Optical Coherence Microscopy Images Research Articles

Tumor spheroid elasticity estimation using mechano-microscopy combined with a conditional generative adversarial network

Background and ObjectivesTechniques for imaging the mechanical properties of cells are needed to study how cell mechanics influence cell function and disease progression. Mechano-microscopy (a high-resolution variant of compression optical coherence elastography) generates elasticity images of a sample undergoing compression from the phase difference between optical coherence microscopy (OCM) B-scans. However, the existing mechano-microscopy signal processing chain (referred to as the algebraic method) assumes the sample stress is uniaxial and axially uniform, such that violation of these assumptions reduces the accuracy and precision of elasticity images. Furthermore, it does not account for prior information regarding the sample geometry or mechanical property distribution. In this study, we investigate the feasibility of training a conditional generative adversarial network (cGAN) to generate elasticity images from phase difference images of samples containing a cell spheroid embedded in a hydrogel. MethodsTo construct the cGAN training and simulated test sets, we generated 30,000 artificial elasticity images using a parametric model and computed the corresponding phase difference images using finite element analysis to simulate compression applied to the artificial samples. We also imaged real MCF7 breast tumor spheroids embedded in hydrogel using mechano-microscopy to construct the experimental test set and evaluated the cGAN using the algebraic elasticity images and co-registered OCM and confocal fluorescence microscopy (CFM) images. ResultsComparison with the simulated test set ground truth elasticity images shows the cGAN produces a lower root mean square error (median: 3.47 kPa, 95 % confidence interval (CI) [3.41, 3.52]) than the algebraic method (median: 4.91 kPa, 95 % CI [4.85, 4.97]). For the experimental test set, the cGAN elasticity images contain features resembling stiff nuclei at locations corresponding to nuclei seen in the algebraic elasticity, OCM, and CFM images. Furthermore, the cGAN elasticity images are higher resolution and more robust to noise than the algebraic elasticity images. ConclusionsThe cGAN elasticity images exhibit better accuracy, spatial resolution, sensitivity, and robustness to noise than the algebraic elasticity images for both simulated and real experimental data.

Attention LSTM U-Net model for Drosophila melanogaster heart tube segmentation in optical coherence microscopy images.

Optical coherence microscopy (OCM) imaging of the Drosophila melanogaster (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology in vivo. OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation. However, image artifacts caused by sample motion or reflections reduce the accuracy of the analysis. To improve the precision and efficiency of image data analysis, we developed an Attention LSTM U-Net model (FlyNet3.0), which incorporates an attention learning mechanism to track the beating fly heart in OCM images. The new model has improved the intersection over union (IOU) compared to FlyNet2.0 + with reflection artifacts from 86% to 89% and with movement from 81% to 89%. We also extended the capabilities of OCM analysis through the introduction of an automated, in vivo heart wall thickness measurement method, which has been validated on a Drosophila model of cardiac hypertrophy. This work will enable the comprehensive, non-invasive characterization of fly heart physiology in a high-throughput manner.

Characteristics and utility of high-resolution optical coherence microscopy images of endocervical canal lesions.

To investigate optical coherence microscopy (OCM) imaging features and the application value of these high-resolution images for identifying endocervical canal lesions (ECLs), which is a clinical dilemma in cervical cancer screening programs. In total, 520 OCM images were obtained by scanning the cervical canal lesions with an ultra-high-resolution OCM system (204 specimens from 73 patients). The OCM morphologic characteristics of ECLs were observed and summarized, and then 3 researchers performed a diagnostic test of OCM images of cervical canal lesions. The accuracy, sensitivity, specificity, positive predictive value, negative predictive value, 95% confidence interval of each parameter, and interinvestigator agreement (κ) were calculated. Normal endocervix, cysts, squamous metaplasia, high-grade squamous intraepithelial lesions involving glands, and invasive carcinoma had distinct OCM characteristics, which correlated well with corresponding H&E histologic sections. The accuracy, sensitivity, and specificity of the 3 researchers were 90.6%, 89.3% (95% CI, 86.5%-91.7%) and 91.6% (95% CI, 89.2%-93.5%), respectively. The positive predictive value was 90.1% (95% CI, 87.3%-92.4%), and the negative predictive value was 90.9% (95% CI, 88.5%-92.9%), with almost perfect agreement (κ = 0.874). The application of the OCM system in cervical canal lesions is feasible and could help improve detection of occult ECLs in cervical cancer screening programs. This study lays the foundation for further research on OCM in cervical canal lesions in vivo, which also has a potential impact on projecting pathologic evaluation beyond what is currently possible, perhaps globally.

Photoacoustic elasto-viscography and optical coherence microscopy for multi-parametric ex vivo brain imaging.

Optical coherence microscopy (OCM) has shown the importance of imaging ex vivo brain slices at the microscopic level for a better understanding of the disease pathology and mechanism. However, the current OCM-based techniques are mainly limited to providing the tissue's optical properties, such as the attenuation coefficient, scattering coefficient, and cell architecture. Imaging the tissue's mechanical properties, including the elasticity and viscosity, in addition to the optical properties, to provide a comprehensive multi-parametric assessment of the sample has remained a challenge. Here, we present an integrated photoacoustic elasto-viscography (PAEV) and OCM imaging system to measure the sample's optical absorption coefficient, attenuation coefficient, and mechanical properties, including elasticity and viscosity. The obtained mechanical and optical properties were consistent with anatomical features observed in the PAEV and OCM images. The elasticity and viscosity maps showed rich variations of microstructural mechanical properties of mice brain. In the reconstructed elasto-viscogram of brain slices, greater elasticity, and lower viscosity were observed in white matter than in gray matter. With the ability to provide multi-parametric properties of the sample, the PAEV-OCM system holds the potential for a more comprehensive study of brain disease pathology.

Advancing full-field metrology: rapid 3D imaging with geometric phase ferroelectric liquid crystal technology in full-field optical coherence microscopy.

Optical coherence microscopy (OCM) is a variant of OCT in which a high-numerical aperture lens is used. Full-field OCM (FF-OCM) is an emerging non-invasive, label-free, interferometric technique for imaging of surface structures or semi-transparent biomedical subjects with micron-scale resolutions. Different approaches to three dimensional full-field optical metrology are reviewed. The usual method for the phase-shifting technique in FF-OCM involves mechanically moving a mirror to change the optical path difference for obtaining en-face OCM images. However, with the use of a broadband source in FF-OCM, the phase shifts of different spectral components are not the same, resulting in the ambiguities in 3D image reconstruction. In this study, we demonstrate, by imaging tissues and cells, a unique geometric phase-shifter based on ferroelectric liquid crystal technology, to realize achromatic phase-shifting for rapid three-dimensional imaging in a FF-OCM system.

Study on the application of optical coherence microscopy in Hirschsprung's disease

To explore the clinical application value of optical coherence microscopy (OCM) in Hirschsprung’s disease. 109 HSCR patients were recuited in a Chinese hospital from January 2018 to July 2021. All the recruited patients underwent barium enema angiography preoperatively and the resected diseased intestinal tubes were evaluated intraoperatively. The OCM and the histopathological examination were performed successively on the surgical specimens, and the OCM images were compared with the relevant tissue sections to characterize different lesions. 10 non-HSCR fetal colorectal tissues at the same period were retained for OCM, the characteristics of which with and without HSCR under OCM imaging were analyzed. In the OCM images of in vitro tissue, it can be clearly observed that the scattering degree of HSCR narrow segment mucosal is high, glands and crypt structures are reduced or even atrophy, and the scattering degree of submucosal and intermuscular is low; In the dilated segment, the low scattering and high scattering are complex, and the muscle layer is obviously hypertrophy and structural disorder. Compared with the pathological findings, the OCM sensitivity, Kappa value, and AUC area reached 92.66%, 0.63, and 0.91, respectively. OCM can quickly and clearly display the structure of all layers of colorectal tissue, which is highly consistent with the corresponding histopathological examination results and has high sensitivity. which will provide a more reliable basis for OCM diagnosis of early HSCR, targeted biopsy and location of operative treatment, and has a certain potential for clinical application.

Rapid pseudo-H&E imaging using a fluorescence-inbuilt optical coherence microscopic imaging system.

A technique using Linnik-based optical coherence microscopy (OCM), with built-in fluorescence microscopy (FM), is demonstrated here to describe cellular-level morphology for fresh porcine and biobank tissue specimens. The proposed method utilizes color-coding to generate digital pseudo-H&E (p-H&E) images. Using the same camera, colocalized FM images are merged with corresponding morphological OCM images using a 24-bit RGB composition process to generate position-matched p-H&E images. From receipt of dissected fresh tissue piece to generation of stitched images, the total processing time is <15 min for a 1-cm2 specimen, which is on average two times faster than frozen-section H&E process for fatty or water-rich fresh tissue specimens. This technique was successfully used to scan human and animal fresh tissue pieces, demonstrating its applicability for both biobank and veterinary purposes. We provide an in-depth comparison between p-H&E and human frozen-section H&E images acquired from the same metastatic sentinel lymph node slice (∼10 µm thick), and show the differences, like elastic fibers of a tiny blood vessel and cytoplasm of tumor cells. This optical sectioning technique provides histopathologists with a convenient assessment method that outputs large-field H&E-like images of fresh tissue pieces without requiring any physical embedment.

Improved Diagnostic Imaging of Brain Tumors by Multimodal Microscopy and Deep Learning.

Fluorescence-guided surgery is a state-of-the-art approach for intraoperative imaging during neurosurgical removal of tumor tissue. While the visualization of high-grade gliomas is reliable, lower grade glioma often lack visible fluorescence signals. Here, we present a hybrid prototype combining visible light optical coherence microscopy (OCM) and high-resolution fluorescence imaging for assessment of brain tumor samples acquired by 5-aminolevulinic acid (5-ALA) fluorescence-guided surgery. OCM provides high-resolution information of the inherent tissue scattering and absorption properties of tissue. We here explore quantitative attenuation coefficients derived from volumetric OCM intensity data and quantitative high-resolution 5-ALA fluorescence as potential biomarkers for tissue malignancy including otherwise difficult-to-assess low-grade glioma. We validate our findings against the gold standard histology and use attenuation and fluorescence intensity measures to differentiate between tumor core, infiltrative zone and adjacent brain tissue. Using large field-of-view scans acquired by a near-infrared swept-source optical coherence tomography setup, we provide initial assessments of tumor heterogeneity. Finally, we use cross-sectional OCM images to train a convolutional neural network that discriminates tumor from non-tumor tissue with an accuracy of 97%. Collectively, the present hybrid approach offers potential to translate into an in vivo imaging setup for substantially improved intraoperative guidance of brain tumor surgeries.

FlyNet 2.0: drosophila heart 3D (2D + time) segmentation in optical coherence microscopy images using a convolutional long short-term memory neural network.

A custom convolutional neural network (CNN) integrated with convolutional long short-term memory (LSTM) achieves accurate 3D (2D + time) segmentation in cross-sectional videos of the Drosophila heart acquired by an optical coherence microscopy (OCM) system. While our previous FlyNet 1.0 model utilized regular CNNs to extract 2D spatial information from individual video frames, convolutional LSTM, FlyNet 2.0, utilizes both spatial and temporal information to improve segmentation performance further. To train and test FlyNet 2.0, we used 100 datasets including 500,000 fly heart OCM images. OCM videos in three developmental stages and two heartbeat situations were segmented achieving an intersection over union (IOU) accuracy of 92%. This increased segmentation accuracy allows morphological and dynamic cardiac parameters to be better quantified.

In Vivo Rat Brain Imaging through Full-Field Optical Coherence Microscopy Using an Ultrathin Short Multimode Fiber Probe

We demonstrate full-field optical coherence microscopy (OCM) using an ultrathin forward-imaging short multimode fiber (SMMF) probe with a core diameter of 50 μm, outer diameter of 125 μm, and length of 7.4 mm, which is a typical graded-index multimode fiber used for optical communications. The axial and lateral resolutions were measured to be 2.14 μm and 2.3 μm, respectively. By inserting the SMMF 4 mm into the cortex of an in vivo rat brain, scanning was performed to a depth of 147 μm from the SMMF facet with a field of view of 47 μm. Three-dimensional (3D) OCM images were obtained at depths ranging from approximately 20 μm to 90 μm. Based on the morphological information of the resliced 3D images and the dependence of the integration of the OCM image signal on the insertion length, the obtained 3D information of nerve fibers has been presented.

Computer-Aided Diagnosis of Label-Free 3-D Optical Coherence Microscopy Images of Human Cervical Tissue.

Objective:Ultrahigh-resolution optical coherence microscopy (OCM) has recently demonstrated its potential for accurate diagnosis of human cervical diseases. One major challenge for clinical adoption, however, is the steep learning curve clinicians need to overcome to interpret OCM images. Developing an intelligent technique for computer-aided diagnosis (CADx) to accurately interpret OCM images will facilitate clinical adoption of the technology and improve patient care.Methods:497 high-resolution 3-D OCM volumes (600 cross-sectional images each) were collected from 159 ex vivo specimens of 92 female patients. OCM image features were extracted using a convolutional neural network (CNN) model, concatenated with patient information (e.g., age and HPV results), and classified using a support vector machine classifier. Ten-fold cross-validations were utilized to test the performance of the CADx method in a five-class classification task and a binary classification task.Results:An 88.3±4.9% classification accuracy was achieved for five fine-grained classes of cervical tissue, namely normal, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL), and cancer. In the binary classification task (low-risk [normal, ectropion and LSIL] vs. high-risk [HSIL and cancer]), the CADx method achieved an area-under-the-curve (AUC) value of 0.959 with an 86.7±11.4% sensitivity and 93.5±3.8% specificity.Conclusion:The proposed deep-learning based CADx method outperformed four human experts. It was also able to identify morphological characteristics in OCM images that were consistent with histopathological interpretations.Significance:Label-free OCM imaging, combined with deep-learning based CADx methods, hold a great promise to be used in clinical settings for the effective screening and diagnosis of cervical diseases.

Segmentation of Drosophila heart in optical coherence microscopy images using convolutional neural networks.

Convolutional neural networks (CNNs) are powerful tools for image segmentation and classification. Here, we use this method to identify and mark the heart region of Drosophila at different developmental stages in the cross-sectional images acquired by a custom optical coherence microscopy (OCM) system. With our well-trained CNN model, the heart regions through multiple heartbeat cycles can be marked with an intersection over union of ~86%. Various morphological and dynamical cardiac parameters can be quantified accurately with automatically segmented heart regions. This study demonstrates an efficient heart segmentation method to analyze OCM images of the beating heart in Drosophila.

Ultrahigh-resolution optical coherence microscopy accurately classifies precancerous and cancerous human cervix free of labeling.

Cervical cancer remains the fourth most common cause of cancer worldwide and the third leading cause of cancer deaths for women in developing countries. Traditional screening tools, such as human papillomavirus and Pap tests, cannot provide results in real-time and cannot localize suspicious regions. Colposcopy-directed biopsies are invasive in nature and only a few sites of the cervix may be chosen for investigation. A non-invasive, label-free and real-time imaging method with a resolution approaching that of histopathology is desirable for early detection of the disease.Methods: Ultrahigh-resolution optical coherence microscopy (OCM) is an emerging imaging technique used to obtain 3-dimensional (3-D) “optical biopsies” of biological samples with cellular resolution. In this study, 497 3-D OCM datasets from 159 specimens were collected from 92 patients.Results: Distinctive patterns for normal cervix, squamocolumnar junction, ectropion, low-grade and high-grade squamous intraepithelial lesions (LSIL and HSIL) and invasive cervical lesions were clearly observed from OCM images, which matched well with corresponding histological slides. OCM images demonstrated a sensitivity of 80% (95% confidence interval, CI, 72%-86%) and a specificity of 89% (95% CI, 84%-93%) for detecting high-risk lesions (HSIL and invasive lesions) when blindly tested by three investigators. A substantial inter-observer agreement was observed (κ=0.627), which showed high diagnostic consistency among three investigators.Conclusion: These results laid the foundation for future non-invasive optical evaluation of cervical tissue in vivo, which could lead to a less invasive and more effective screening and “see-and-treat” strategy for the management of cervical cancer.

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

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.

Drosophila Preparation and Longitudinal Imaging of Heart Function In Vivo Using Optical Coherence Microscopy (OCM).

Longitudinal study of the heartbeat in small animals contributes to understanding structural and functional changes during heart development. Optical coherence microscopy (OCM) has been demonstrated to be capable of imaging small animal hearts with high spatial resolution and ultrahigh imaging speed. The high image contrast and noninvasive properties make OCM ideal for performing longitudinal studies without requiring tissue dissections or staining. Drosophila has been widely used as a model organism in cardiac developmental studies due to its high number of orthologous human disease genes, its similarity of molecular mechanisms and genetic pathways with vertebrates, its short life cycle, and its low culture cost. Here, the experimental protocols are described for the preparation of Drosophila and optical imaging of the heartbeat with a custom OCM system throughout the life cycle of the specimen. By following the steps provided in this report, transverse M-mode and 3D OCM images can be acquired to conduct longitudinal studies of the Drosophila cardiac morphology and function. The en face and axial sectional OCM images and the heart rate (HR) and cardiac activity period (CAP) histograms, were also shown to analyze the heart structural changes and to quantify the heart dynamics during Drosophila metamorphosis, combined with the videos constructed with M-mode images to trace cardiac activity intuitively. Due to the genetic similarity between Drosophila and vertebrates, longitudinal study of heart morphology and dynamics in fruit flies could help reveal the origins of human heart diseases. The protocol here would provide an effective method to perform a wide range of studies to understand the mechanisms of cardiac diseases in humans.

Ultrathin forward-imaging short multimode fiber probe for full-field optical coherence microscopy

To extend the applications of optical coherence tomography (OCT) to the fields of physiology and clinical medicine, less invasive, robust, and reliable optical probes are required. Thus, we demonstrate an ultrathin forward-imaging short multimode fiber (SMMF) optical coherence microscopy (OCM) probe with a 50 μm core diameter, 125μm total diameter, and 5.12mm length. Imaging conditions and magnification were analyzed, and they correspond closely to the measured results. The dispersion of the SMMF was investigated, and the modal dispersion coefficient was found to be 2.3% of the material dispersion coefficient. The axial resolution was minimized at 2.15μm using a 0.885-mm-thick dispersion compensator. The lateral resolution was evaluated to be 4.38μm using a test pattern. The contrast of the OCM images was 5.7 times higher than that of the signal images owing to the coherence gate. The depth of focus and diameter of the field of view were measured to be 60μm and 40–50 μm, respectively. OCM images of the dried fins of small fish (Medaka) were measured and internal structures could be recognized.

Fast subcellular optical coherence photoacoustic microscopy for pigment cell imaging.

We developed a fast ultrahigh resolution optical coherence photoacoustic microscopy (FU-OCPAM) system by combining two complementary imaging modes of optical coherence microscopy (OCM) and photoacoustic microscopy (PAM) for cellular/subcellular imaging. The system used optical scanning to realize fast imaging speed and provided ultrahigh resolution of 1.24 and 0.59 μm for OCM and PAM, respectively. We imaged the retinal pigment epithelium (RPE) to demonstrate the subcellular imaging capability of the FU-OCPAM system. The OCM and PAM images clearly showed the RPE cell morphology and reflected the complementary optical properties of scattering and absorption. A quantitative analysis of the RPE cells was made based on photoacoustic (PA) signals. The cell area mainly ranged from 80 to 300 μm2, and had a linear relationship with the sum intensity of PA signals which mainly reflected the melanin content of the cells. The morphology and the PA signal could be used to identify qualitatively and quantitatively the aging and healthy states of the RPE cells. The results show the potential applications in studying the real-time cellular response to external stimulations and the progress of aging and diseases at the cellular level with FU-OCPAM.

Wide-field optical coherence microscopy of the mouse brain slice.

The imaging capability of optical coherence microscopy (OCM) has great potential to be used in neuroscience research because it is able to visualize anatomic features of brain tissue without labeling or external contrast agents. However, the field of view of OCM is still narrow, which dilutes the strength of OCM and limits its application. In this study, we present fully automated wide-field OCM for mosaic imaging of sliced mouse brains. A total of 308 segmented OCM images were acquired, stitched, and reconstructed as an en-face brain image after intensive imaging processing. The overall imaging area was 11.2×7.0 mm (horizontal×vertical), and the corresponding pixel resolution was 1.2×1.2 μm. OCM images were compared to traditional histology stained with Nissl and Luxol fast blue (LFB). In particular, the orientation of the fibers was analyzed and quantified in wide-field OCM.

A Circadian Clock Gene, Cry, Affects Heart Morphogenesis and Function in Drosophila as Revealed by Optical Coherence Microscopy

Circadian rhythms are endogenous, entrainable oscillations of physical, mental and behavioural processes in response to local environmental cues such as daylight, which are present in the living beings, including humans. Circadian rhythms have been related to cardiovascular function and pathology. However, the role that circadian clock genes play in heart development and function in a whole animal in vivo are poorly understood. The Drosophila cryptochrome (dCry) is a circadian clock gene that encodes a major component of the circadian clock negative feedback loop. Compared to the embryonic stage, the relative expression levels of dCry showed a significant increase (>100-fold) in Drosophila during the pupa and adult stages. In this study, we utilized an ultrahigh resolution optical coherence microscopy (OCM) system to perform non-invasive and longitudinal analysis of functional and morphological changes in the Drosophila heart throughout its post-embryonic lifecycle for the first time. The Drosophila heart exhibited major morphological and functional alterations during its development. Notably, heart rate (HR) and cardiac activity period (CAP) of Drosophila showed significant variations during the pupa stage, when heart remodeling took place. From the M-mode (2D + time) OCM images, cardiac structural and functional parameters of Drosophila at different developmental stages were quantitatively determined. In order to study the functional role of dCry on Drosophila heart development, we silenced dCry by RNAi in the Drosophila heart and mesoderm, and quantitatively measured heart morphology and function in those flies throughout its development. Silencing of dCry resulted in slower HR, reduced CAP, smaller heart chamber size, pupal lethality and disrupted posterior segmentation that was related to increased expression of a posterior compartment protein, wingless. Collectively, our studies provided novel evidence that the circadian clock gene, dCry, plays an essential role in heart morphogenesis and function.

Swept source optical coherence microscopy using a 1310 nm VCSEL light source

We demonstrate high speed, swept source optical coherence microscopy (OCM) using a MEMS tunable vertical cavity surface-emitting laser (VCSEL) light source. The light source had a sweep rate of 280 kHz, providing a bidirectional axial scan rate of 560 kHz. The sweep bandwidth was 117 nm centered at 1310 nm, corresponding to an axial resolution of 13.1 µm in air, corresponding to 8.1 µm (9.6 µm spectrally shaped) in tissue. Dispersion mismatch from different objectives was compensated numerically, enabling magnification and field of view to be easily changed. OCM images were acquired with transverse resolutions between 0.86 µm - 3.42 µm using interchangeable 40X, 20X and 10X objectives with ~600 µm x 600 µm, ~1 mm x 1 mm and ~2 mm x 2 mm field-of-view (FOV), respectively. Parasitic variations in path length with beam scanning were corrected numerically. These features enable swept source OCM to be integrated with a wide range of existing scanning microscopes. Large FOV mosaics were generated by serially acquiring adjacent overlapping microscopic fields and combining them in post-processing. Fresh human colon, thyroid and kidney specimens were imaged ex vivo and compared to matching histology sections, demonstrating the ability of OCM to image tissue specimens.