Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography

Kasseck, Christoph; Jaedicke, Volker; Gerhardt, Nils C.; Welp, Hubert; Hofmann, Martin R.

doi:10.1016/j.optcom.2010.06.073

Cited by 17 publications

(10 citation statements)

References 28 publications

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“…At the same time, the reliability of OCT measurements can be significantly increased by designing of adequate algorithms for solution of the OCT inverse ill-posed problems. [34][35][36] However, speckle and scattering noise of the OCT signal prevents the data reconstruction; [37][38][39][40] thus, finding an appropriate denoising technique remains one of the challenging problems of OCT technologies.…”

Section: Introductionmentioning

confidence: 99%

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

et al. 2018

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We present the nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography (OCT). It employs an experimental training algorithm based on imaging of a test-object, made of the colloidal suspension of the monodisperse nanoparticles and contains the microscale inclusions. The geometry and the scattering properties of the test-object are known a priori allowing us to set the criteria for the training algorithm. Using a wide set of the wavelet kernels and the wavelet-domain filtration approaches, the appropriate filter is constructed based on the test-object imaging. We apply the proposed approach and chose an efficient wavelet denoising procedure by considering the combinations of the decomposition basis from five wavelet families with eight types of the filtration threshold. We demonstrate applicability of the wavelet-filtering for the in vitro OCT image of human brain meningioma. The observed results prove high efficiency of the proposed OCT image denoising technique.

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Section: Introductionmentioning

confidence: 99%

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

et al. 2018

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“…Here the spectroscopic analysis is performed first with a narrow and then with a broad window and both results are combined by multiplication [11]. The dual window method as well as pattern recognition and machine learning algorithms were used in this study to enhance contrast between different images based on spectroscopic features [12][13] [14]. …”

Section: Introductionmentioning

confidence: 99%

Spectral domain optical coherence tomography for ex vivo brain tumor analysis

et al. 2015

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Non-contact imaging methods to distinguish between healthy tissue and brain tumor tissue during surgery would be highly desirable but are not yet available. Optical Coherence Tomography (OCT) is a non-invasive imaging technology with a resolution around 1-15 μm and a penetration depth of 1-2 mm that may satisfy the demands. To analyze its potential, we measured ex vivo human brain tumor tissue samples from 10 patients with a Spectral Domain OCT system (Thorlabs Callisto: center wavelength of 930 nm) and compared the results with standard histology. In detail, three different measurements were made for each sample. First the sample was measured directly after surgery. Then it was embedded in paraffin (also H&E staining) and examined for the second time. At last, the slices of each paraffin block cut by the pathology were measured. Each time a B-scan was created and for a better comparison with the histology a 3D image was generated, in order to get the corresponding en face images. In both, histopathological diagnosis and the analysis of the OCT images, different types of brain tumor showed difference in structure. This has been affirmed by two blinded investigators. Nevertheless the difference between two images of samples taken directly after surgery is less distinct. To enhance the contrast in the images further, we employ Spectroscopic OCT and pattern recognition algorithms and compare these results to the histopathological standard.

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“…It was introduced by Morgner et al in terms of time-domain OCT [1] and Leitgeb et al in terms of spectral-domain OCT [2]. Among different applications [3][4][5][6], several attempts of applying this technique for a blood analysis have appeared [7][8][9]. Other authors have also presented theoretical analyses of various methods for the time-frequency signal analysis of sOCT [10][11][12].…”

Section: Introductionmentioning

confidence: 99%

Quantitative comparison of analysis methods for spectroscopic optical coherence tomography: comment

Kraszewski¹,

Trojanowski²,

Strąkowski³

2014

Biomed. Opt. Express

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In a recent paper by Bosschaart et al. [Biomed. Opt. Express 4, 2570 (2013)] various algorithms of time-frequency signal analysis have been tested for their performance in blood analysis with spectroscopic optical coherence tomography (sOCT). The measurement of hemoglobin concentration and oxygen saturation based on blood absorption spectra have been considered. Short time Fourier transform (STFT) was found as the best method for the measurement of blood absorption spectra. STFT was superior to other methods, such as dual window Fourier transform. However, the algorithm proposed by Bosschaart et al. significantly underestimates values of blood oxygen saturation. In this comment we show that this problem can be solved by thorough design of STFT algorithm. It requires the usage of a non-gaussian shape of STFT window that may lead to an excellent reconstruction of blood absorption spectra from OCT interferograms. Our study shows that sOCT can be potentially used for estimating oxygen saturation of blood with the accuracy below 1% and the spatial resolution of OCT image better than 20 μm.

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Substance identification by depth resolved spectroscopic pattern reconstruction in frequency domain optical coherence tomography

Cited by 17 publications

References 28 publications

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

Nanoparticle-enabled experimentally trained wavelet-domain denoising method for optical coherence tomography

Spectral domain optical coherence tomography for ex vivo brain tumor analysis

Quantitative comparison of analysis methods for spectroscopic optical coherence tomography: comment

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