Imaging through scattering media such as biological tissue is challenging due to the multiple scattering of light which severely degrades image quality and obscures hidden objects. This work experimentally validates an approach for imaging objects embedded within scattering media by combining multiview optical setup, based on lens array, and various image processing techniques. Experiments were carried out in transmission mode upon targets of different geometric shapes embedded within different turbid environments. Different setup conditions such as medium turbidity, target shape, illumination state, and image processing were tested and compared. A quantitative image quality metrics including contrast- to-noise ratio, signal-to-noise ratio, contrast, and visibility were adopted to evaluate the quality of the reconstructed targets. Results indicate an effective performance of the proposed methodology for imaging in turbid media.

The study of the relationship between the shape of an object and the patterns of liquid flowing through them is of great interest in many fields of science and industry. Generally, objects with different geometry will generate different flow map which is related to their shape. A wide variety of methods have been proposed with the goal of detecting object by shapes. In this context, here, we explore and demonstrate the combination of machine learning and laser speckle contrast imaging to classify the shape of an object via liquid flowing through it. In our setup, laser light shines on the flow which passes through four objects of different shapes. The diffused speckle images are acquired by a camera and are converted to flow maps on the computer. These maps are then fed into a convolutional neural network for classification and recognition of the objects. We created a database with experimental flow maps and trained the SqueezeNet model to classify new maps; 70% images were used for training and 30% for validation and testing per object. Through experimental results, we show that the proposed method can successfully classify objects with a high accuracy rate. The findings of this study could, for example, be used to discriminate between different types of prostate cancer, especially where identifying abnormal flow map during urination holds diagnostic value.

Optical imaging setup based on the combination of spatially modulated visible light illumination and laser speckle technique is utilized to evaluate the effect of anesthetic drugs on several brain parameters including absorption, scattering, oxygen saturation, cerebral blood flow and more. Two anesthetic drugs ketamine-xylazine (KX) and thiopental were investigated. During experiments, a series of visible sinusoidal patterns with different spatial frequencies are projected onto a mouse brain and diffusely reflected light is recorded by a camera. Then, illumination is blocked, and a laser light shines on the brain while the diffusely reflected light is recorded by the same camera. In this manner, visible and laser speckle images are captured and processed to reconstruct the cerebral tissue features. Variations in optical properties, levels of tissue chromophore content, and in blood flow were observed.

Imaging through scattering media is challenging due to the multiple scattering of light which severely degrades image quality and obscures hidden objects. This study experimentally validates a practical approach for imaging and classifying fluorescent objects embedded within scattering media by combining optical and computational techniques. A lens array was utilized to capture single-shot images of fluorescent objects from different viewpoints embedded between layers of biological tissue and illuminated by laser light. The resulting sub-images were extracted, digitally cropped, and evaluated using a contrast-to-noise ratio (CNR) metric. A sorting algorithm ranked the sub-images from high to low quality based on their CNR values. High-quality sub-images were aligned to a common center and averaged, excluding those with low CNR, to enhance image reconstruction. A support vector machine was trained on reference images to facilitate subsequent classification during the reconstruction process. High classification accuracy was achieved for fluorescent objects of varying geometric shapes.

In this work, we employed laser speckle contrast imaging coupled with the analysis of several statistical moments on the recorded images to recognize the shape of an object through the analysis of the pattern of a flow around it. We postulated that a flow through different geometries would generate a unique flow structure and thus the mathematical analysis of the differences can be utilized to detect and recognize various shape geometries. To this aim, the flow of scattered liquid that had passed through objects of different shapes was illuminated by a laser beam and the diffused speckle image was recorded by a camera. In the computer, the captured speckle image was first converted to a flow image and then mathematically analyzed to recognize the shape of the objects. Three out of the eight statistical metrics were found to be the best candidates for the recognition of shapes, thus proving our hypothesis.

Continuous measurement of pressure is vital in many fields of industry, medicine, and science. Of particular interest is the ability to measure pressure in a noninvasive and contact-free manner. This work presents the potential of oblique incident reflectometry (OIR) to monitor variation in pressure via the reduced scattering parameter (μs′). Pressure deforms the geometry of the medium and causes distortion of its internal structure and the spatial distribution of optical properties. Light scattering is related to the morphology (size, density, distribution, etc.) and refractive index distributions of the medium, and applied pressure will influence directly these parameters. Therefore, we assume that pressure can be quantitatively assessed through monitoring the reduced scattering coefficient. For this purpose, the technique of OIR to evaluate the scattering parameter during pressure variations was utilized. OIR is a simple noninvasive and contact-free imaging technique able to quantify both absorption and scattering properties of a sample. In our setup, the medium is illuminated obliquely by a narrow laser beam, and the diffuse reflectance light is captured by a CCD camera. In offline processing, the shift (δ) of the diffuse light center from the incident point is mathematically analyzed and μs′ coefficient (μs′∼δ−1) is extracted. We present here confirmation of the validity of this assumption through results of a series of experiments performed on turbid liquid and artery occlusion of a human subject under different pressure levels. Thus, μs′ has the potential to serve as a good indicator for the monitoring of pressure.

We present herein the combination of Fourier transform profilometry based orthogonal projection with a laser speckle imaging method to provide three-dimensional (3D) fingertip blood flow imaging and shape reconstruction for biometric authentication purposes. In the proposed approach, orthogonal sinusoidal grating patterns with a fixed spatial frequency emanating from a digital projector first illuminate the surface of a person's finger. Then, the projector is blocked and a laser source illuminates the finger. The projector and laser work alternately such that only one source illuminates the finger at a given time allowing no illumination crosstalk between the two devices. The deformed grating patterns and the diffuse backscattered laser light, respectively, are recorded on the same CCD camera. In the computer, the 3D shape of the finger is reconstructed simultaneously with a blood flow map of the finger. Finally, the images are merged yielding a dynamic flow topography visualization of the person's finger. This dual-modality strategy utilizes the strengths of each imaging method in a complementary way, thus enabling simple 3D shape recovery and characterization of a specific user, and increasing the specificity of authentication security. The method is experimentally validated on both human and fake fingers through VanderLugt optical correlation framework. Overall, the results demonstrate the utility of our proposed approach for biometric authentication. We envision that our optoelectronic setup could be potentially integrated into a wide variety security platform.

The Kubelka-Munk (KM) model is frequently used to describe the optical properties (absorption and scattering) of inhomogeneous media in terms of the measured reflection and transmittance. The classical KM is modeled by two fluxes which counterpropagate in the media and can be described by system of differential equations. This system, on the other hand, can also be rewritten in the form of state equations, widely used in many areas of engineering such as electrical circuits, control systems and more. Here, we describe the simple mathematical expression of light reflectance and transmittance in turbid media based on the KM light propagation model via a system of state equations. To this end, we demonstrate the use of the state equations to solve KM differential equations in a simple and straightforward manner. To validate the solution of our model against the KM solution, different turbid media including tissue phantoms, milk with varying concentrations of sugar, human hands, and mouse brain were tested. We used a spatial frequency domain imaging system to recover the absorption and scattering properties of each of the media. These measured properties were then introduced into both models to obtain diffuse light reflectance and transmittance information. Experimental results demonstrate significant concurrence between the two approaches within a minute difference in all tested cases. The results presented in this study demonstrate the validity of our alternative strategy to the traditional KM solution.

Skin cancer, an anomalous development of skin cells in the epidermis, is among the most common types of cancer worldwide. Because of its clinical importance and to improve early diagnosis and patient management, there is an urgent need to develop noninvasive, accurate medical diagnostic tools. To this aim, light reflectance spectroscopy over the visible and near-infrared spectral range (400-1000 nm) based on a single-fiber six-around-one optical probe was applied to extract nine features used for diagnostics. These features include skewness, entropy, energy, kurtosis, scattering amplitude, and others, and are spread over each of four different spectral signatures, namely, light reflectance, absorbance, scattering profile approximation, and absorption/scattering ratio. Our preliminary studies focused on 11 adult patients with diagnoses of malignant melanoma (n = 4), basal cell carcinoma (n = 5), and squamous cell carcinoma (n = 2) in a variety of locations on the body. Measurements were taken first in vivo before surgery, at the site of the lesion and from healthy skin of the same patient, and ex vivo after surgical excision, where the lesion was rinsed in saline solution and measurements of the reflected light from the "inside" facing plane of the tissue were taken in the same manner. Overall, experimental results demonstrate that by examining a variety of wavebands, features, and statistical metrics, we can detect and distinguish cancer from normal tissue and different cancer subtypes. Nevertheless, discrepancies in results between in vivo and ex vivo tissue were observed and explanations for these discrepancies are discussed.

Polyp segmentation is an important task in early identification of colon polyps for prevention of colorectal cancer. Numerous methods of machine learning have been utilized in an attempt to solve this task with varying levels of success. A successful polyp segmentation method which is both accurate and fast could make a huge impact on colonoscopy exams, aiding in real-time detection, as well as enabling faster and cheaper offline analysis. Thus, recent studies have worked to produce networks that are more accurate and faster than the previous generation of networks (e.g., NanoNet). Here, we propose ResPVT architecture for polyp segmentation. This platform uses transformers as a backbone and far surpasses all previous networks not only in accuracy but also with a much higher frame rate which may drastically reduce costs in both real time and offline analysis and enable the widespread application of this technology.

Background Conventional calibration of the gamma camera consists of the calculation of calibration factors (CFs) (ratio of counts/cc and true concentration activity) as the function of the volume of interest (VOI). However, such method shows inconsistent results when the background activity varies. The aim of the present study was to propose a new calibration method by considering the sphere-to-background counts/voxel ratio (SBVR) in addition to the VOI for CFs calculation. A PET cylindrical flood phantom, a NEMA IQ body phantom, a Data spectrum Torso Phantom (ECT/TOR/P) and a LK-S Kyoto Liver/Kidney phantom were used. The NEMA IQ phantom was used to calibrate the camera and to produce CFs for the different spheres volumes and for varying sphere-to-background activity ratios. The spheres were filled with a uniform activity concentration of 177 Lu, while the background was first filled with cold water and activity was added between each SPECT scan. SPECT imaging was performed for 30-s, 20-s, and 10-s exposure per view. The calculated CFs were expressed as function of the sphere volume and SBVR. The obtained CFs were validated for an additional NEMA IQ acquisition with different activities in spheres and background and for the Torso and Liver/Kidney phantoms with inserted NEMA IQ spheres. The quantification accuracy was compared with the conventional method not taking SBVR into consideration. Results The relative errors in quantification using the NEMA IQ phantom with the new calibration method were 0.16%, 5.77%, 9.34% for the large, medium and small sphere, respectively, for a time per view of 30-s. The conventional calibration method gave errors of 3.65%, 6.65%, 30.28% for 30-s. The LK-S Kyoto Liver/Kidney Phantom resulted in quantification errors of 3.40%, 2.14%, 11.18% for the large, medium and small spheres, respectively, for 30-s; compared to 11.31%, 17.54%, 14.43% for 30-s, respectively, for the conventional method. Similar results were obtained for shorter acquisitions times with 20-s and 10-s time per view. Conclusion These results suggest that SBVR allows to improve quantification accuracy. The shorter time-per-view acquisitions had similar relative differences compared to the full-time acquisition which allows shorter imaging times with 177 Lu and improved patient comfort. The SBVR method is simple to set up and can be proposed for standardization.

This work employs an integrated optical imaging system to evaluate the effect of anesthetic drugs on several brain parameters including absorption, scattering, oxygen saturation, and cerebral blood flow (CBF). Two anesthetic drugs, namely ketamine-xylazine (KX) and thiopental, frequently used by researchers in mouse experiments, were investigated herein. The combined system is based on spatially modulated visible light illumination and a laser speckle based-technique. With this apparatus, a series of structured light (sinusoidal patterns) with phase shifts at each of two spatial frequencies (low and high) are projected onto the surface of a mouse brain and diffusely reflected light is acquired by a camera. A six-position filter wheel, equipped with five visible bandpass filters, is placed at the output of the projector. Then, structured illumination is blocked and a laser source illuminates the tissue while the diffusely reflected light is captured by the same camera through the remaining open hole in the wheel. In this manner, visible and laser speckle images are captured and processed off-line to reconstruct the cerebral tissue features. This work's major findings are six-fold: first, over a specific range of the spectrum a difference of more than 20% was found in the absorption coefficient presumably as a sensitivity to hemoglobin. Second, a difference of more than 20% was observed between drug types in the scattering coefficient along the entire spectral range. Third, CBF varied considerably in thiopental anesthesia throughout the experiment. Fourth, oxygen saturation level remains stable in both anesthetics. Fifth, thiopental keeps blood glucose in normal range while with KX glucose level increases. Sixth, the co-registered system is capable of revealing quantitative functional contrast and may be an attractive method in a variety of biomedical applications. Overall, these findings suggest that the choice of KX or thiopental should be carefully considered as a component of study design in research utilizing animal models.

We experimentally demonstrate an integrated approach for improving reconstruction quality of targets embedded within a turbid media by integrating multiple viewpoints (projections) together with an extended unsharp masking (eUSM) algorithm recently demonstrated in atmospheric solar images. In the setup, objects with differing geometrical shapes and sizes were embedded within media with a range of turbidity and varying attenuation and viscosity values. The medium was illuminated with a laser beam after which multiview images, obtained by lens array, were captured with a camera. In offline processing, each sub-image (viewpoint) was digitally cropped, extracted from the array, eUSM filtered, and qualitatively evaluated by contrast metrics. After sub-images were sorted and ranked based on metric values, the selected 'best' sub-images were shifted to a common center and co-added with others using a shift-and-add (SAA) method to form a single average image revealing the shape of the object. Linear polarization was also introduced into the setup to give higher contrast image quality. Moreover, by optimizing eUSM parameters, further improvement was achieved in the reconstruction. Experimental results illustrate the improvement of the proposed approach as compared to the previously described SAA method alone.

This work employs a laser speckle imaging-based technique in conjunction with analysis of different statistical orders on the acquired images to rapidly screen between groups of mice in different glycemic states across a range between 46 to 476 mg/dL. Nine mathematical parameters were extracted and tested from the captured images including speckle contrast, power spectrum density, derivatives, skewness, kurtosis, and more. Since the body and its organs are dependent on a continuous supply of glucose as a source of energy, we postulate that glycemic states alter body function through variations in metabolites composition and cell structure of the tissue. This alteration in turn modifies the spatial statistics of the acquired speckle patterns which can be analyzed to help differentiate between glycemic states. To this end, a total of fifteen mice were used in this study and were divided into three groups of five mice each, characterized by differing glycemic states: high glucose (>250 mg/dL, avg: 415 mg/dL), normal glucose (100–180 mg/dL, avg: 136 mg/dL), and low glucose (<100 mg/dL, avg: 65 mg/dL). Varying glucose concentration was achieved using different commonly used anesthetic drugs in the presence or absence of insulin; high glucose levels were achieved by anesthetic drugs ketamine/xylazine, while low glucose was achieved by insulin injection and thiopental as anesthetic. Following experiments, a commercial finger-stick glucometer device was used as a reference indicator on blood taken from the mouse tail vein. Results from experiments performed by illuminating the mouse tail by a laser beam indicate that five out of the nine mathematical features of the speckle images can distinguish between states and therefore may serve as a useful glycemic screening tool.

In this work, a dual-display endoscopic vision system was designed as a multi-parametric tool to monitor dynamic responses of biological tissues. The endoscope integrates oximetry principles, laser speckles imaging, and image fusion processing to simultaneously monitor hemodynamic and metabolic information with high spatial resolution. In addition, morphological changes of the tissue were evaluated using linear approximation to Rayleigh-MIE scattering over the visible range. The setup contains a white light source (QTH lamp), an NIR laser (810 nm), commercially available endoscope, high speed four position filter wheel, and two CCD cameras (color and monochromatic) for near real-time processing and display. Validation of the system was demonstrated in two models of tissue injury challenge, including a drug toxicity experiment conducted on a mouse model and the artery occlusion of a human finger. The experimental results illustrate the ability of our system to simultaneously map and temporally track changes in tissue parameters which has the potential to provide valuable insight into the physiological state of the tissue during endoscopic surgical procedures, making it attractive in the future for use in clinical practice and research applications.

In order to characterize biological tissue or to model light propagation in tissue medium it is necessary to define four key optical parameters, namely, absorption coefficient μa, reduced scattering coefficient μs’, anisotropy factor g (or scattering phase function), and refractive index n. Successful derivation of these valuable parameters can provide comprehensive information regarding the condition of tissue during disease pathogenesis and therapy and help to build accurate light propagation simulator. Usually, absorption and reduced scattering coefficients are investigated while the other two are assuming to be constant factors (g ≈ 0.9, n ≈ 1.4) across the near-infrared region. In order to quantify the g and n spectrum of a sample, we propose the use of spatial frequency domain (SFD). In SFD, periodic illumination patterns (structured illumination) at different spatial frequencies and wavelengths are serially projected onto the sample surface. Then, the collected diffusely reflected light is analyzed via the diffusion equation (or Monte Carlo simulation) in SFD to separately recover the sample’s absorption and scattering properties over a wide field of view. This work aimed to use the solution of the diffusion equation (DE) developed for the diffuse reflectance in SFD to estimate the wavelength-dependent variability of the g and n parameters of various turbid samples in the near-infrared spectral region. Since the solution of DE contains four unknown parameters, we use four different spatial frequencies to extract these four components; four equations at four different frequencies with four unknown parameters are solved at each of the four discrete wavelengths used. During data processing of n, its wavelength-dependence was fitted using the dispersion model of Sellmeier. While there are several approaches to retrieving information about g and n, the work in SFD offers an easier and more straightforward framework and grants expanded capability to this domain by enlarging its measurement range.

A method that combines Lucy-Richardson deconvolution algorithm (LRA) with a multiview projection setup to improve imaging reconstruction of objects embedded in scattering media is presented. In the first step, the medium was illuminated with a laser beam and single-shot multiple images of the object were obtained from different viewpoints. Next, the above procedure was performed under the same conditions but only the medium, without the object, was illuminated by a light point source to obtain the system PSF. Third, each sub-image of the object was digitally cropped, extracted from the array, and deconvolved with the average PSF image following the LRA. Finally, all the sub-deconvolved images were shifted to a common point and superimposed to reveal the shape of the hidden object. Various setup conditions including medium turbidity, thickness, target shape, and illumination state were tested and compared. Results indicate an effective performance of the proposed method.

Treatment of traumatic brain injury, within a few minutes and even before transportation to the nearest medical facility, can significantly curtail injury severity and even prevent death. The primary aim of this study was to evaluate the effect of laser irradiation as a therapeutic intervention tool immediately following brain injury. To this end, a dual-wavelength laser speckle contrast imaging (DW-LSCI) system based on two laser sources at two wavelengths 532 and 660 nm was employed to monitor changes in cerebral blood flow, tissue saturation and rate of oxygen consumption in a mouse model of intact head injury. In addition, structural changes of tissue were evaluated using linear approximation to Rayleigh-MIE scattering in the range between the two laser wavelengths. Furthermore, cerebral tissue temperature was imaged by a thermal camera providing additional information on physiological brain tissue condition. Experiments were conducted on anesthetized mice (n = 6, female) subjected to a closed head weight-drop model of focal brain injury. After 5 min of baseline measurement, focal brain injury was induced and measurements were conducted for 10 min. Low-level laser therapy (LLLT) was than administrated for a duration of 15 min with uniform exposure of 45 J/cm2 from a CW diode laser source (810 nm). Concurrently, measurements were carried out over the treatment time interval. Laser illumination was then blocked and measurements continued for another 20 min, followed by euthanasia. In comparison to baseline measurements, noticeable variations were revealed post-injury which indicate the severity of brain damage. The use of LLLT inhibited the development of complications in the injured mice by increasing blood flow and saturation and overall oxygen consumption level over the injured area which highlights its effectiveness as a neuroprotective agent immediately following brain injury. Different doses of low-level laser irradiation were also tested (n = 12) with less effectiveness on cerebral parameters. The results presented here support our hypothesis that a high dose of laser irradiation as a first aid can attenuate the injury and save the brain from further worse outcome. To the best of our knowledge, the implementation of the DW-LSCI system to monitor brain hemodynamic and metabolic response to LLLT shortly after head injury in intact mouse brain has not been previously reported.

Three-dimensional (3D) measurement of an object is widely used in many fields including machine vision, quality control, robotics, medical diagnostics, and others. High-precision 3D surface topography is necessary for describing object shape accurately with high spatial resolution. A combined approach to improve 3D object shape recovery based on Fourier orthogonal fringe projection together with Hilbert transform is proposed and demonstrated. This new idea of combination is highly effective due to the suppressing of background intensity of the deformed fringe pattern while the zero spectrum is extracted precisely and easily. Removing the zero order component leads to increase the visualization and resolution of the measured object. Application of Hilbert processing for object shape recovery in orthogonal Fourier projection domain to improve 3D visualization has not been reported before. The processing framework of this strategy is described in detail. Validation of the proposed method is verified by experiments including visualization of objects with various shapes and sizes. A comparison between profilometry methods is also given which verify better performance in reconstruction of complex objects. 3D reconstruction of flow running at different speeds on a scattering medium with this combined approach is also demonstrated for the first time.