Optical coherence micro-elastography: mechanical-contrast imaging of tissue microstructure

We present optical coherence micro-elastography, an improved form of compression optical coherence elastography. We demonstrate the capacity of this technique to produce en face images, closely corresponding with histology, that reveal micro-scale mechanical contrast in human breast and lymph node tissues. We use phase-sensitive, three-dimensional optical coherence tomography (OCT) to probe the nanometer-to-micrometer-scale axial displacements in tissues induced by compressive loading. Optical coherence micro-elastography incorporates common-path interferometry, weighted averaging of the complex OCT signal and weighted least-squares regression. Using three-dimensional phase unwrapping, we have increased the maximum detectable strain eleven-fold over no unwrapping and the minimum detectable strain is 2.6 με. We demonstrate the potential of mechanical over optical contrast for visualizing micro-scale tissue structures in human breast cancer pathology and lymph node morphology.OCIS codes: (110.4500) Optical coherence tomography, (110.1650) Coherence imaging, (100.5088) Phase unwrapping     

Pulsatile tissue deformation dynamics of the murine retina and choroid mapped by 4D optical coherence tomography

Irregular ocular pulsatility and altered mechanical tissue properties are associated with some of the most sight-threatening eye diseases. Here we present 4D optical coherence tomography (OCT) for the quantitative assessment and depth-resolved mapping of pulsatile dynamics in the murine retina and choroid. Through a pixel-wise analysis of phase changes of the complex OCT signal, we reveal spatiotemporal displacement characteristics across repeated frame acquisitions. We demonstrate in vivo fundus elastography (FUEL) imaging in wildtype mouse retinas and in a mouse model of retinal neovascularization and uncover subtle structural deformations related to ocular pulsation. Our data in mouse eyes hold promise for a powerful retinal elastography technique that may enable a new paradigm of OCT-based measurements and image contrast.     

Static compression optical coherence elastography to measure the mechanical properties of soft contact lenses

In this study, a novel method was developed for estimating the elastic modulus (Young’s modulus) of soft contact lens materials using static compression optical coherence elastography. Using a commercially available spectral domain optical coherence tomography instrument, an experimental setup was developed to image a soft contact lens sample before and during compression with a known applied force, from which the lens material’s mechanical properties can be derived. A semi-automatic segmentation method using graph-search theory and dynamic processing was used to trace the lens boundaries and to determine key structural changes within the images. To validate the method, five soft contact lens materials with a range of known elastic moduli and water contents were tested. The proposed method was successful in estimating the Young’s modulus in the five different soft contact lens materials. It was demonstrated that the method provides highly repeatable measurements, with an intraclass correlation coefficient of >95%. The Young’s modulus results were compared to published manufacturer data with no significant difference for four out of the five materials (p > 0.05). These results demonstrate that a static compression optical coherence tomography method can reliably measure the elastic modulus of soft contact lenses. This provides a methodology that can be used to explore in vitro contact lens mechanical properties, but more importantly, may also be extended to study the mechanical characteristics of in vivo or ex vivo tissue, provided that they can be imaged using OCT.     

Feasibility of optical coherence elastography measurements of shear wave propagation in homogeneous tissue equivalent phantoms

In this work, we explored the potential of measuring shear wave propagation using optical coherence elastography (OCE) based on a swept-source optical coherence tomography (OCT) system. Shear waves were generated using a 20 MHz piezoelectric transducer (circular element 8.5 mm diameter) transmitting sine-wave bursts of 400 μs, synchronized with the OCT swept source wavelength sweep. The acoustic radiation force (ARF) was applied to two gelatin phantoms (differing in gelatin concentration by weight, 8% vs. 14%). Differential OCT phase maps, measured with and without the ARF, demonstrate microscopic displacement generated by shear wave propagation in these phantoms of different stiffness. We present preliminary results of OCT derived shear wave propagation velocity and modulus, and compare these results to rheometer measurements. The results demonstrate the feasibility of shear wave OCE (SW-OCE) for high-resolution microscopic homogeneous tissue mechanical property characterization.     

Quantitative optical coherence elastography based on fiber-optic probe for in situ measurement of tissue mechanical properties

We developed a miniature quantitative optical coherence elastography (qOCE) instrument with an integrated Fabry-Perot force sensor, for in situ elasticity measurement of biological tissue. The technique has great potential for biomechanics modeling and clinical diagnosis. We designed the fiber-optic qOCE probe that was used to exert a compressive force to deform tissue at the tip of the probe. Using the space-division multiplexed optical coherence tomography (OCT) signal detected by a spectral domain OCT engine, we were able to quantify the probe deformation that was proportional to the force applied, and to quantify the tissue deformation corresponding to the external stimulus. Simultaneous measurement of force and displacement allowed us to extract Young’s modulus of biological tissue. We experimentally calibrated our qOCE instrument, and validated its effectiveness on tissue mimicking phantoms and biological tissues.OCIS codes: (170.4500) Optical coherence tomography, (280.4788) Optical sensing and sensors, (280.3340) Laser Doppler velocimetry     

Quantitative compressive optical coherence elastography using structural OCT imaging and optical palpation to measure soft contact lens mechanical properties

In this study, the principle of ‘optical palpation’ was applied to a compression optical coherence elastography (OCE) method using spectral domain optical coherence tomography (OCT). Optical palpation utilizes a compliant transparent material of known mechanical properties, which acts as a stress sensor, in order to derive the mechanical properties of a sample material under examination. This technique was applied to determine the mechanical properties of soft contact lenses, with one lens being used as the compliant stress sensor and the other as the sample under investigation to extract the mechanical properties. This compliant stress sensor allowed for the stress of the compression to be measured without the use of a force sensor. The strain of the materials was measured through an automatic boundary segmentation that tracks the material thickness (of the sensor and the sample) during compression through sequential structural OCT images. A total of five contact lens combinations were tested, using three separate commercially available contact lenses with unique mechanical properties. Various combinations of contact lens materials were used to further validate the technique. The Young’s modulus derived from this method was compared to nominal manufacturer’s values. Both accuracy and repeatability were assessed, with highly accurate measurements obtained, with a percentage difference between the nominal and experimentally derived Young’s modulus being less than 6% for all the tested combinations as well as providing a Young’s modulus that was not statistically significant different (p > 0.01) to the nominal value. The results demonstrate the potential of optical palpation in OCE to accurately measure the mechanical properties of a material without the use of sophisticated electronics to capture the stress of the sample. These findings have potential to be translated into a method for tissue mechanical testing with ex vivo and in vivo clinical applications.     

Multi-task learning approach for volumetric segmentation and reconstruction in 3D OCT images

The choroid is the vascular layer of the eye that supplies photoreceptors with oxygen. Changes in the choroid are associated with many pathologies including myopia where the choroid progressively thins due to axial elongation. To quantize these changes, there is a need to automatically and accurately segment the choroidal layer from optical coherence tomography (OCT) images. In this paper, we propose a multi-task learning approach to segment the choroid from three-dimensional OCT images. Our proposed architecture aggregates the spatial context from adjacent cross-sectional slices to reconstruct the central slice. Spatial context learned by this reconstruction mechanism is then fused with a U-Net based architecture for segmentation. The proposed approach was evaluated on volumetric OCT scans of 166 myopic eyes acquired with a commercial OCT system, and achieved a cross-validation Intersection over Union (IoU) score of 94.69% which significantly outperformed (p<0.001) the other state-of-the-art methods on the same data set. Choroidal thickness maps generated by our approach also achieved a better structural similarity index (SSIM) of 72.11% with respect to the groundtruth. In particular, our approach performs well for highly challenging eyes with thinner choroids. Compared to other methods, our proposed approach also requires lesser processing time and has lower computational requirements. The results suggest that our proposed approach could potentially be used as a fast and reliable method for automated choroidal segmentation.     

Optical coherence tomography detection of shear wave propagation in inhomogeneous tissue equivalent phantoms and ex-vivo carotid artery samples

In this work, we explored the potential of measuring shear wave propagation using optical coherence elastography (OCE) in an inhomogeneous phantom and carotid artery samples based on a swept-source optical coherence tomography (OCT) system. Shear waves were generated using a piezoelectric transducer transmitting sine-wave bursts of 400 μs duration, applying acoustic radiation force (ARF) to inhomogeneous phantoms and carotid artery samples, synchronized with a swept-source OCT (SS-OCT) imaging system. The phantoms were composed of gelatin and titanium dioxide whereas the carotid artery samples were embedded in gel. Differential OCT phase maps, measured with and without the ARF, detected the microscopic displacement generated by shear wave propagation in these phantoms and samples of different stiffness. We present the technique for calculating tissue mechanical properties by propagating shear waves in inhomogeneous tissue equivalent phantoms and carotid artery samples using the ARF of an ultrasound transducer, and measuring the shear wave speed and its associated properties in the different layers with OCT phase maps. This method lays the foundation for future in-vitro and in-vivo studies of mechanical property measurements of biological tissues such as vascular tissues, where normal and pathological structures may exhibit significant contrast in the shear modulus.OCIS codes: (170.4500) Optical coherence tomography, (170.6935) Tissue characterization     

Review of tissue simulating phantoms with controllable optical, mechanical and structural properties for use in optical coherence tomography

We review the development of phantoms for optical coherence tomography (OCT) designed to replicate the optical, mechanical and structural properties of a range of tissues. Such phantoms are a key requirement for the continued development of OCT techniques and applications. We focus on phantoms based on silicone, fibrin and poly(vinyl alcohol) cryogels (PVA-C), as we believe these materials hold the most promise for durable and accurate replication of tissue properties.     

Invasive diagnostic imaging of coronary atherosclerosis

Invasive diagnostic imaging technique of coronary atherosclerosis has rapidly developed. For example, intravascular ultrasound(IVUS) is recognized as an essential device for percutaneous coronary intervention to evaluate the vessel wall, vascular lumen and coronary plaque morphologies because of its accuracy for quantitative analysis capability. Recently new imaging modalities such as radio-frequency signal analysis, elastography and contrast harmonic echography have been developed for the evaluation of histological characteristics. Also, optical coherence tomography(OCT), which provides approximately ten-times higher-resolutional cross-section images of the coronary arterial wall in comparison with IVUS, became available in clinical setting. In this article, we review the latest progress of the invasive diagnostic imaging of coronary atherosclerosis.     

Compositional-prior-guided image reconstruction algorithm for multi-modality imaging

The development of effective multi-modality imaging methods typically requires an efficient information fusion model, particularly when combining structural images with a complementary imaging modality that provides functional information. We propose a composition-based image segmentation method for X-ray digital breast tomosynthesis (DBT) and a structural-prior-guided image reconstruction for a combined DBT and diffuse optical tomography (DOT) breast imaging system. Using the 3D DBT images from 31 clinically measured healthy breasts, we create an empirical relationship between the X-ray intensities for adipose and fibroglandular tissue. We use this relationship to then segment another 58 healthy breast DBT images from 29 subjects into compositional maps of different tissue types. For each breast, we build a weighted-graph in the compositional space and construct a regularization matrix to incorporate the structural priors into a finite-element-based DOT image reconstruction. Use of the compositional priors enables us to fuse tissue anatomy into optical images with less restriction than when using a binary segmentation. This allows us to recover the image contrast captured by DOT but not by DBT. We show that it is possible to fine-tune the strength of the structural priors by changing a single regularization parameter. By estimating the optical properties for adipose and fibroglandular tissue using the proposed algorithm, we found the results are comparable or superior to those estimated with expert-segmentations, but does not involve the time-consuming manual selection of regions-of-interest.     

Improved measurement of vibration amplitude in dynamic optical coherence elastography

Abstract: Optical coherence elastography employs optical coherence tomography (OCT) to measure the displacement of tissues under load and, thus, maps the resulting strain into an image, known as an elastogram. We present a new improved method to measure vibration amplitude in dynamic optical coherence elastography. The tissue vibration amplitude caused by sinusoidal loading is measured from the spread of the Doppler spectrum, which is extracted using joint spectral and time domain signal processing. At low OCT signal-to-noise ratio (SNR), the method provides more accurate vibration amplitude measurements than the currently used phase-sensitive method. For measurements performed on a mirror at OCT SNR = 5 dB, our method introduces <3% error, compared to >20% using the phase-sensitive method. We present elastograms of a tissue-mimicking phantom and excised porcine tissue that demonstrate improvements, including a 50% increase in the depth range of reliable vibration amplitude measurement.OCIS codes: (110.4500) Optical coherence tomography, (290.5820) Scattering measurements, (170.6935) Tissue characterization     

Convolutional dictionary learning for blind deconvolution of optical coherence tomography images

In this study, we demonstrate a sparsity-regularized, complex, blind deconvolution method for removing sidelobe artefacts and stochastic noise from optical coherence tomography (OCT) images. Our method estimates the complex scattering amplitude of tissue on a line-by-line basis by estimating and deconvolving the complex, one-dimensional axial point spread function (PSF) from measured OCT A-line data. We also present a strategy for employing a sparsity weighting mask to mitigate the loss of speckle brightness within tissue-containing regions caused by the sparse deconvolution. Qualitative and quantitative analyses show that this approach suppresses sidelobe artefacts and background noise better than traditional spectral reshaping techniques, with negligible loss of tissue structure. The technique is particularly useful for emerging OCT applications where OCT images contain strong specular reflections at air-tissue boundaries that create large sidelobe artefacts.     

Multi-class classification of breast tissue using optical coherence tomography and attenuation imaging combined via deep learning

We demonstrate a convolutional neural network (CNN) for multi-class breast tissue classification as adipose tissue, benign dense tissue, or malignant tissue, using multi-channel optical coherence tomography (OCT) and attenuation images, and a novel Matthews correlation coefficient (MCC)-based loss function that correlates more strongly with performance metrics than the commonly used cross-entropy loss. We hypothesized that using multi-channel images would increase tumor detection performance compared to using OCT alone. 5,804 images from 29 patients were used to fine-tune a pre-trained ResNet-18 network. Adding attenuation images to OCT images yields statistically significant improvements in several performance metrics, including benign dense tissue sensitivity (68.0% versus 59.6%), malignant tissue positive predictive value (PPV) (79.4% versus 75.5%), and total accuracy (85.4% versus 83.3%), indicating that the additional contrast from attenuation imaging is most beneficial for distinguishing between benign dense tissue and malignant tissue.     

Analysis of image formation in optical coherence elastography using a multiphysics approach

Image formation in optical coherence elastography (OCE) results from a combination of two processes: the mechanical deformation imparted to the sample and the detection of the resulting displacement using optical coherence tomography (OCT). We present a multiphysics model of these processes, validated by simulating strain elastograms acquired using phase-sensitive compression OCE, and demonstrating close correspondence with experimental results. Using the model, we present evidence that the approximation commonly used to infer sample displacement in phase-sensitive OCE is invalidated for smaller deformations than has been previously considered, significantly affecting the measurement precision, as quantified by the displacement sensitivity and the elastogram signal-to-noise ratio. We show how the precision of OCE is affected not only by OCT shot-noise, as is usually considered, but additionally by phase decorrelation due to the sample deformation. This multiphysics model provides a general framework that could be used to compare and contrast different OCE techniques.OCIS codes: (000.3860) Mathematical methods in physics, (000.4430) Numerical approximation and analysis, (030.6140) Speckle, (110.2990) Image formation theory, (110.4500) Optical coherence tomography     

Noncontact depth-resolved micro-scale optical coherence elastography of the cornea

High-resolution elastographic assessment of the cornea can greatly assist clinical diagnosis and treatment of various ocular diseases. Here, we report on the first noncontact depth-resolved micro-scale optical coherence elastography of the cornea achieved using shear wave imaging optical coherence tomography (SWI-OCT) combined with the spectral analysis of the corneal Lamb wave propagation. This imaging method relies on a focused air-puff device to load the cornea with highly-localized low-pressure short-duration air stream and applies phase-resolved OCT detection to capture the low-amplitude deformation with nano-scale sensitivity. The SWI-OCT system is used here to image the corneal Lamb wave propagation with the frame rate the same as the OCT A-line acquisition speed. Based on the spectral analysis of the corneal temporal deformation profiles, the phase velocity of the Lamb wave is obtained at different depths for the major frequency components, which shows the depthwise distribution of the corneal stiffness related to its structural features. Our pilot experiments on ex vivo rabbit eyes demonstrate the feasibility of this method in depth-resolved micro-scale elastography of the cornea. The assessment of the Lamb wave dispersion is also presented, suggesting the potential for the quantitative measurement of corneal viscoelasticity.OCIS codes: (170.4460) Ophthalmic optics and devices, (170.4500) Optical coherence tomography, (120.5050) Phase measurement     

In-vivo segmentation and quantification of coronary lesions by optical coherence tomography images for a lesion type definition and stenosis grading

Optical coherence tomography (OCT) is a catheter-based medical imaging technique that produces cross-sectional images of blood vessels. This technique is particularly useful for studying coronary atherosclerosis. In this paper, we present a new framework that allows a segmentation and quantification of OCT images of coronary arteries to define the plaque type and stenosis grading. These analyses are usually carried out on-line on the OCT-workstation where measuring is mainly operator-dependent and mouse-based. The aim of this program is to simplify and improve the processing of OCT images for morphometric investigations and to present a fast procedure to obtain 3D geometrical models that can also be used for external purposes such as for finite element simulations. The main phases of our toolbox are the lumen segmentation and the identification of the main tissues in the artery wall. We validated the proposed method with identification and segmentation manually performed by expert OCT readers. The method was evaluated on ten datasets from clinical routine and the validation was performed on 210 images randomly extracted from the pullbacks. Our results show that automated segmentation of the vessel and of the tissue components are possible off-line with a precision that is comparable to manual segmentation for the tissue component and to the proprietary-OCT-console for the lumen segmentation. Several OCT sections have been processed to provide clinical outcome.     

Real-time three-dimensional optical coherence tomography image-guided core-needle biopsy system

Advances in optical imaging modalities, such as optical coherence tomography (OCT), enable us to observe tissue microstructure at high resolution and in real time. Currently, core-needle biopsies are guided by external imaging modalities such as ultrasound imaging and x-ray computed tomography (CT) for breast and lung masses, respectively. These image-guided procedures are frequently limited by spatial resolution when using ultrasound imaging, or by temporal resolution (rapid real-time feedback capabilities) when using x-ray CT. One feasible approach is to perform OCT within small gauge needles to optically image tissue microstructure. However, to date, no system or core-needle device has been developed that incorporates both three-dimensional OCT imaging and tissue biopsy within the same needle for true OCT-guided core-needle biopsy. We have developed and demonstrate an integrated core-needle biopsy system that utilizes catheter-based 3-D OCT for real-time image-guidance for target tissue localization, imaging of tissue immediately prior to physical biopsy, and subsequent OCT imaging of the biopsied specimen for immediate assessment at the point-of-care. OCT images of biopsied ex vivo tumor specimens acquired during core-needle placement are correlated with corresponding histology, and computational visualization of arbitrary planes within the 3-D OCT volumes enables feedback on specimen tissue type and biopsy quality. These results demonstrate the potential for using real-time 3-D OCT for needle biopsy guidance by imaging within the needle and tissue during biopsy procedures.     

Multi-scale GCN-assisted two-stage network for joint segmentation of retinal layers and discs in peripapillary OCT images

An accurate and automated tissue segmentation algorithm for retinal optical coherence tomography (OCT) images is crucial for the diagnosis of glaucoma. However, due to the presence of the optic disc, the anatomical structure of the peripapillary region of the retina is complicated and is challenging for segmentation. To address this issue, we develop a novel graph convolutional network (GCN)-assisted two-stage framework to simultaneously label the nine retinal layers and the optic disc. Specifically, a multi-scale global reasoning module is inserted between the encoder and decoder of a U-shape neural network to exploit anatomical prior knowledge and perform spatial reasoning. We conduct experiments on human peripapillary retinal OCT images. We also provide public access to the collected dataset, which might contribute to the research in the field of biomedical image processing. The Dice score of the proposed segmentation network is 0.820 ± 0.001 and the pixel accuracy is 0.830 ± 0.002, both of which outperform those from other state-of-the-art techniques.     

Tissue self-attention network for the segmentation of optical coherence tomography images on the esophagus

Automatic segmentation of layered tissue is the key to esophageal optical coherence tomography (OCT) image processing. With the advent of deep learning techniques, frameworks based on a fully convolutional network are proved to be effective in classifying pixels on images. However, due to speckle noise and unfavorable imaging conditions, the esophageal tissue relevant to the diagnosis is not always easy to identify. An effective approach to address this problem is extracting more powerful feature maps, which have similar expressions for pixels in the same tissue and show discriminability from those from different tissues. In this study, we proposed a novel framework, called the tissue self-attention network (TSA-Net), which introduces the self-attention mechanism for esophageal OCT image segmentation. The self-attention module in the network is able to capture long-range context dependencies from the image and analyzes the input image in a global view, which helps to cluster pixels in the same tissue and reveal differences of different layers, thus achieving more powerful feature maps for segmentation. Experiments have visually illustrated the effectiveness of the self-attention map, and its advantages over other deep networks were also discussed.