Optical Coherence Elastography (OCE) is a non-invasive elastography technique, which can deduce the elastic properties of tissue by measuring the displacement or deformation in tissue caused by internal or external excitation. In recent years, it has been well accepted that the biomechanical properties of cornea are associated with various ophthalmic diseases. OCE has exhibited a good potential in measuring corneal elasticity, and however in vivo quantitative measurement remains challenging. Thus, in this paper, we designed an OCE system equipped with a load-measuring stress sensor to measure the deformation and geometric parameters of cornea during compression. Furthermore, a compressive OCE corneal elasticity measurement model was developed based on shell theory, which is dedicated to translating the measured value into Young's modulus. Finally, the method was evaluated on both artificial eye model and porcine cornea ex vivo. The results indicate that the OCE measurement combined with shell model can potentially aid in clinical measurement on cornea elasticity in vivo.
Optical coherence tomography (OCT) has been widely used in ophthalmology with its micron-resolution, depth-resolving capability in imaging bio-tissues in vivo. Recently, deep learning methods are emerging to achieve axial super-resolution (SR) in OCT, aimed to reduce the cost of broad-band light source. However, all of those deep learning methods were developed based on real-valued networks, ignoring the phase information of complex-valued OCT image which contains structural information. In this study, we proposed a complex-valued enhanced deep super-resolution network (Cv-EDSR) to obtain OCT axial super-resolution. We validated the superior performance of Cv-EDSR over the traditional EDSR on two datasets (swine esophagus and human retina), and demonstrated three benefits of Cv-EDSR: a) Cv-EDSR generated more realistic SR images, b) Cv-EDSR achieved an improved quality of SR images, c) Cv-EDSR possessed a better generalization performance.
PurposeOptical coherence tomography (OCT) is a noninvasive, high-resolution imaging modality capable of providing both cross-sectional and three-dimensional images of tissue microstructures. Owing to its low-coherence interferometry nature, however, OCT inevitably suffers from speckles, which diminish image quality and mitigate the precise disease diagnoses, and therefore, despeckling mechanisms are highly desired to alleviate the influences of speckles on OCT images.ApproachWe propose a multiscale denoising generative adversarial network (MDGAN) for speckle reductions in OCT images. A cascade multiscale module is adopted as MDGAN basic block first to raise the network learning capability and take advantage of the multiscale context, and then a spatial attention mechanism is proposed to refine the denoised images. For enormous feature learning in OCT images, a deep back-projection layer is finally introduced to alternatively upscale and downscale the features map of MDGAN.ResultsExperiments with two different OCT image datasets are conducted to verify the effectiveness of the proposed MDGAN scheme. Results compared those of the state-of-the-art existing methods show that MDGAN is able to improve both peak-single-to-noise ratio and signal-to-noise ratio by 3 dB at most, with its structural similarity index measurement and contrast-to-noise ratio being 1.4% and 1.3% lower than those of the best existing methods.ConclusionsResults demonstrate that MDGAN is effective and robust for OCT image speckle reductions and outperforms the best state-of-the-art denoising methods in different cases. It could help alleviate the influence of speckles in OCT images and improve OCT imaging-based diagnosis.
Corneal nerve fibers (CNF) usually exhibit changes associated with ocular and systemic diseases. Thus, in clinic, CNF is imaged by corneal confocal microscopy (CCM) for ocular disease diagnosis. To obtain an objective and accurate diagnosis, CNF needs to be segmented from CCM image and further its centerline is extracted to pave the wave for producing quantitative diagnostic markers. It is reasonable to state that the performance of CNF segmentation and centerline extraction places a big impact on the disease diagnosis. Therefore, in this study, we aim to improve the CNF segmentation and centerline extraction. CNF segmentation algorithm is developed based on UNet++, which is modified by adding skip connection to be more applicable to multi-scale information. Following CNF segmentation, a new CNF centerline extraction algorithm is developed based on neighborhood statistics. Compared with the traditional thinning algorithm and the skeleton extraction algorithm, our method yields a more consistent and smoother central line extraction by reducing the effect of imperfect segmentation and noise which is hard to avoid in deep learning segmentation. The proposed segmentation method is evaluated on an open dataset by comparing with the conventional UNet, UNet++ and UNet3+. The proposed centerline extraction method is evaluated on the same image dataset by comparing with the traditional thinning algorithm and the skeleton extraction algorithm. The results show that our method can outperform the conventional UNet++ in terms of Acc (accuracy), TPR (True Positive Rate), TNR (True Negative Rate), Dice and FDR (False Discovery Rate). The centerline extraction method can extract the centerline with less errors.
Optical coherence elastography (OCE) is a new biomedical optical elastic imaging technology. It inherits the advantage of optical coherence tomography (OCT) with high resolution, and it has sub-nanometer displacement measurement sensitivity. OCE uses OCT to detect the deformation of biological tissue along the depth direction under loading, so as to obtain the elastic information of tissue. Among the OCE forms with various loading strategies, compression OCE has attracted great interests for its ease of implementation. However, the quantitative measurement of the loading remains a challenge. Therefore, in this study, we developed a handheld OCE system based on compression OCE with a specially designed stress sensor for loading measurement. The OCE system is built based on swept-source OCT, and a handheld sampling probe was developed with a specially-designed pressure sensor for load measurement. The OCE probe with the stress sensor is evaluated on both artificial phantom and human skin. The results show that the OCE system has a good potential for elasticity measurement on biological tissues in vivo.
In this study, we developed a novel dual-side view OCT (DSV-OCT) system for thickness measurement on opaque materials. The dual-side view was achieved on conventional swept source OCT platform by creating two symmetrical sampling arms. This allows to image both sides of the material simultaneously and produce the surface contours of the two sides in a single C-scan. Finally, the thickness of the opaque material can be calculated from the two surface contours above. We evaluated the performance of our DSV-OCT using a microscope slide as sample. The results demonstrated that our DSV-OCT has a good capability for thickness measurement on opaque materials with an accuracy of about 3 μm.
Skin cancer is one of the most common cancers. Most skin cancers are not life threatening, but malignant melanoma is fatal. Currently, it still remains a challenge to discriminate malignant melanoma from benign melanoma by using conventional diagnostic techniques, such as ultrasonography, computed tomography, magnetic resonance imaging and positron emission tomography. As a new type of bio-optical imaging technology, hyperspectral imaging (HSI) has become the focus of research. It can provide information about hemoglobin and melanin content for the differentiation of various skin diseases. In this study, we propose a hyperspectral imaging system based on push-broom imaging spectrometer to image skin-pigmented nevus, and then segment the nevus out from surrounding normal skin through pixel-wised spectrum classification with deep learning techniques. The HIS system can produce hyperspectral image over the spectral range of 465-630nm and with a spectral resolution of 2.1 nm. Meanwhile, we evaluated the performance of K-means, Gaussian Mixture Model (GMM) and Hierarchical Clustering (HAC) in detecting the nevus with manual segmentation as the gold standard. The results show that these three techniques all have a good accuracy in differentiating the nevus from normal skin, which proves that the hyperspectral system combined with classification techniques has a good potential to detect the pigmented nevus on the skin.
Conformal coating is a protective coating widely used in printed circuit boards (PCBs), protecting PCBs from harsh environmental conditions. Its curing extent and thickness are the key factors, determining its protection performance. At present, the traditional method to evaluate the curing extent is metallographic section, which cuts PCB and images its cross-section under a microscope. However, it is destructive. In this study, we proposed to use optical coherence tomography (OCT) to evaluate the coating curing extent. Note that Brownian motion inside the conformal coating gets slower during curing process, leading to a smaller OCT intensity variation over time. Therefore, speckle variance (SV) of OCT imaging, which actually measures the OCT intensity variation, is expected to become smaller during the curing process and can be used to evaluate the curing extent over the whole imaging depth. To demonstrate the capability of SVOCT in detecting the curing extent of conformal coating, multiple OCT images were acquired at each curing status for SV calculation. The results show that the speckle variance of OCT image will gradually decrease during the curing process of conformal coating and eventually stabilize after the coating cures completely. This can be utilized to assess the curing extent of the conformal coating.
Optical coherence tomography (OCT) is a non-destructive and non-contact sensing tool for imaging optical scattering media with microscopic spatial resolution. According to its imaging mechanism, this technology is very suitable for imaging and thickness measurement of multilayer structures. Thus, OCT has been widely used for medical diagnostics and non-destructive inspection in industries. However, due to the limited imaging depth, OCT can only be used for non-opaque materials. In this study, we developed a novel technique based on OCT imaging for thickness measurement of opaque materials. To demonstrate the ability of the technique, we obtained a double side view by establishing two symmetrical sample beams based on a home-built 1060nm swept source OCT system. Using the OCT system we developed, we can collect two surface contour information for non-transparent materials, and eventually calculate the thickness of the nontransparent material. The results show that the developed system keeps the imaging capability of OCT and further extend for opaque material thickness measurement.
Conformal coating is a thin film used for protecting printed circuit boards (PCBs) from harsh environmental conditions, which reduces the failure rate of PCBs. The thickness of conformal coating is one of the key factors determining the protection efficacy on PCB. Therefore, the thickness measurement is highly desired to qualify the conformal coating. In this study, we propose to employ high-resolution spectral-domain optical coherence tomography (SD-OCT) for measuring the conformal coating thickness. An SD-OCT with axial resolution of 1.72 μm is developed. The system can provide cross-sectional imaging of the conformal coating layer. Then a boundary detection algorithm is developed to identify the coating layer from the OCT image and eventually calculate the thickness of the coating layer. Our proposed method is evaluated through comparing with metallographic slicing method, which cuts PCB into cross-section and measure conformal coating thickness under a microscope. The results demonstrate that our method produces a very consistent measurement results as compared to metallographic slicing method. In addition to the good accuracy, our algorithm’s computation load is low (about one hundred milliseconds per B-scan), indicating the potential to achieve on-line inspection of coating thickness.
Intraocular pressure (IOP) is the pressure exerted by the eye contents on the eyeball wall and is used to maintain the shape of the eyeball. It may cause glaucoma when the dynamic balance of the generation and excretion of aqueous humor in the eyeball is broken. The Goldmann applanation tonometer (GAT) based on the Imbert-Fick principle is considered to be the reference standard for glaucoma diagnosis in clinics. OCT is widely used for eye screening by imaging structural changes caused by various eye diseases. In this paper, we have developed an OCT-assisted transparent flexible force sensing system (O-FPSS) for IOP measurements. In general, the hybrid O-FPSS consists of a droplet-based flexible transparent force sensor placed over an optical coherence tomography imaging lens, in which the IOP measured once the apex of the cornea is flatted by the sensor. According to the Imbert-Fick law, when cornea is flattened, the pressure applied by the sensor equals to the IOP. Specially, the droplet-based capacitive flexible force sensor is consisted by two flexible conductive membranes, and an ionic is sandwiched in between, in which the force applied on the cornea can be monitored by the output. The sensing membrane deforms uniformly upon contacting the cornea, leading to the expansion of the droplet and an increase of the overall capacitance. On the other hand, to get the flatten area between the sensor and the cornea, a swept-source OCT (SS-OCT) is used to record the interfacial with a resolution of 5μm.
Optical coherence tomography (OCT) is now a popular high resolution optical imaging technology capable of providing three-dimension images of internal microstructures within biological tissues. To date, the most successful application of OCT has been in ophthalmology, where the technology has become an indispensable diagnostic tool. It has proven able to image the structural changes due to various eye diseases. Besides, those structural changes may also be associated with certain physiological conditions, for instance, vessel density changes resulting from intraocular pressure change. Intraocular pressure (IOP) can also serve as an important physiological marker for the diagnosis of ophthalmic diseases. Therefore, in this study, we aim to develop ophthalmic OCT combined with a novel flexible pressure sensor for retina imaging and intraocular pressure measurement. A swept source OCT (SS-OCT) system is designed, and its axial resolution is about 5 μm. The OCT system is specially designed to allow for both anterior and posterior eye segment imaging. The anterior eye segment imaging is dedicated to measure the contact area between the pressure sensor and the cornea, which is needed by the pressure sensor to calculate the intraocular pressure. This system will be a versatile ophthalmic imaging platform: (1) conventional anterior and posterior eye imaging; (2) intraocular pressure measurement. Further, it will serve as a useful tool aiding in eye disease diagnostics in clinics.
Phase-resolved Doppler optical coherence tomography (PR-D-OCT) is a functional OCT imaging technique that can
provide high-speed and high-resolution depth-resolved measurement on flow in biological materials. However, a
common problem with conventional PR-D-OCT is that this technique often measures the flow motion projected onto the
OCT beam path. In other words, it needs the projection angle to extract the absolute velocity from PR-D-OCT
measurement. In this paper, we proposed a novel dual-beam PR-D-OCT method to measure absolute flow velocity
without separate measurement on the projection angle. Two parallel light beams are created in sample arm and focused
into the sample at two different incident angles. The images produced by these two beams are encoded to different depths
in single B-scan. Then the Doppler signals picked up by the two beams together with the incident angle difference can be
used to calculate the absolute velocity. We validated our approach in vitro on an artificial flow phantom with our
home-built 1060 nm swept source OCT. Experimental results demonstrated that our method can provide an accurate
measurement of absolute flow velocity with independency on the projection angle.
Optical coherence tomography (OCT), as a low-coherence interferometric imaging technique, inevitably suffers from
speckle noise, which can reduce image quality and signal-to-noise (SNR). In this paper, we present a dual-beam angular
compounding method to reduce speckle noise and improve SNR of OCT image. Two separated parallel light beams are
created on the sample arm using a 1x2 optical fiber coupler and are focused into samples at different angles. The
epi-detection scheme creates three different light path combinations of these two light beams above. The three
combinations produce three images in single B-scan, which are completely separated in depth. The three images show
uncorrelated speckle patterns and therefore can be averaged to create a new image with reduced speckle noise. Compared
to those reported angular and spatial compounding methods, our method retains their advantages, and moreover has a
faster imaging speed and keep the transverse resolution. This method was evaluated on human fingertips in vivo. The
results demonstrated a good improvement in speckle contrast.
Optical coherence tomography (OCT) is one of the successful inventions in medical imaging as a clinic routine in the past decades. This imaging technique is based on low coherence interferometer and consequently suffers from speckle noise inherently, which can degrade image quality and obscure micro-structures. Therefore, effective speckle reduction techniques have been always desired and researched since optical coherence tomography was invented. In this study, we proposed an angular compounding method to reduce speckle noise of OCT image. Two different angular light paths are created on the sample arm using two beam splitters. The epi-detection scheme creates three different combinations of the two angular light paths above, which produce three images in single B-scan. To compound these three images, these three images are separated in depth by delaying one light path relative to the other. Compared to those reported angular compounding methods, our method showed an advantage of faster imaging speed. This method was evaluated on an artificial eye model. The results demonstrated a 1.46-fold improvement in speckle contrast.
We present a miniature motorized endoscopic probe for Optical Frequency Domain Imaging with an outer diameter of
1.65 mm and a rotation speed of 3,000 – 12,500 rpm. The probe has a motorized distal end which provides a significant
advantage over proximally driven probes since it does not require a drive shaft to transfer the rotational torque to the
distal end of the probe and functions without a fiber rotary junction. The probe has a focal Full Width at Half Maximum
of 9.6 μm and a working distance of 0.47 mm. We analyzed the non-uniform rotation distortion and found a location
fluctuation of only 1.87° in repeated measurements of the same object. The probe was integrated in a high-speed Optical
Frequency Domain Imaging setup at 1310 nm. We demonstrated its performance with imaging ex vivo pig bronchial and
in vivo goat lung.
We present a miniature motorized endoscopic probe for Optical Frequency Domain Imaging with an outer diameter of 1.65 mm and a rotation speed of 3,000 – 12,500 rpm. This is the smallest motorized high speed OCT probe to our knowledge. The probe has a motorized distal end which provides a significant advantage over proximally driven probes since it does not require a drive shaft to transfer the rotational torque to the distal end of the probe and functions without a fiber rotary junction. The probe has a focal Full Width at Half Maximum of 9.6 μm and a working distance of 0.47 mm. We analyzed the non-uniform rotation distortion and found a location fluctuation of only 1.87° in repeated measurements of the same object. The probe was integrated in a high-speed Optical Frequency Domain Imaging setup at 1310 nm We demonstrated its performance with imaging ex vivo pig bronchial and in vivo goat lung.
Phase-resolved optical frequency domain imaging (OFDI) has emerged as a promising technique for blood flow
measurement in human tissues. Phase stability is essential for this technique to achieve high accuracy in flow velocity
measurement. In OFDI systems that use k-clocking for the data acquisition, phase-error occurs due to jitter in the data
acquisition electronics. We presented a statistical analysis of jitter represented as point shifts of the k-clocked spectrum.
We demonstrated a real-time phase-error correction algorithm for phase-resolved OFDI. A 50 KHz wavelength-swept
laser (Axsun Technologies) based balanced-detection OFDI system was developed centered at 1310 nm. To evaluate the
performance of this algorithm, a stationary gold mirror was employed as sample for phase analysis. Furthermore, we
implemented this algorithm for imaging of human skin. Good-quality skin structure and Doppler image can be observed
in real-time after phase-error correction. The results show that the algorithm can effectively correct the jitter-induced
phase error in OFDI system.
Near-infrared (NIR) fluorescence imaging is a novel optical technique with an ability of probing larger
volume of tissues or lesions located in deep tissue areas. An integrated fluorescence and reflectance imaging
system was developed to evaluate its potential for cancer diagnosis. The results show that the NIR
autofluorescence intensity of normal colon tissues is significantly higher than that of cancer, and the
diagnostic accuracy of 92.8% can be achieved using NIR autofluorescence/reflectance imaging. This work
demonstrates that NIR autofluorescence/NIR reflectance imaging technique has potential for colonic cancer
diagnosis and detection.
KEYWORDS: Near infrared, Spectroscopy, Cervix, In vivo imaging, Near infrared spectroscopy, Diagnostics, Principal component analysis, Tissues, Algorithm development, Cancer
The purpose of this study is to explore the feasibility of utilizing near-infrared (NIR) autofluorescence
spectroscopy for in vivo diagnosis of precancer (i.e., dysplasia) in the cervix. A rapid NIR spectroscopy
system in combination with a fiber-optic probe was developed for the in vivo NIR fluorescence
measurements under the 785 nm laser excitation. Multivariate statistical techniques including principal
component analysis (PCA) and linear discriminant analysis (LDA) were employed to develop the diagnostic
algorithms for spectra classification. Classification result obtained from the PCA-LDA model based on tissue
NIR autofluorescence data yielded a diagnostic sensitivity of 84.8% and specificity of 85.1% for
discrimination of precancer from normal cervical sites. The results demonstrate that NIR autofluorescence
technique has the capacity for the noninvasive, in vivo diagnosis of precancer in the cervix.
KEYWORDS: Raman spectroscopy, Near infrared, In vivo imaging, Tissue optics, Spectroscopy, Cancer, Cervix, Principal component analysis, Diagnostics, Algorithm development
Near-infrared (NIR) Raman spectroscopy has shown promise to detect cancer and precancer in human
through measuring the biomolecular and biochemical changes of tissue associated with diseases
transformation. Most of studies of NIR Raman spectroscopy on tissue diagnosis are concentrated on the
so-called fingerprint region (800-1800 cm-1), there are only very limited work for tissue diagnosis using
the high wavenumber (2800-3700 cm-1) spectral features. The purpose of this study is to explore the
ability of NIR Raman spectroscopy in high wavenumber region for the in vivo detection of cervical
precancer. A rapid NIR Raman spectroscopy system associated with a fiber-optic Raman probe was
used for the in vivo spectroscopic measurements. Multivariate statistical techniques including principal
components analysis (PCA) and linear discriminant analysis (LDA) were employed to develop the
diagnostic algorithm based on the spectral data from 2800-3700 cm-1. Classification result based on
PCA-LDA showed that high wavenumber NIR Raman spectroscopy can achieve the diagnostic
sensitivity of 93.5% and specificity of 95.7% for precancer classification.
The purpose of this study was to explore the feasibility of using near-infrared (NIR) Raman spectroscopy and
multivariate techniques for distinguishing cancer from normal and benign tissue in the colon. A total of 105 colonic
specimens were used for Raman studies including 41 normal, 18 polyps, and 46 malignant tumors. The multivariate statistical techniques such as PCA-SVM were utilized to extract the significant Raman features and to develop
effective diagnostic algorithms for tissue classification. The results showed that high-quality Raman spectra in the
800-1800 cm-1 range can be acquired from human colonic tissues in vitro, and Raman spectra differed significantly
between normal, benign and malignant tumor tissue. PCA-SVM yielded a diagnostic sensitivity of 100%, 100%, and
97.7%, and specificity of 99.8%, 100%, and 100%, respectively, for differentiation between normal, polyp, and
malignant tissue. Therefore, NIR Raman spectroscopy associated with multivariate techniques provides a significant
potential for the noninvasive diagnosis of colonic cancers in vivo based on optical evaluation of biomolecules.
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