Ultrahigh speed spectral-domain optical coherence microscopy

Lee, Hsiang‐Chieh; Liu, Jonathan J.; Sheikine, Yuri; Aguirre, Aaron D.; Connolly, James L.; Fujimoto, James G.

doi:10.1364/boe.4.001236

Cited by 26 publications

(15 citation statements)

References 45 publications

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“…In addition, variations in path length delay arising from the non-coincident pivot locations of the galvanometer mirrors in scanning microscope systems produce a curved en face image surface which does not match the objective focal plane [86]. Fourier-domain detection enables simultaneous imaging of multiple depths, which reduces the complexity of acquiring en face images and enables the reconstruction of en face images at multiple depths [87,88]. Post-processing algorithms may be applied to volumetric Fourier-domain OCM data in order to compensate for path length variations across the scan field as well as dispersion mismatch between sample and reference arms [89,90].…”

Section: High-speed Microscopy and Endoscopymentioning

confidence: 99%

Ultrahigh Speed OCT

Grulkowski

Liu

Potsaid

et al. 2015

Optical Coherence Tomography

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Section: High-speed Microscopy and Endoscopymentioning

confidence: 99%

Ultrahigh Speed OCT

Grulkowski

Liu

Potsaid

et al. 2015

Optical Coherence Tomography

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“…For example, an AOD with a 100 kHz scan rate (v ∼ 0.1) has been employed for nonfluorescent imaging [30]. In addition, the swept-source originally developed for optical coherence tomography (OCT) and spectrallyencoded imaging in a longer near-infrared (NIR) range (∼800-1500 nm), can now achieve a swept-rate of >100 kHz [31][32][33][34]. If this swept-source can be realized in the visible range, a spectral-encoded laser-scanning solution at multihundreds kHz could also be a viable solution to scale up the speed of LSFM to the limit.…”

Section: A Relationship Between Spatial Resolution and Scanning Speedmentioning

confidence: 99%

Speed-dependent resolution analysis of ultrafast laser-scanning fluorescence microscopy

Chan

Wong

et al. 2014

J. Opt. Soc. Am. B

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The image resolution of an aberration-corrected laser-scanning fluorescence microscopy (LSFM) system, like all other classical optical imaging modalities, is ultimately governed by diffraction limit and can be, in practice, influenced by the noise. However, consideration of only these two parameters is not adequate for LSFM with ultrafast laser-scanning, in which the dwell time of each resolvable image point becomes comparable with the fluorescence lifetime. In view of the continuing demand for faster LSFM, we here revisit the theoretical framework of LSFM and investigate the impact of the scanning speed on the resolution. In particular, we identify there are different speed regimes and excitation conditions in which the resolution is primarily limited by diffraction limit, fluorescence lifetime, or intrinsic noise. Our model also suggests that the speed of the current laser-scanning technologies is still at least an order of magnitude below the limit (∼sub-MHz to MHz), at which the diffraction-limited resolution can be preserved. We thus anticipate that the present study can provide new insight for practical designs and implementation of ultrafast LSFM, based on emerging laser-scanning techniques, e.g., ultrafast wavelength-swept sources, or optical time-stretch.

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“…Optical coherence tomography (OCT) provides an alternative non-invasive optical imaging modality that can provide 3D, high-resolution images of biological tissue architectures without staining (Huang et al, 1991; Fujimoto, 2003; Fujimoto et al, 2000; Tearney et al, 1997b). Optical coherence microscopy (OCM) combines the advantages of OCT and confocal microscopy using high numerical aperture objectives to provide cellular resolution images (Izatt et al, 1994; Aguirre et al, 2010b,a; Ahsen et al, 2013; Lee et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy

Wan

Lee²,

Huang

et al. 2017

Medical Image Analysis

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This paper proposes a texture analysis technique that can effectively classify different types of human breast tissue imaged by Optical Coherence Microscopy (OCM). OCM is an emerging imaging modality for rapid tissue screening and has the potential to provide high resolution microscopic images that approach those of histology. OCM images, acquired without tissue staining, however, pose unique challenges to image analysis and pattern classification. We examined multiple types of texture features and found Local Binary Pattern (LBP) features to perform better in classifying tissues imaged by OCM. In order to improve classification accuracy, we propose novel variants of LBP features, namely average LBP (ALBP) and block based LBP (BLBP). Compared with the classic LBP feature, ALBP and BLBP features provide an enhanced encoding of the texture structure in a local neighborhood by looking at intensity differences among neighboring pixels and among certain blocks of pixels in the neighborhood. Fourty-six freshly excised human breast tissue samples, including 27 benign (e.g. fibroadenoma, fibrocystic disease and usual ductal hyperplasia) and 19 breast carcinoma (e.g. invasive ductal carcinoma, ductal carcinoma in situ and lobular carcinoma in situ) were imaged with large field OCM with an imaging area of 10×10mm2 (10, 000 × 10, 000 pixels) for each sample. Corresponding H&E histology was obtained for each sample and used to provide ground truth diagnosis. 4310 small OCM image blocks (500 × 500 pixels) each paired with corresponding H&E histology was extracted from large-field OCM images and labeled with one of the five different classes: adipose tissue (n = 347), fibrous stroma (n = 2,065), breast lobules (n = 199), carcinomas (pooled from all sub-types, n = 1,127), and background (regions outside of the specimens, n = 572). Our experiments show that by integrating a selected set of LBP and the two new variant (ALBP and BLBP) features at multiple scales, the classification accuracy increased from 81.7% (using LBP features alone) to 93.8% using a neural network classifier. The integrated feature was also used to classify large-field OCM images for tumor detection. A receiver operating characteristic (ROC) curve was obtained with an area under the curve value of 0.959. A sensitivity level of 100% and specificity level of 85.2% was achieved to differentiate benign from malignant samples. Several other experiments also demonstrate the complementary nature of LBP and the two variants (ALBP and BLBP features) and the significance of integrating these texture features for classification. Using features from multiple scales and performing feature selection are also effective mechanisms to improve accuracy while maintaining computational efficiency.

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Ultrahigh speed spectral-domain optical coherence microscopy

Cited by 26 publications

References 45 publications

Ultrahigh Speed OCT

Ultrahigh Speed OCT

Speed-dependent resolution analysis of ultrafast laser-scanning fluorescence microscopy

Integrated local binary pattern texture features for classification of breast tissue imaged by optical coherence microscopy

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