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Life sciences are devoted to the research of all aspects of living organisms. As the basic unit of a living organism, biological cells are diverse and different. These differences are a direct reflection of their corresponding biological characteristics, so "seeing the cell more clearly" has been the goal of scientists at home and abroad. Based on the principle of coherent imaging, digital holographic quantitative phase microscopy imaging technology adopts digital recording and numerical reconstruction to realize phase retrieval and three-dimensional reconstruction. It has become one of the most promising methods in cellular research with better imaging efficiency, reconstruction accuracy, and stability. However, in the band-pass filtering process, tilt aberrations are often introduced due to manual frame selection of the +1 order spectrum, which is detrimental to the imaging quality of the system. To address this problem, this paper proposes a method to automatically determine the spectral center based on the surface fitting of a two-dimensional Hann window function, which improves the phase retrieval accuracy and imaging quality without reducing the computational speed. A more accurate spectral center can be obtained by conducting the sub-pixel spectral center of surface fitting. In addition, the accuracy is sub-pixel level and requires sub-pixel displacement correction of the tilted phase plane to obtain better quantitative phase retrieval results and imaging quality.

As a new imaging method with high optical contrast and large ultrasonic detection depth, photoacoustic imaging breaks through the barrier between resolution and imaging depth of traditional optical imaging technology, and obtains rapid development. As an important branch, photoacoustic microscopy (PAM) imaging technology inherits the advantages of photoacoustic imaging technology. Using optical focusing imaging mode, high contrast and high resolution biological tissue structure, molecular and functional imaging can be realized, which has potential application value in neuroscience, ophthalmology, vascular biology and dermatology. However, PAM relies on close optical focusing to achieve high resolution imaging effect. The traditional PAM usually relies on high NA objective lens for laser focusing, which limits the depth of field (DoF) size of the imaging system while obtaining high lateral resolution, so that PAM has limitations in three-dimensional imaging and obtaining information of different depth directions. Moreover, the sharp decline of spatial resolution and signal-to-noise ratio in the defocus area will also affect the accuracy of quantitative analysis of tissue morphology and structure. In order to solve this problem, this paper proposes a three-dimensional photoacoustic information fusion method based on principal component analysis fusion algorithm. Virtual photoacoustic microscopy platform was used to obtain three-dimensional vascular data with different focuses. The B-scan slices at the same position of three-dimensional photoacoustic data with different focuses are fused in turn. The fused slices are recombined to reconstruct high-resolution photoacoustic data with large depth of field. The results show that this method can achieve depth of field extension while maintaining the high resolution advantages of photoacoustic microscopy.

As ultrasonic phased array total focus method (TFM) imaging technology can achieve full range of dynamic focusing with clear imaging and strong ability to characterize defects, TFM imaging algorithm has become the gold standard for testing other post-processing algorithms. However, due to the large amount of data and time-consuming calculation, the TFM imaging has limited the application in some industrial fields. With the reduction of the number of phased array transmitting elements, the imaging quality gets worse. To improve the imaging efficiency of the TFM algorithm and ensure the imaging quality, this paper proposes a method combining Siamese Convolutional Neural Network (SCNN) and Genetic Algorithm (GA) to obtain an optimal sparse array layout, to approximate the imaging effect of the full array with limited effective elements. After selection of an appropriate sparsity ratio, GA is used to optimize the sparse array layout. The sparse array elements emit ultrasonic waves, and the full array elements receive echo signals for imaging. SCNN is trained by a self-built industrial defect dataset, to output the similarity between the sparse-TFM image and the full-array TFM image. The similarity is used as an objective evaluation index to evaluate the imaging effect. The optimal sparse array layout is proposed by combining subjective evaluation with objective evaluation.

Three-dimensional (3D) imaging technology has been widely applied in various fields, such as intelligent manufacturing, online inspection, reverse engineering, cultural relic protection, etc. In this work, we present a high-accuracy real-time omnidirectional 3D scanning and inspection system based on fringe projection profilometry. Firstly, a multi-camera system based on geometric constraints is constructed to perform stereo phase unfolding without additional auxiliary projection images to ensure high-accuracy 3D data acquisition in real time. Then, we propose a rapid 3D point cloud registration approach combining simultaneous localization and mapping (SLAM) with iterative closest point (ICP) techniques to achieve alignment of point cloud slices with accuracy of up to 100 microns. Finally, a cycle-positioning-based registration scheme is developed to allow for real-time 360 degree 3D surface defect inspection. The experimental results show that our system is capable of real-time omnidirectional 3D modelling and real-time 360° defect detection.

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.

Photoacoustic microscopy (PAM) is an imaging technology developed rapidly in recent years. The technology has the advantages of high resolution, rich contrast of optical imaging and high penetration depth of acoustic imaging. It is widely used in biomedical field, such as tumor detection. Photoacoustic images can not only reflect the structural characteristics of tissues, but also reflect the metabolic state, disease characteristics and even nerve activity of tissues, so as to realize functional imaging. Photoacoustic (PA) signals are inherently recorded in noisy environments and are also exposed to the noise of system components. The presence of noise has a great negative impact on image quality and interferes with image details. Therefore, it is necessary to reduce the noise in PA signals to reconstruct images with less interference information. Because deep learning can process image information quickly and efficiently, deep learning has become the preferred method for photoacoustic image denoising in recent years. In this study, the photoacoustic blood vessel image obtained was added with a certain intensity of Gaussian noise, and the denoising generative adversarial network based on Wasserstein distance (WGAN) was used to denoise the photoacoustic blood image. For the purpose of evaluation, the Peak Signal-to-Noise Ratio (RSNR), Structural Similarity Index Metric (SSIM), Universal Quality Index (UQI) and Image Enhancement Factor (IEF) were calculated. According to the calculation results, this study effectively improves the image quality, proves the effectiveness of the neural network, and has good clinical significance and broad application prospects.

Millimeter wave and terahertz imaging technology is a frontier detection technology with practical application values for people's livelihood, national defense and other fields. This paper summarizes millimeter wave and terahertz imaging technology, consisting of its technical principle, historical development, research status and practical applications.In the paper,discusses focus on the applications of millimeter wave and terahertz imaging technology in security inspections.

Photoacoustic imaging (PAI) is an emerging and efficient imaging technology based on the discovery of the photoacoustic effect. It is a medical imaging technology used for internal imaging of human tissues. It combines the advantages of acoustic imaging and optical imaging. However, because it achieves high resolution through the intense focus of the laser beam, the resulting photoacoustic image will have a poor depth of field and less structural information. In order to solve this problem, an end-to-end general network fusion framework based on convolutional neural networks is applied to extract important image information from the input image through the convolutional layer, and then we use appropriate fusion rules for feature fusion, and finally the fusion features are processed to obtain large-volume and high-resolution photoacoustic images. Analyzing the source image and the fusion image can prove that the model embodies good generalization ability and excellent experimental results in the process of photoacoustic image fusion.

Photoacoustic imaging has gradually developed into a new and important imaging technology. As an important branch of photoacoustic imaging, optical-resolution photoacoustic microscopy combines the advantages of optical imaging and acoustic imaging, it has the advantages of high resolution, high contrast, high sensitivity and so on. However, in order to obtain high resolution, it is often necessary to focus the laser beam strongly, which will lead to the small depth of field and the inability to obtain large-scale structural information. However, in clinical diagnosis, doctors hope to obtain large-scale, high-resolution structural and functional information as much as possible, so it is of great significance to solve the problem of small depth of field in photoacoustic microscopy. Here, we proposed three-dimensional fusion for large volumetric optical-resolution photoacoustic microscopy. Firstly, two groups of virtual cerebral vascular 3D photoacoustic data obtained at different focal locations were obtained by using virtual photoacoustic microscopic imaging platform. Then, based on the multi-scale weight gradient fusion algorithm, the B-scan data of mouse cerebrovascular data were fused, and the maximum projection reduction was performed on the fused 3D data. Finally, the images before and after fusion were compared. Experimental results show that this algorithm can effectively obtain large volumetric and high-resolution photoacoustic images.

Millimeter (MM) and Terahertz (THz) waves are transparent to many non-metallic materials and is expected to be applicable for non-destructive inspections. Especially, the radar 3D imaging technology using the semiconductor continuous wave (CW) MMW and THz sources is important because of its compactness, stability and high signal-to-noise ratio (SNR). In this presentation, we will introduce the development of a prototype of a 300 GHz walk-through body scanner for the security gates. For the actual system, we are developing the frequency-modulated continuous wave (FMCW) imaging method around 300 GHz. We have demonstrated the 3-dimentional real-time imaging of a material hidden underneath cloths. We will demonstrate the system configuration and the current status as well as some results of the actual imaging.

Photoacoustic imaging(PAI) is a emerging powerful and efficient imaging technology. Optical-resolution photoacoustic microscopy is an useful photoacoustic imaging technique combining the advantages of both optical imaging and acoustic imaging which obtains many attractive advanges such as high resolution, high contrast and so on. Laser beam is often focused strongly to achieve high resolution. However, this will lead to a poor depth-of-field and less structural information which limits the further application of this technology. Aimage fusion method based on CNN feature extraction is proposed to achieve large volumetric optical-resolution photoacoustic microscopy. First, two groups of simulated 3D photoacoustic data of different focal locations were obtained through photoacoustic microscopic imaging platform. Then B-scan data were fused and maximum projection of the reconstructed 3D data is taken to display the photoacoustic information. By comparing the source images and the fused image, we show that the proposed method can be implemented to obtain large volumetric and high-resolution photoacoustic images.

In dynamic scene imaging, multi-sensor high dynamic range (HDR) fusion technology used to solve the ghosting problem of multi-exposure fusion based on camera aperture, and achieve high-quality HDR video imaging. Therefore, it has made significant progress in image capture and synthesis of HDR optical imaging. First, we introduce the development status of multi-sensor based on time bracketing and space bracketing. Then introduce the different method of multi-exposure image fusion, including aligning different exposures, rejecting misalignment information and patch-based optimization. Finally, discuss the key technical issues and development trends of this technology in HDR imaging technology.

In recent years, photoacoustic imaging, as a new type of biomedical imaging method, combines the advantages of high selectivity in pure optical tissue imaging and deep penetration in pure ultrasound tissue imaging to obtain high-resolution and high-contrast tissue images. The use of photoacoustic imaging technology to deal with complex medical tissue problems is still a new research direction. How to compress large amounts of data and quickly transmit and store important value information has become a problem waiting for optimization. This paper uses the StagewiseOMP and tracking algorithm to combine it with the photoacoustic imaging of the k-wave simulation toolbox to rebuild a virtual simulation platform for blood vessel imaging. On the one hand, compressed sensing can reduce the sampling rate and speed up imaging. On the other hand, it can modify the demand for hardware equipment to facilitate data transmission and storage. A simulation model of photoacoustic field propagation, photoacoustic signal recording and image reconstruction was established using the k-wave simulation toolbox. We have used the excellent performance of the simulation platform through imaging technology to complete the imaging restoration of part of the blood area tube tissue.

In recent years, photoacoustic imaging is a new imaging technology which combines the advantages of high resolution and rich contrast of optical imaging and high penetration depth of acoustic imaging. Therefore, photoacoustic imaging is widely used in biomedical fields, such as brain imaging, tumor detection and so on. As an important branch of photoacoustic imaging, photoacoustic microscopy imaging of optical resolution can reach the submicron level and can meet the needs of multi-scale imaging from cell to tissue. However, the strongly focused Gaussian beam is usually used in the traditional system of photoacoustic microscopy imaging of optical resolution, and so the depth of imaging is small, which makes it difficult to achieve fast imaging of large volume. In order to solve this problem, researchers have proposed many schemes, one of which is to use non-diffracted beams instead of Gaussian beams for imaging. As a nondiffracted beam, Bessel beam is applied to photoacoustic microscopy imaging of optical resolution in this study because it has the characteristic of large depth of field. However, in practice, the cost of building the system of photoacoustic microscopy imaging of optical resolution is high and its structure is complex, so there is an urgent need for the technology of virtual simulation for photothermal simulation analysis of the imaging system. In order to achieve this goal, a virtual simulation platform of photoacoustic microscopy imaging system of Bessel beam based on finite element analysis was built by using simulation software COMSOL Multiphysics. In COMSOL, the Bessel beam realized by compound axicon was constructed and then irradiated the gastric tissue and tumor. The propagation of the Bessel beam in the gastric tissue and tumor was simulated by solving the diffusion equation. And the temperature changes of the gastric tissue and tumor were obtained by solving the equation of biological heat transfer. This study is helpful to understand the propagation of Bessel beam in the gastric tissue and tumor and the interaction between them, and has a certain theoretical significance for biomedical optical imaging.

Photoacoustic imaging is an emerging imaging technology based on the photoacoustic effect. As a hybrid imaging technology that combines pure optical imaging and ultrasound imaging, it also has the advantages of optical imaging with high resolution and rich contrast. And the advantage of high penetration depth of acoustic imaging. With its advantages, photoacoustic imaging has extremely broad applications in biomedical testing, such as brain imaging and tumor imaging. Due to the optical diffraction limit of the objective lens, the image resolution of the obtained image is hard to be further improved, therefore, finer structural information is difficult to obtain. In order to solve this problem, we use an end-to-end convolutional neural network from low resolution to high resolution to further process the obtained low-resolution images to obtain optimized high-resolution image and improve the quality of imaging. A convolutional neural network is built on the pycharm platform through the open source Tensorflow library. Bicubic interpolation is used to preprocess the original data. Then we perform network training on the processed sample data and finally a series of photoacoustic microscopy images of cerebral blood vessels[1,2] were tested. The test results show that the resolution of the image is significantly improved, and a clearer image is obtained. The experimental results verify that this end-to-end convolutional neural network from low resolution to high resolution can effectively improve the resolution of photoacoustic imaging. This has laid a good foundation for the follow-up biomedical research[3] of photoacoustic imaging technology.

With the rapid advancement of imaging technology, space-based remote sensing instruments are becoming more sophisticated and are producing substantially more amounts of data for downloading. Data alteration is very likely to occur during the transmission over the long distances from probes to carrier spacecraft and subsequently back to Earth,. Cyclic Redundancy Check (CRC) is the most well-known data package error check technique which has been used in many applications. Unfortunately, due to its serial computation process, it could be a bottleneck for critical applications that require rapid processing. To overcome such issue, we present here a parallel CRC computational method based on an FPGA with simulation and testing to validate the methodology.

Active millimeter-wave imaging is in widespread use for security screening and other applications. The Pacific Northwest National Laboratory (PNNL) has developed a variety of microwave and millimeter-wave imaging systems and technology, including the cylindrical imaging technology that forms the basis of the L3/Leidos ProVision system. Since 2016, PNNL has been actively participating in a working group that is developing a proposed American National Standards Institute (ANSI) standard (N42.59) that will be used to evaluate and verify performance of active millimeterwave imaging systems used for security screening of humans. The standard is developing image quality tools (IQTs) that will be used to assess a variety of imaging metrics, such as lateral resolution, contrast, and depth resolution. Depth resolution is vital for high-performance microwave and millimeter-wave imaging because it enables precise focusing over a full 3D volume, and allows for differentiation of reflections from multiple surfaces, such as a layer of clothing over the human body. In this paper, depth resolution is analyzed using theoretical simulations and experimental 3D imaging studies. Presented results examine depth resolution using IQTs developed from a thin partially transparent film placed in front of a metallic surface, creating reflections that are laterally aligned and at variable separation. Coherence between these reflections is investigated as it complicates the interpretation of the imaging results.

In recent years, photoacoustic imaging is a new imaging technology which combines the advantages of high resolution and rich contrast of optical imaging and high penetration depth of acoustic imaging. Therefore, photoacoustic imaging is widely used in biomedical fields, such as brain imaging, tumor detection and so on. As an important branch of photoacoustic imaging, photoacoustic microscopy imaging of optical resolution can reach the submicron level and can meet the needs of multi-scale imaging from cell to tissue. However, the strongly focused Gaussian beam is usually used in the traditional system of photoacoustic microscopy imaging of optical resolution, and so the depth of imaging is small, which makes it difficult to achieve fast imaging of large volume. In order to solve this problem, researchers have proposed many schemes, one of which is to use non-diffracted beams instead of Gaussian beams for imaging. As a nondiffracted beam, Bessel beam is applied to photoacoustic microscopy imaging of optical resolution in this study because it has the characteristic of large depth of field. However, in practice, the cost of building the system of photoacoustic microscopy imaging of optical resolution is high and its structure is complex, so there is an urgent need for the technology of virtual simulation for photothermal simulation analysis of the imaging system. In order to achieve this goal, a virtual simulation platform of photoacoustic microscopy imaging system of Bessel beam based on finite element analysis was built by using simulation software COMSOL Multiphysics. In COMSOL, the Bessel beam realized by compound axicon was constructed and then irradiated the human brain tissue. The propagation of the Bessel beam in the brain tissue was simulated by solving the diffusion equation. And the temperature changes of gray matter and blood vessel were obtained by solving the equation of biological heat transfer. This study is helpful to understand the propagation of Bessel beam in human brain and the interaction between them, and has a certain theoretical significance for the optical imaging of human brain.

As an emerging nondestructive imaging technology recently, photoacoustic imaging (PAI), which is based on photoacoustic effect, combines the advantages: the high resolution and contrast of optical imaging and the high penetration depth of acoustic imaging. Thereinto, as a branch of photoacoustic imaging, photoacoustic microimaging inherited the advantages of photoacoustic imaging. The unique focusing mode of photoacoustic microimaging can meet the requirements of higher resolution in biological imaging, thus, it gained extensive applications in medical science field. However, on account of using high numerical aperture objective lens strongly focus on Gaussian beam, traditional photoacoustic microimaging system has shallow depth of imaging field, and its transverse resolution and signal-to-noise ratio deteriorate rapidly outside the focal point, limiting the velocity of large volume imaging. Owing to solve these problems, in this paper, we build a simulation platform for Airy beam photoacoustic microscopy based on K-Wave simulation toolbox. This platform uses Airy beam to inspire initial Photoacoustic signal in large volume and K-Wave simulation toolbox to simulate the propagation, recording and reconstruction process of Photoacoustic signal. As nondiffraction beam, Airy beam features the capacity of large depth of field, thus, its application could reach the requirement of large depth of field imaging of Photoacoustic microscopy system. Measuring the performances of the constructed Photoacoustic microscopy system, we constructed three-dimensional imaging of the blood vessel. By simulating A-Scan, B-Scan and C-Scan, we measured the performances of this system, such as axial resolution, transverse resolution and depth of field. Meanwhile, the three-dimensional imaging of the vertically tilted fiber also verified the three-dimensional imaging capability of the Airy beam photoacoustic microscopy simulation platform. The establishment of the simulation platform has a significance for the theoretical research of photoacoustic microscopy and its application in biomedicine.

Photoacoustic imaging is a promising biomedical imaging technology that can provide the biological information of animals and humans in vivo. Taking advantages of both optics and ultrasound simultaneously, the photoacoustic imaging has rich optical contrast and high ultrasonic resolution in deep tissues. Especially, the optical-resolution photoacoustic microscopy (OR-PAM), the major implementation of the photoacoustic imaging, achieves sharp spatial resolution with focused optical beams. However, developed OR-PAM systems are disadvantaged by limited temporal and/or spatial resolutions by hardware parts. Here, we introduce a high speed super-resolution localization OR-PAM that overcomes the limited resolutions. First, to improve a temporal resolution, we equipped a galvanometer scanner, which has been used in many optical microscopies. Previously, due to the vulnerability of the scanner to water, the PAM system using the scanner scanned only light, not ultrasound. However, our system overcame the weakness by immersing only a scanning part except to a galvanometer part, leading to scanning light and ultrasound simultaneously. Steering both laser beams and ultrasound waves with a scanner’s mirror immersed in the water, our system achieves a wide scanning range and a high signal-to-noise ratio as well as the B-mode imaging speed of 500 Hz. Furthermore, we acquired a super-resolved microvascular image in vivo using an agent-free localization imaging technology based on the fast scanning speed. These results show that our super-resolution high-speed OR-PAM can be used in a variety of fields, including neurology, oncology, and pathology.

Photoacoustic imaging technology is a new imaging technology has emerged in recent years, which combines the advantages of optical imaging and acoustic imaging, optical imaging and acoustic imaging have their own advantages, optical imaging has rich contrast , acoustic imaging has high resolution and high penetration. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection. As an important branch of photoacoustic imaging technology, optical-resolution photoacoustic microscopy can be applied from cells to tissues. However, the traditional optical-resolution photoacoustic microscopy usually uses a tight focused gaussian beam, the depth-of-field is small, and it is difficult to achieve rapid large volumetric imaging. In order to overcome this issue, researchers have proposed many solutions, one of which is to replace gaussian beams with non-diffracted beams. Bessel beam, as a nondiffracted beam, has a large depth of field. We applied it to optical -resolution photoacoustic microscopy. In reality, the cost of building such a Bessel-beam photoacoustic microscopy is relatively high and the system is complicated. Therefore, a virtual simulation technology is urgently needed for simulation research. Here, we proposed a virtual simulation platform of Bessel-beam photoacoustic microscopy based on k-Wave. The platform generates Bessel beam in the focus area of the objective lens by the circular slit, and the Bessel beam irradiates on the sample to generate the initial photoacoustic pressure field in a wide depth range. The k-Wave is used to simulate the propagation of photoacoustic fields, record photoacoustic signals and image reconstruction. The three-dimensional imaging of vascular network further verifies the large depth of field of Bessel-beam photoacoustic microscopy simulation platform. The establishment of the simulation platform is of great significance to the theoretical research of Bessel-beam photoacoustic microscopy and its application in biomedicine.

In an effort to meet the need of imaging technology that reliably measure the dynamics of accommodation, we developed a swept source OCT system for imaging and biometry of the crystalline lens dynamics at high sampling rates (~100 Hz). Preliminary data suggests that high sampling rates improve detection of lens micro-fluctuations during accommodation and that sampling rates higher than 40 Hz might be needed to fully capture crystalline lens dynamics. Long term, the information acquired with the system will improve our understanding of the mechanism of accommodation and enable to design and evaluate new procedures to restore accommodation.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

As an emerging nondestructive imaging technology recently, photoacoustic imaging (PAI), which is based on photoacoustic effect, combines the advantages: the high resolution and contrast of optical imaging and the high penetration depth of acoustic imaging. Thereinto, as a branch of photoacoustic imaging, photoacoustic microimaging inherited the advantages of photoacoustic imaging. The unique focusing mode of photoacoustic microimaging can meet the requirements of higher resolution in biological imaging, thus, it gained extensive applications in medical science field. However, on account of using high numerical aperture objective lens strongly focus on Gaussian beam, traditional photoacoustic microimaging system has shallow depth of imaging field, and its transverse resolution and signal-to-noise ratio deteriorate rapidly outside the focal point, limiting the velocity of large volume imaging. Owing to solve these problems, in this paper, we build a simulation platform for Airy beam photoacoustic microscopy based on K-Wave simulation toolbox. This platform uses Airy beam to inspire initial Photoacoustic signal in large volume and K-Wave simulation toolbox to simulate the propagation, recording and reconstruction process of Photoacoustic signal. As nondiffraction beam, Airy beam features the capacity of large depth of field, thus, its application could reach the requirement of large depth of field imaging of Photoacoustic microscopy system. Measuring the performances of the constructed Photoacoustic microscopy system, we constructed three-dimensional imaging of the blood vessel. By simulating A-Scan, B-Scan and C-Scan, we measured the performances of this system, such as axial resolution, transverse resolution and depth of field. Meanwhile, the three-dimensional imaging of the vertically tilted fiber also verified the three-dimensional imaging capability of the Airy beam photoacoustic microscopy simulation platform. The establishment of the simulation platform has a significance for the theoretical research of photoacoustic microscopy and its application in biomedicine.

Label-free multimodal nonlinear optical imaging and optical biopsies of fresh, unstained, resected tissue specimens offer a wealth of new biomarkers for assessing the tumor microenvironment and diagnosing disease. By developing widely coherent supercontinuum from photonic crystal fibers, new excitation wavelengths can be generated to tailor the light stimulus in new ways. As a result, Simultaneous Label-free Auto-fluorescence Multi-harmonic (SLAM) microscopy can visualize the rich intrinsic molecular, metabolic, and structural information in cells and tissues. Results suggest broad potential of this stain-free, slide-free, imaging technology and methodology for real-time point-of-procedure applications, including the histopathological assessment of living biopsy specimens.

As an emerging nondestructive imaging technology recently, Photoacoustic imaging (PAI), which is based on Photoacoustic effect, combines the advantages: the high resolution and contrast of optical imaging and the high penetration depth of acoustic imaging. Thereinto, as a branch of Photoacoustic imaging, Photoacoustic microimaging inherited the advantages of Photoacoustic imaging. The unique focusing mode of Photoacoustic microimaging can meet the requirements of higher resolution in biological imaging, thus, it gained extensive applications in medical science field. However, on account of using high numerical aperture objective lens strongly focus on Gaussian beam, traditional Photoacoustic microimaging system has shallow depth of imaging field, and its transverse resolution and signal-to-noise ratio deteriorate rapidly outside the focal point, limiting the velocity of large volume imaging. Owing to solve these problems, in this paper, we build a simulation platform for Airy beam photoacoustic microscopy based on K-Wave simulation toolbox. This platform uses Airy beam to inspire initial Photoacoustic signal in large volume and K-Wave simulation toolbox to simulate the propagation, recording and reconstruction process of Photoacoustic signal. As Nondiffraction beam, Airy beam features the capacity of large depth of field, thus, its application could reach the requirement of large depth of field imaging of Photoacoustic microscopy system. Measuring the performances of the constructed Photoacoustic microscopy system, we constructed three-dimensional imaging of the blood vessel. By simulating A-Scan, B-Scan and C-Scan, we measured the performances of this system, such as axial resolution, transverse resolution and depth of field. Meanwhile, the three-dimensional imaging of the vertically tilted fiber also verified the three-dimensional imaging capability of the Airy beam photoacoustic microscopy simulation platform. The establishment of the simulation platform has a significance for the theoretical research of photoacoustic microscopy and its application in biomedicine.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-to-noise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

PURPOSE: Identifying areas of abnormality on MRI brain scans in individuals with focal epilepsy is funda- mental to the diagnosis and treatment of the condition. However, in about a third of patients with focal epilepsy, brain scans appear to be normal (MRI-negative) as human observers cannot detect any abnormality with cur- rent imaging technology. The objective of this paper is to provide a novel approach in presenting localization using machine learning in order to locate areas of abnormality on patients with focal epilepsy on a per-voxel basis by comparing them with healthy controls. As a proof-of-concept, the technique is first applied to patients with visible lesions providing a ground truth (MRI-positive), but future work will extend this to MRI-negative subjects. METHODS: Our data consists of multi-modal brain MR images from 62 healthy control subjects and 44 MRI- positive patients with focal epilepsy. We utilized a support vector machine (SVM) as our probabilistic classifier and train it with two classes of data. We generate probability maps applying our machine learning classifier on all voxels of a test subjects to visualize the predictions. Overlap scores are used to evaluate the classifier performance in MRI-positive patients. RESULTS: Our model reached 83% specificity, 91% sensitivity, and an Area Under the Curve (AUC) of 0.896 for the task of voxel-based classification of normal versus abnormal voxels. In addition, Dice scores of up to 0.66 were achieved for the overlap measure of lesion probability map and the ground truth labels annotated by a neurologist. CONCLUSION: We demonstrated a novel approach in presenting localization using machine learning tech- niques to localize focal epilepsy lesions from multi-modal MR images.

Non-line-of-sight (NLOS) imaging technology is to ‘see’ the target out of sight, such as an object around a corner or hidden by some shelters. However, due to constraints of device definition and computing load, NLOS system is usually expensive and requires hidden objects with special material and simple shape. Besides, imaging space of system is limited. We perform a series of simulation with 1550nm infrared laser to expand the application field and improve the performance of NLOS system. Based on math and physical properties, main experimental components are modeled and data acquisition process is completed first. Then, the ellipsoid inversion algorithm is used to reconstruct the hidden space and imaging results are obtained. Finally, multiple series of system parameters are set and their influence on imaging results is analyzed. Results demonstrate that echo signal intensity after multiple reflections provides adequate information to reconstruct the geometry of a hidden object. However, the number of laser scanning position, resolution of detector, voxel division and location of the scanning area will all have a critical influence on NLOS imaging results.

In recent years, the development of computational imaging technology provides a new method for the realization of non-scanning super-resolution imaging. In this paper, a pixel super-resolution algorithm based on Fourier ptychographic technology is proposed, and the corresponding integrated and systematic programmable aperture coded super-resolution imaging system is constructed. By modulating the intensity with the coded aperture mask, utilizing different system point spread functions to obtain multiple samples of the original scene, and finally adopting sparse optimization iterative algorithm to reconstruct the original image, the result of super- resolution imaging is more than 3.5 times of Nyquist sampling frequency. In this tutorial, the proposed new super-resolution photoelectric imaging technology innovatively adopts the approach of coded aperture to realize image super-resolution imaging and effectively solve image pixelation. High-resolution images beyond the spatial resolution of the detector are obtained without any physical moving device or scanning mechanism. Compared with the traditional micro-scanning technology, it not only improves the reliability and stability of the system but also greatly reduces the cost and volume weight of the system.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform. We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images. The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising. The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

Photoacoustic imaging technology is a new imaging technology has emerged in recent years, which combines the advantages of optical imaging and acoustic imaging, optical imaging and acoustic imaging have their own advantages, optical imaging has rich contrast , acoustic imaging has high resolution and high penetration. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection. As an important branch of photoacoustic imaging technology, optical-resolution photoacoustic microscopy can be applied from cells to tissues. However, the traditional optical-resolution photoacoustic microscopy usually uses a tight focused gaussian beam, the depth-of-field is small, and it is difficult to achieve rapid large volumetric imaging. In order to overcome this issue, researchers have proposed many solutions, one of which is to replace gaussian beams with non-diffracted beams. Bessel beam, as a nondiffracted beam, has a large depth of field. We applied it to optical -resolution photoacoustic microscopy. In reality, the cost of building such a Bessel-beam photoacoustic microscopy is relatively high and the system is complicated. Therefore, a virtual simulation technology is urgently needed for simulation research. Here, we proposed a virtual simulation platform of Bessel-beam photoacoustic microscopy based on k-Wave toolbox. The platform generates Bessel beam in the focus area of the objective lens by the circular slit, and the Bessel beam irradiates on the sample to generate the initial photoacoustic pressure field in a wide depth range. The K-Wave toolbox is used to simulate the propagation of photoacoustic fields, record photoacoustic signals and image reconstruction. The system can image a vascular network. The three-dimensional imaging of vascular network further verifies the large depth of field of Bessel-beam photoacoustic microscopy simulation platform. The establishment of the simulation platform is of great significance to the theoretical research of Bessel-beam photoacoustic microscopy and its application in biomedicine.

Three-dimensional measurement technology is a popular technology in recent years. The traditional binocular vision measurement system has a low image matching accuracy when there are few or many duplicate feature points on the surface of the object to be measured, which affects the work of three-dimensional reconstruction. In this paper, the combination of structured light three-dimensional measurement technology and binocular imaging technology reduces the difficulty of feature point search, increases the accuracy of image matching, and conducts experiments of three-dimensional measurement by building a verification system. The measurement results show that the technology is highly feasible.

Spinal surgery, such as pedicle screw placement, is difficult and risky due to the complexity of the physiological characteristics of the spine. Although intraoperative image guidance using a C-arm or an O-arm can significantly improve the success rate of the procedure, it is time consuming and the person is exposed to X-ray radiation for a long time. To solve this problem, this paper aims to exploring the application of new real-time 3D imaging technology in spinal surgery. Firstly, a CT scan of the pig's spine bone about 20 cm in length was performed and the accurate 3D data was obtained. Then, the 3D reconstruction was performed using a TOF camera and a light field camera. At last, the accuracy of the results was compared. The experimental results show that although the TOF camera has a good real-time performance, its resolution and accuracy of 3D reconstruction are low. The multi-frame superposition method can improve the data accuracy. The real-time performance of the light field camera is slightly lower. And the point cloud acquired by the bone is sparse because the spine texture feature is not obvious. But the accuracy of the 3D reconstruction can be significantly improved after the aid of structured light illumination. Therefore, the two techniques about 3D imaging proposed in this paper have some certain development potential in real-time navigation for spinal surgery.

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.

Photoacoustic imaging is a new imaging technology in recent years, which combines the advantages of high resolution and rich contrast of optical imaging with the advantages of high penetration depth of acoustic imaging. Photoacoustic imaging has been widely used in biomedical fields, such as brain imaging, tumor detection and so on. The signal-tonoise ratio (SNR) of image signals in photoacoustic imaging is generally low due to the limitation of laser pulse energy, electromagnetic interference in the external environment and system noise. In order to solve the problem of low SNR of photoacoustic images, we use feedforward denoising convolutional neural network to further process the obtained images, so as to obtain higher SNR images and improve image quality. We use Python language to manage the referenced Python external library through Anaconda, and build a feedforward noise-reducing convolutional neural network on Pycharm platform.We first processed and segmated a training set containing 400 images, and then used it for network training. Finally, we tested it with a series of cerebrovascular photoacoustic microscopy images.The results show that the peak signal-to-noise ratio (PSNR) of the image increases significantly before and after denoising.The experimental results verify that the feed-forward noise reduction convolutional neural network can effectively improve the quality of photoacoustic microscopic images, which provides a good foundation for the subsequent biomedical research.

Rapid advancements in EO/IR imaging technology boosted by developments in focal plane array technology led to significant increase in performance, availability and accordingly in application in the various systems. As a consequence the more efforts in the area of possible countermeasure development are necessary. Starting from EO/IR generalized image forming process and related influences on the imager performances as key part of imager performance, using knowledge generated from well-established electronic countermeasure science and known results in EO/IR countermeasure application, EO/IR countermeasures classification is proposed. Using this classification the currently known countermeasures are analyzed trying to identify existing challenges for future efforts. Also the recent advanced in image processing techniques, i.e. application of artificial intelligence for automatic target recognition, could be used as EO/IR imaging counter-counter measure and should be considered separately.

Event-Based Sensors (EBSs) are passive electro-optical (EO) imaging sensors which have read-out hardware that only outputs when and where temporal changes in scene brightness are detected. In the case of a static background and platform, this hardware ideally implements background clutter cancellation, leaving only moving object data to be read out. This data reduction leads to a bandwidth reduction, which is equivalent to increasing spatio-temporal resolution. This advantage can be exploited in multiple ways, using trade-offs between spatial and temporal resolution, and between spatial resolution and field-of-view. In this paper, we introduce the EBS concept and our previous experiments and analysis. We discuss important EBS properties, followed by discussion of applications where the EBS could provide significant benefit over conventional frame-based EO sensors. Finally, we present a method for analyzing EBS technology for specific applications (i.e. determine performance compared to conventional technology). This approach involves abstraction of EBS and conventional imaging technology and provides a way to determine the value of EBSs over conventional imaging technology for facilitating future EBS application development.

Surface penetrating imaging technology reconstructs target images that conform to visual perception by interpreting radar data, which reflects the target's geometry and electromagnetic scattering characteristics directly. For surface penetration imaging, various sources of clutter cause failure of image interpretation, therefore clutter suppression is the key to obtain target information accurately. Though clutter suppression approaches aiming at the strong clutter make the target image prominent enough for imaging process, the weak clutter with energy close to the target affects the imaging quality. In this paper, a nonlinear gain control method is proposed for the suppression of weak clutter and improving the contrast of the imaging by non-linearly weakening the data below the threshold and enhancing the data above the threshold. Experiments show that the proposed method can effectively improve the imaging quality.

Millimeter wave (MMW) imaging systems have the capability of detecting anomalous objects, which can include explosive threats and other prohibited items concealed on persons in airport checkpoints and other facilities requiring personnel screening. Benign materials that simulate explosives and other threats are used to test Advanced Imaging Technology (AIT) systems when live threats cannot be placed on human subjects. While laboratory dielectric measurements are used to formulate candidate simulants, it is useful, and sometimes necessary, to independently validate a simulant for an AIT system of interest. An imaging phantom has been fabricated using standard vacuum hardware and thin plastic films for containing samples of interest. The phantom’s design allows for simultaneous imaging of threats and candidate simulants in a fixed, repeatable fashion. The phantom was self-validated with deionized water using a criterion for resolving two overlapped distributions. Results obtained from a subsequent study of a flammable liquid versus its candidate simulant are presented, validating the use of the simulant for use with the target AIT system.

3D-TOF detection technology is a new range-finding and imaging technology. The 3D-TOF camera prepared by this technology can demodulate the reflected light of different phases after the active emission light hits the target to obtain the distance information of the target point. It can obtain two-dimensional gray-scale imaging information, so the technology can obtain the three-dimensional point cloud of the target object in real time without relying on mechanical scanning, which greatly improves the speed of obtaining target information, achieves high frame rate measurement, reduces data processing difficulty, the volume weight of the sensor is greatly reduced. These advantages will enable 3D-TOF detection technology to have great development in aerospace applications. In this paper, a method for evaluating 3D-TOF camera ranging performance is studied. With this method, the performance test of 3D-TOF camera can be realized, and the influence of factors such as return light intensity and temperature on the measurement distance can be achieved. The application has high guiding significance.

With the development of sensor technologies, imaging technology is developing more rapidly. What followed was the widespread use of image processing technology in many kinds of applications. For instance, image processing technology has been widely used in video surveillance, medical diagnosis, remote sensing detection and object tracking. As a sub-field of image processing technology, image fusion is the one of most studied technology. The aim of image fusion is to acquire an integrated image that contains more information. This integrated image is more conductive for a human or a machine to understand and mine the information contained in the image. In all kinds of image fusion, infrared (IR) and visible (VIS) image fusion is one of the most valuable multisource image fusion. When imaging the same scene using both IR and VIS imaging system, more information can be obtained, but more redundant information is generated. The IR sensor acquires the thermal radiation information of the object in a scene, so the object can also be detected when the lighting conditions are poor. The image acquired by VIS light sensors has more spectral information, clearer texture details, and higher spatial resolution. Thus, the scene can be described more completely by integrating the IR and VIS images into one image. Meanwhile, the scene can be readily understood by observers, and the information of the scene can be easily perceived. In this paper, an effective IR and VIS image fusion via non-subsampled shearlet transform (NSST) and pulse-coupled neural network (PCNN) in multi-scale morphological gradient (MSMG) domain is proposed. First, low frequency sub-image and high frequency sub-images are obtained through NSST. Then, the low frequency sub-image and high frequency sub-images are fused via a MSMG domain PCNN (MSMG-PCNN) strategy. Finally, the fused image is reconstructed by inverse NSST. Experimental results demonstrate that the proposed MSMG-PCNN-NSST algorithm performs effectively in most cases by qualitative and quantitative evaluation.

In medical imaging, a number of imaging modalities have been used to visualize the structural information of different internal organs in the human body and some modalities can even visualize structures at a cellular level for diagnostic and treatment purposes. Optical resolution photoacoustic microscopy (OR-PAM) is one of the emerging imaging modalities to analyze the anatomy and functionality of tissues non-invasive. It is a hybrid imaging technology that combines photoacoustic (PA) contrast and acoustic resolution to reconstruct images of tissues in humans and animals. However, in OR-PAM the received ultrasonic signal by the thermal expansion of tissues has a low signal-to-noise ratio because most of the signal power is lost in the conversion process from light to acoustic waves which makes it difficult to visualize the structural information in the PA images. Traditional denoising methods such as wiener filter, bandpass filter, wavelet-based denoising, noise reduction by SVD and dictionary-based denoising methods can denoise PA images to some extent but it is still a difficult task to preserve structural information in the images by such methods. In this research, a convolutional autoencoder (CAE) based model is proposed to denoise and learn the structural patterns of blood vessels in PA images. To achieve this task, a CAE model is first trained between noisy and Gabor filtered sub-images, those contain the patterns of different vascular structures. Then, the trained model is used to approximate the denoise version of the input noisy sub-images of blood vessels. The proposed model is trained and tested on PA images of blood vessels of a mouse ear, acquired by the OR-PAM imaging system and the results show that our proposed method can effectively approximate and reconstruct the noisy vascular structures than traditional images denoising filtering methods.

This paper reports on line-field confocal optical coherence tomography (LC-OCT), a recently invented imaging technology now capable of generating either a horizontal (en face) section image at an adjustable depth, or a vertical section image (B-scan) at an adjustable lateral position. For both operating modes, images are acquired in real-time (10 frames/second), with real-time control of the depth and lateral positions. Using a supercontinuum laser as a broadband light source and a high numerical microscope objective, an isotropic spatial resolution of ∼ 1 μm is achieved. The imaging fields of view are 1.2×0.5 mm² (x×y, horizontal) and 1.2×0.5 mm² (x×z, vertical). LC-OCT has been used in dermatology for skin imaging.

X-ray photon counting is a novel imaging technology gaining increasing academic and industrial interest due to its potential for high-resolution computed tomography (CT) and spectral CT. These photon-counting detector systems could have numerous benefits in medical imaging because they provide a substantial increase in the amount of available raw imaging information (e.g., multi-energy information). However, new algorithms are needed for using this information to increase the accuracy of diagnostic decisions. In this work, we focused on addressing technical challenges to consider when designing and building a laboratory benchtop for photon counting CT systems. We developed a photon counting cone-beam CT system containing a photon counting detector with a small detective area based on a translate-rotate geometry and a step-and-shoot acquisition mode. To control and synchronize the CT system components, we developed a desktop computer program with a user-friendly graphical user interface. Furthermore, we established a fully automated method for estimating some of the parameters that describe the geometry of the cone-beam CT system. This method is based on an iterative optimization technique in which the reconstructed image of a small sphere is evaluated to find the required geometrical parameters for better image reconstruction. Finally, we used images reconstructed from different scans to confirm the methodology used in the study.

Image stitching technology is an important technology for generating large field of view images, and it is also a research hotspot in the field of digital image processing. Binocular imaging technology is closely related to image stitching technology and is widely used in robot vision, 3D reconstruction and other fields. However, in the existing image stitching methods, when the image with the distant view and the near object are coexisted, due to the parallax, the near object often has partial missing or unnatural transition, which leads to the poor image stitching effect and low naturalness. Aiming at this problem, we uses the characteristics of the binocular camera that can obtain depth information, and regards the information as one of the basis of the seam positioning. A strategy based on the combination of geometry, color and depth features is proposed, and we constructed a seaming algorithm model for positioning the optimal seam. The comparison experiment results show that the stitching naturalness is improved by 37% according to the proposed seaming algorithm.

3D imaging through a diffuser is a popular topic recently due to its extensive use, which is from earth observation to biomedical diagnosis. Although many methods have been proposed to realize this goal, such as holographic imaging and wavefront shaping, it is still complicated i.e. they require either a reference beam or sequential scanning to compensate the random phase. Here, we proposed a new verified approach without reference beam, just using a structured light illumination combined with time averaging technology to resolve the phase of the object. The result shows that this method is easy to get 3D image without speckle noise. Furthermore, it can also be used to detect the deformation of a hidden object and be extended to the biomedical imaging.

Motions inside living tissues are ubiquitous signatures of the active processes involved in the maintenance of cellular function and health. An innovative label-free non-invasive imaging technology called Biodynamic Imaging (BDI), based on low-coherence interferometry and digital holography, captures intracellular dynamics through ultra-low-frequency Doppler spectra encoding a broad range of cellular and subcellular motions. Clinical trials of BDI assessing patient response to therapy are underway in human ovarian, esophageal and breast cancers.

Photoacoustic imaging (PAI) is an exciting emerging modality with many potential clinical and preclinical applications. Due to its nascent status, at present, no standards exist to define system performance or establish well-validated, consensus-based performance test methods. The International Photoacoustic Standardisation Consortium (IPASC) is a community-led organization that seeks to reach consensus on PAI standardisation to improve the quality of preclinical studies and accelerate clinical translation. Current IPASC activities include: phantom development; establishment of standard terminology and image quality metrics; and open-source data file format development. These activities aim to help PAI become a mature, widely-adopted imaging technology.