Here, we analytically study the signal digitization procedure in FD-OCT and propose a novel mixed-signal framework to model its time-domain image formation. It turns out that FD-OCT is a shift-variant system, if the conventional IDFT-based technique is used to reconstruct the A-lines. Specifically, both amplitude and phase responses of the system are dependent on the axial location of the input sample. We believe this finding could provide us with new insights towards the image reconstruction of FD-OCT and guide researchers to develop better reconstruction algorithms in the future.
We use the prototype swept-source (SS) OCT at 1060 nm to image human Schlemm’s canal (SC) and perform full reconstruction in the en face plane. Compared with spectral-domain (SD) OCT systems at 800 nm, the SS OCT system at 1060 nm offers deeper signal penetration, and has no sensitivity roll-off effect to allow for better localization and delineation of SC. One volumetric scan was taken from each of the eight cardinal positions to cover the entire SC circumferentially around the limbus. The en face slices were taken from each volume at the SC region, and were stitched together to generate an en face representation of the 360 deg SC.
We investigate the influence of the OCT system resolution on high-quality en face corneal endothelial cell images in vivo, to allow for quantitative analysis of cell density. We vary the lateral resolution of the ultrahigh-resolution (UHR) OCT system (centered at 850 nm) by using different objectives, and the axial resolution by windowing the source spectrum. We are able to obtain a high-quality en face corneal endothelial cell map in vivo using UHR OCT for the first time. Quantitative analysis result of cell density from in vivo en face corneal endothelial cell map agrees with previously reported data.
KEYWORDS: Optical coherence tomography, Polarization, Ranging, Space mirrors, Range imaging, Demodulation, Phase shifts, Fourier transforms, Mirrors, In vivo imaging
In Fourier domain OCT, the depth profile is mirrored about the zero delay, limiting the imaging depth to half of the entire ranging space. We present a novel configuration for OCT to robustly remove the complex conjugate artifact. Our method utilizes the intrinsic delay of circularly polarized light in two polarization channels, using only passive broadband polarization optics and conventional polarization diversity detection unit. Our method is immune to sample motion and adds no restrictions to source bandwidth, imaging speed or computational load. 45 dB suppression of the mirror artifact is demonstrated by an SSOCT with some in-vivo images.
The purpose of this study was to develop and evaluate the performance of a convolutional neural network (CNN) that uses a novel A-line based classification approach to detect cancer in OCT images of breast specimens. Deep learning algorithms have been developed for OCT ophthalmology applications using pixel-based classification approaches. In this study, a novel deep learning approach was developed that classifies OCT A-lines of breast tissue. De-identified human breast tissues from mastectomy and breast reduction specimens were excised from patients at Columbia University Medical Center. A total of 82 specimens from 49 patients were imaged with OCT, including both normal tissues and non-neoplastic tissues. The proposed algorithm utilized a hybrid 2D/1D convolutional neural network (CNN) to map each single B-scan to a 1D label vector, which were derived from manual annotation. Each A-line was labelled as one of the following tissue types: ductal carcinoma in situ (DCIS), invasive ductal carcinoma (IDC), adipose, and stroma. Five-fold cross-validation Dice scores across tissue types were: 0.82-0.95 for IDC, 0.54-0.75 for DCIS, 0.67-0.91 for adipose, and 0.61-0.86 for stroma. In a second experiment, IDC and DCIS were combined as a single tissue class (malignancy) while stroma and adipose were combined as a second tissue class (non-malignancy). In this setup, the experiment yielded five-fold cross-validation Dice scores between 0.89-0.93, respectively. Future work includes acquiring more patient samples and to compare the algorithm to previous works, including both deep learning and traditional automatic image processing methods for classification of breast tissue in OCT images.
Subcellular resolution is required for OCT to portray the microstructural information of myocardium issue that is comparable to histology. Compare with its intrinsic intensity contrast, functional OCT system may provide contrast related to the tissue composition. We present a high-resolution (HR) cross-polarization OCT system that can provide functional contrast of human myocardium tissue in one-shot measurement. The system is implemented based on our previously reported high-resolution long imaging range OCT system with minimal modification. It features a broadband supercontinuum source, single-channel and one-shot detection, with moderate signal processing. The system has an axial resolution of 3.07 μm, and it is capable to produce accurate polarization information by calibrating the reconstruction performance with a quarter wave plate. The orthogonal polarization channels are multiplexed to fit within one imaging range. Following CP-OCT detection, the retardation can be reconstructed based on the complex signals, and the depolarization effect can be depicted by the channel intensity ratio. Tissue specimens from ten fresh human hearts are used to demonstrate the capability of CP-OCT contrasts. By analyzing the intrinsic and functional OCT contrasts of fresh human myocardium tissues against histology slides, we show that various tissue structures and tissue types of the myocardium, such as fibrosis and ablated lesions, can be better depicted by the function contrasts. We also suggest the possibility of using A-line features from the two orthogonal polarization channels to distinguish normal myocardium, fibrotic myocardium, and ablated lesions. This may serve as a rapid and cost-efficient solution for assessment of myocardium and further facilitate automatic tissue classification.
We employed a home-built ultrahigh resolution (UHR) OCT system at 800nm to image human breast cancer sample ex vivo. The system has an axial resolution of 2.72µm and a lateral resolution of 5.52µm with an extended imaging range of 1.78mm. Over 900 UHR OCT volumes were generated on specimens from 23 breast cancer cases. With better spatial resolution, detailed structures in the breast tissue were better defined. Different types of breast cancer as well as healthy breast tissue can be well delineated from the UHR OCT images. To quantitatively evaluate the advantages of UHR OCT imaging of breast cancer, features derived from OCT intensity images were used as inputs to a machine learning model, the relevance vector machine. A trained machine learning model was employed to evaluate the performance of tissue classification based on UHR OCT images for differentiating tissue types in the breast samples, including adipose tissue, healthy stroma and cancerous region. For adipose tissue, grid-based local features were extracted from OCT intensity data, including standard deviation, entropy, and homogeneity. We showed that it was possible to enhance the classification performance on distinguishing fat tissue from non-fat tissue by using the UHR images when compared with the results based on OCT images from a commercial 1300 nm OCT system. For invasive ductal carcinoma (IDC) and normal stroma differentiation, the classification was based on frame-based features that portray signal penetration depth and tissue reflectivity. The confusing matrix indicated a sensitivity of 97.5% and a sensitivity of 77.8%.
Breast cancer is the third leading cause of death in women in the United States. In human breast tissue, adipose cells are infiltrated or replaced by cancer cells during the development of breast tumor. Therefore, an adipose map can be an indicator of identifying cancerous region. We developed an automated classification method to generate adipose map within human breast.
To facilitate the automated classification, we first mask the B-scans from OCT volumes by comparing the signal noise ratio with a threshold. Then, the image was divided into multiple blocks with a size of 30 pixels by 30 pixels. In each block, we extracted texture features such as local standard deviation, entropy, homogeneity, and coarseness. The features of each block were input to a probabilistic model, relevance vector machine (RVM), which was trained prior to the experiment, to classify tissue types. For each block within the B-scan, RVM identified the region with adipose tissue. We calculated the adipose ratio as the number of blocks identified as adipose over the total number of blocks within the B-scan.
We obtained OCT images from patients (n = 19) in Columbia medical center. We automatically generated the adipose maps from 24 B-scans including normal samples (n = 16) and cancerous samples (n = 8). We found the adipose regions show an isolated pattern that in cancerous tissue while a clustered pattern in normal tissue. Moreover, the adipose ratio (52.30 ± 29.42%) in normal tissue was higher than the that in cancerous tissue (12.41 ± 10.07%).
The ciliated epithelium is important to the human respiratory system because it clears mucus that contains harmful microorganisms and particulate matter. We report the ex vivo visualization of human trachea/bronchi ciliated epithelium and induced flow characterized by using spectral-domain optical coherence tomography (SD-OCT). A total number of 17 samples from 7 patients were imaged. Samples were obtained from Columbia University Department of Anesthesiology’s tissue bank. After excision, the samples were placed in Gibco Medium 199 solution with oxygen at 4°C until imaging. The samples were maintained at 36.7°C throughout the experiment. The imaging protocol included obtaining 3D volumes and 200 consecutive B-scans parallel to the head-to-feet direction (superior-inferior axis) of the airway, using Thorlabs Telesto system at 1300 nm at 28 kHz A-line rate and a custom built high resolution SDOCT system at 800nm at 32 kHz A-line rate. After imaging, samples were processed with H and E histology. Speckle variance of the time resolved datasets demonstrate significant contrast at the ciliated epithelium sites. Flow images were also obtained after injecting 10μm polyester beads into the solution, which shows beads traveling trajectories near the ciliated epithelium areas. In contrary, flow images taken in the orthogonal plane show no beads traveling trajectories. This observation is in line with our expectation that cilia drive flow predominantly along the superior-inferior axis. We also observed the protective function of the mucus, shielding the epithelium from the invasion of foreign objects such as microspheres. Further studies will be focused on the cilia’s physiological response to environmental changes such as drug administration and physical injury.
The directionality of collagen fibers across the anterior cruciate ligament (ACL) as well as the insertion of this key ligament into bone are important for understanding the mechanical integrity and functionality of this complex tissue. Quantitative analysis of three-dimensional fiber directionality is of particular interest due to the physiological, mechanical, and biological heterogeneity inherent across the ACL-to-bone junction, the behavior of the ligament under mechanical stress, and the usefulness of this information in designing tissue engineered grafts. We have developed an algorithm to characterize Optical Coherence Tomography (OCT) image volumes of the ACL. We present an automated algorithm for measuring ligamentous fiber angles, and extracting attenuation and backscattering coefficients of ligament, interface, and bone regions within mature and immature bovine ACL insertion samples. Future directions include translating this algorithm for real time processing to allow three-dimensional volumetric analysis within dynamically moving samples.
We present an initial study to describe the potential of optical coherence tomography (OCT) for characterizing
endomyocardial tissue. We obtained ventricular OCT images from 15 fresh human hearts. Layer thickness
measurements and texture features were extracted from volumetric datasets with endocardial thickening, normal
myocardium, myocardium with interstitial fibrosis, normal endocardium, and adipose tissue. Within our datasets, we
observed that the thickness of endocardium was not different within samples with normal or myocardium with interstitial
fibrosis, however samples with endocardial thickening showed statistically increased endocardial thickness.
We present an ultrahigh-resolution spectral domain optical coherence tomography (OCT) system in 800 nm with a low-noise supercontinuum source (SC) optimized for myocardial imaging. The system was demonstrated to have an axial resolution of 2.72 μm with a large imaging depth of 1.78 mm and a 6-dB falloff range of 0.89 mm. The lateral resolution (5.52 μm) was compromised to enhance the image penetration required for myocardial imaging. The noise of the SC source was analyzed extensively and an imaging protocol was proposed for SC-based OCT imaging with appreciable contrast. Three-dimensional datasets were acquired ex vivo on the endocardium side of tissue specimens from different chambers of fresh human and swine hearts. With the increased resolution and contrast, features such as elastic fibers, Purkinje fibers, and collagen fiber bundles were observed. The correlation between the structural information revealed in the OCT images and tissue pathology was discussed as well.
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