Detecting precancerous lesions in the hamster cheek pouch using spectroscopic white-light optical coherence tomography to assess nuclear morphology via spectral oscillations

Deep learning classification of ex vivo human colon tissues using spectroscopic optical coherence tomography

Screening for colorectal cancer (CRC) with colonoscopy has improved patient outcomes; however, it remains the third leading cause of cancer-related mortality, novel strategies to improve screening are needed. Here, we propose an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). Depth resolved OCT images are analyzed as a function of wavelength to measure optical tissue properties and used as input to machine learning algorithms. Previously, we used this approach to analyze mouse colon polyps. Here, we extend the approach to examine human biopsied colonic epithelial tissue samples ex vivo. Optical properties are used as input to a novel deep learning architecture, producing accuracy of up to 97.9% in discriminating tissue type. SOCT parameters are used to create false colored en face OCT images and deep learning classifications are used to enable visual classification by tissue type. This study advances SOCT toward clinical utility for analysis of colonic epithelium.

Deep learning classification of ex vivo human colon tissues using spectroscopic OCT

Screening programs for colorectal cancer (CRC) have had a profound impact on the morbidity and mortality of this disease by detecting and removing early cancers and precancerous adenomas with colonoscopy. However, CRC continues to be the third leading cause of cancer-related mortality in both men and woman, partly because of limitations in colonoscopy-based screening. Thus, novel strategies to improve the efficiency and effectiveness of screening colonoscopy are urgently needed. Here, we propose to address this need using an optical biopsy technique based on spectroscopic optical coherence tomography (OCT). The depth resolved images obtained with OCT are analyzed as a function of wavelength to measure optical tissue properties. The optical properties can be used as input to machine learning algorithms as a means to classify adenomatous tissue in the colon. In this study, biopsied tissue samples from the colonic epithelium are analyzed ex vivo using spectroscopic OCT and tissue classifications are generated using a novel deep learning architecture, informed by machine learning methods including LSTM and KNN. The overall classification accuracy obtained was 88.9%, 76.0% and 97.9% in discriminating tissue type for these methods. Further, we apply an approach using false coloring of en face OCT images based on SOCT parameters and deep learning predictions to enable visual identification of tissue type. This study advances the spectroscopic OCT towards clinical utility for analyzing colonic epithelium for signs of adenoma.

Spectroscopic optical coherence tomography at 1200 nm for lipid detection

Significance

Spectroscopic analysis of optical coherence tomography (OCT) data can yield added information about the sample's chemical composition, along with high-resolution images. Typical commercial OCT systems operate at wavelengths that may not be optimal for identifying lipid-containing samples based on absorption features.

Aim

The main aim of this study was to develop a 1200 nm spectroscopic OCT (SOCT) for the classification of lipid-based and water-based samples by extracting the lipid absorption peak at 1210 nm from the OCT data.

Approach

We developed a 1200 nm OCT system and implemented a signal processing algorithm that simultaneously retrieves spectroscopic and structural information from the sample. In this study, we validated the performance of our OCT system by imaging weakly scattering phantoms with and without lipid absorption features. An orthogonal projections to latent structures-discriminant analysis (OPLS-DA) model was developed and applied to classify weakly scattering samples based on their absorption features.

Results

The OCT system achieved an axial resolution of 7.2    μ m and a sensitivity of 95 dB. The calibrated OPLS-DA model on weakly scattering samples with lipid and water-based absorption features correctly classified 19/20 validation samples.

Conclusions

The 1200 nm SOCT system can discriminate the lipid-containing weakly scattering samples from water-based weakly scattering samples with good predictive ability.

Spectroscopic optical coherence tomography for classification of colorectal cancer in a mouse model

Noninvasive diagnosis of the malignant potential of colon polyps can improve prevention of colorectal cancer without the need for time-consuming and expensive biopsies. This study examines the use of spectroscopic optical coherence tomography (OCT) to classify tissue from genetically engineered mouse models of early-stage adenoma (APC) and advanced adenocarcinoma (AKP) in which tumors are induced in the distal colon. The optical tissue properties of scattering power and scattering attenuation coefficient are evaluated by analyzing the imaging data collected from tissues. Classifications are generated using 2D linear discriminant analysis with high levels of discrimination obtained. The overall classification accuracy obtained was 91.5%, with 100% sensitivity and 96.7% specificity in separating tumors from benign tissue, and 77.8% sensitivity and 99.4% specificity in separating adenocarcinoma from nonmalignant tissue. Thus, this study demonstrates the clinical potential of using spectroscopic OCT for rapid detection of colon adenoma and colorectal cancer.

A review of low-cost and portable optical coherence tomography

Optical coherence tomography (OCT) is a powerful optical imaging technique capable of visualizing the internal structure of biological tissues at near cellular resolution. For years, OCT has been regarded as the standard of care in ophthalmology, acting as an invaluable tool for the assessment of retinal pathology. However, the costly nature of most current commercial OCT systems has limited its general accessibility, especially in low-resource environments. It is therefore timely to review the development of low-cost OCT systems as a route for applying this technology to population-scale disease screening. Low-cost, portable and easy to use OCT systems will be essential to facilitate widespread use at point of care settings while ensuring that they offer the necessary imaging performances needed for clinical detection of retinal pathology. The development of low-cost OCT also offers the potential to enable application in fields outside ophthalmology by lowering the barrier to entry. In this paper, we review the current development and applications of low-cost, portable and handheld OCT in both translational and research settings. Design and cost-reduction techniques are described for general low-cost OCT systems, including considerations regarding spectrometer-based detection, scanning optics, system control, signal processing, and the role of 3D printing technology. Lastly, a review of clinical applications enabled by low-cost OCT is presented, along with a detailed discussion of current limitations and outlook.

Correlation of the derivative as a robust estimator of scatterer size in optical coherence tomography (OCT)

The size-dependent spectral variations, predicted by Mie theory, have already been considered as a contrast enhancement mechanism in optical coherence tomography. In this work, a new spectroscopic metric, the bandwidth of the correlation of the derivative, was developed for estimating scatterer size which is more robust and accurate compared to existing methods. Its feasibility was demonstrated using phantoms containing polystyrene microspheres as well as images of normal and cancerous human colon. The results are very promising, suggesting that the proposed metric could be utilized for measuring nuclear size distribution, a diagnostically valuable marker, in human tissues.

Characterization of lipid-rich plaques using spectroscopic optical coherence tomography

Intravascular optical coherence tomography (IV-OCT) is a high-resolution imaging method used to visualize the internal structures of walls of coronary arteries in vivo. However, accurate characterization of atherosclerotic plaques with gray-scale IV-OCT images is often limited by various intrinsic artifacts. In this study, we present an algorithm for characterizing lipid-rich plaques with a spectroscopic OCT technique based on a Gaussian center of mass (GCOM) metric. The GCOM metric, which reflects the absorbance properties of lipids, was validated using a lipid phantom. In addition, the proposed characterization method was successfully demonstrated in vivo using an atherosclerotic rabbit model and was found to have a sensitivity and specificity of 94.3% and 76.7% for lipid classification, respectively.

Functional optical coherence tomography: principles and progress

In the past decade, several functional extensions of optical coherence tomography (OCT) have emerged, and this review highlights key advances in instrumentation, theoretical analysis, signal processing and clinical application of these extensions. We review five principal extensions: Doppler OCT (DOCT), polarization-sensitive OCT (PS-OCT), optical coherence elastography (OCE), spectroscopic OCT (SOCT), and molecular imaging OCT. The former three have been further developed with studies in both ex vivo and in vivo human tissues. This review emphasizes the newer techniques of SOCT and molecular imaging OCT, which show excellent potential for clinical application but have yet to be well reviewed in the literature. SOCT elucidates tissue characteristics, such as oxygenation and carcinogenesis, by detecting wavelength-dependent absorption and scattering of light in tissues. While SOCT measures endogenous biochemical distributions, molecular imaging OCT detects exogenous molecular contrast agents. These newer advances in functional OCT broaden the potential clinical application of OCT by providing novel ways to understand tissue activity that cannot be accomplished by other current imaging methodologies.

Depth resolved detection of lipid using spectroscopic optical coherence tomography

Optical frequency domain imaging (OFDI) can identify key components related to plaque vulnerability but can suffer from artifacts that could prevent accurate identification of lipid rich regions. In this paper, we present a model of depth resolved spectral analysis of OFDI data for improved detection of lipid. A quadratic Discriminant analysis model was developed based on phantom compositions known chemical mixtures and applied to a tissue phantom of a lipid-rich plaque. We demonstrate that a combined spectral and attenuation model can be used to predict the presence of lipid in OFDI images.

Comparison of different metrics for analysis and visualization in spectroscopic optical coherence tomography

Spectroscopic Optical Coherence Tomography (S-OCT) extracts depth resolved spectra that are inherently available from OCT signals. The back scattered spectra contain useful functional information regarding the sample, since the light is altered by wavelength dependent absorption and scattering caused by chromophores and structures of the sample. Two aspects dominate the performance of S-OCT: (1) the spectral analysis processing method used to obtain the spatially-resolved spectroscopic information and (2) the metrics used to visualize and interpret relevant sample features. In this work, we focus on the second aspect, where we will compare established and novel metrics for S-OCT. These concepts include the adaptation of methods known from multispectral imaging and modern signal processing approaches such as pattern recognition. To compare the performance of the metrics in a quantitative manner, we use phantoms with microsphere scatterers of different sizes that are below the system's resolution and therefore cannot be differentiated using intensity based OCT images. We show that the analysis of the spectral features can clearly separate areas with different scattering properties in multi-layer phantoms. Finally, we demonstrate the performance of our approach for contrast enhancement in bovine articular cartilage.

In vivo photothermal optical coherence tomography of gold nanorod contrast agents

Photothermal optical coherence tomography (PT-OCT) is a potentially powerful tool for molecular imaging. Here, we characterize PT-OCT imaging of gold nanorod (GNR) contrast agents in phantoms, and we apply these techniques for in vivo GNR imaging. The PT-OCT signal was compared to the bio-heat equation in phantoms, and in vivo PT-OCT images were acquired from subcutaneous 400 pM GNR Matrigel injections into mice. Experiments revealed that PT-OCT signals varied as predicted by the bio-heat equation, with significant PT-OCT signal increases at 7.5 pM GNR compared to a scattering control (p < 0.01) while imaging in common path configuration. In vivo PT-OCT images demonstrated an appreciable increase in signal in the presence of GNRs compared to controls. Additionally, in vivo PT-OCT GNR signals were spatially distinct from blood vessels imaged with Doppler OCT. We anticipate that the demonstrated in vivo PT-OCT sensitivity to GNR contrast agents is sufficient to image molecular expression in vivo. Therefore, this work demonstrates the translation of PT-OCT to in vivo imaging and represents the next step towards its use as an in vivo molecular imaging tool.

Detection of early colorectal cancer development in the azoxymethane rat carcinogenesis model with Fourier domain low coherence interferometry

Fourier domain low coherence interferometry (fLCI) is an emerging optical technique used to quantitatively assess cell nuclear morphology in tissue as a means of detecting early cancer development. In this work, we use the azoxymethane rat carcinogenesis model, a well characterized and established model for colon cancer research, to demonstrate the ability of fLCI to distinguish between normal and preneoplastic ex-vivo colon tissue. The results show highly statistically significant differences between the measured cell nuclear diameters of normal and azoxymethane-treated tissues, thus providing strong evidence that fLCI may be a powerful tool for non-invasive, quantitative detection of early changes associated with colorectal cancer development.

Assessing hemoglobin concentration using spectroscopic optical coherence tomography for feasibility of tissue diagnostics

Hemoglobin (Hb) concentration and oxygen saturation levels are important biomarkers for various diseases, including cancer. Here, we investigate the ability to measure these parameters for tissue using spectroscopic optical coherence tomography (SOCT). A parallel frequency domain OCT system is used with detection spanning the visible region of the spectrum (450 nm to 700 nm). Oxygenated and deoxygenated Hb absorbing phantoms are analyzed. The results show that Hb concentrations as low as 1.2 g/L at 1 mm can be retrieved indicating that both normal and cancerous tissue measurements may be obtained. However, measurement of oxygen saturation levels may not be achieved with this approach.

Measuring morphological features using light-scattering spectroscopy and Fourier-domain low-coherence interferometry

We present measurements of morphological features in a thick turbid sample using light-scattering spectroscopy (LSS) and Fourier-domain low-coherence interferometry (fLCI) by processing with the dual-window (DW) method. A parallel frequency domain optical coherence tomography (OCT) system with a white-light source is used to image a two-layer phantom containing polystyrene beads of diameters 4.00 and 6.98 mum on the top and bottom layers, respectively. The DW method decomposes each OCT A-scan into a time-frequency distribution with simultaneously high spectral and spatial resolution. The spectral information from localized regions in the sample is used to determine scatterer structure. The results show that the two scatterer populations can be differentiated using LSS and fLCI.