numerically flattened leaves. Segmentation in threedimensional OCT images is used to quantify the spatially resolved leaf thickness. Based on a segmented leaf image, the refractive index of an infiltrated leaf is measured to be 1.345 ± 0.004, deviating only 1.2% from that of pure water. Using the refractive index and a correction for refraction effects at the air-leaf interface, we quantitatively mapped the leaf thickness. The results show that OCT is an efficient and promising technique for quantitative phenotyping on leaf and tissue level.

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Signal averaging improves signal-to-noise in OCT images: But which approach works best, and when?

Cited by 56 publications

References 46 publications

Balanced-detection visible-light optical coherence tomography

Balanced-detection visible-light optical coherence tomography

Improved accuracy of quantitative birefringence imaging by polarization sensitive OCT with simple noise correction and its application to neuroimaging

Quantification of plant morphology and leaf thickness with optical coherence tomography

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