In recent years, photoacoustic imaging technology has developed rapidly and has become one of the most important technologies in the field of biomedical imaging. Photoacoustic imaging combines the characteristics of high contrast of optical imaging and strong penetrating power of acoustic imaging. It can obtain tissue imaging with high resolution and can also meet the requirements of quantitative analysis of changes in tissue function and physiological parameters at the same time. So, photoacoustic imaging plays an important role in disease prevention and cancer diagnosis. The traditional information acquisition of photoacoustic imaging is based on Nyquist sampling law (the sampling frequency must be greater than twice the highest signal frequency). This method will waste a lot of sampling resources in photoacoustic imaging with a large amount of data and put forward higher requirements for equipment. In order to break through the limitation of Nyquist sampling law, compressed sensing theory is used to compress and sample the signal. Then the original photoacoustic image is reconstructed by sparse key data. In this paper, Compressive Sampling Matching Pursuit (CoSaMP) is used as the reconstruction algorithm. And the compressed sensing photoacoustic imaging platform is built by K-Wave toolbox (photoacoustic imaging platform tool) of MATLAB simulation software together with the reconstruction algorithm to reconstruct the sparse photoacoustic signals observed. The qualitative and quantitative analysis is carried out on the reconstructed images. Results shows that the reconstruction effect meets the requirements, which verifies the superiority of compressed sensing theory and the reliability and advancement of compressed sensing photoacoustic imaging platform.
Photoacoustic imaging technology is a three-dimensional imaging method based on the photoacoustic effect, which has the characteristics of high resolution, high contrast and high penetration depth. This technology provides a very important means to study the structure, metabolism, physiological and pathological characteristics of biological tissue, so photoacoustic imaging has a great application prospect in biomedicine. However, because of the high-resolution Nyquist sampling rate and large amount of data, it will cause great pressure on the storage equipment and make data transmission difficult. Generally, the problem of large amount of data is solved by compressed sensing. Compressed sensing theory can make the sampling speed determined by the internal structure and content of the signal, rather than by the bandwidth, thus reducing the amount of data. Build the virtual simulation platform of photoacoustic imaging based on k-wave, and use the BP reconstruction algorithm of compressed sensing to restore the original image. The results show that it can restore the original image with high quality while reducing the amount of data.
Photoacoustic tomography technology is a new imaging technology based on photoacoustic effect. It has become one of the most important techniques in biomedical imaging field because of its non-invasive, non-ionized and high resolution. The imaging data of photoacoustic imaging is complex. The traditional Nyquist sampling consumes much time and resources. And it requires high equipment. In order to improve the sampling efficiency and reduce the equipment requirements, Compression Sensing (CS) theory has been used to collect photoacoustic data. Compressed Sensing theory can break through the limitation of Nyquist sampling law and reduce the data redundancy greatly so that the desired imaging results can be reconstructed with less time and resources. In this paper, the K-wave simulation toolbox of MATLAB is used to set up the virtual photoacoustic field and collect the photoacoustic signal of blood vessel. The results show that the MATLAB virtual Compressed Sensing photoacoustic tomography simulation platform based on k-wave can achieve high quality photoacoustic tomography with less data. The superiority of Compressed Sensing theory and the high efficiency and stability of k-wave virtual platform are verified. Also, the Compressed Sensing reconstruction algorithm OMP and ROMP are compared in this paper and the result shows that the ROMP algorithm works better.
Photoacoustic imaging is a new biomedical imaging technology developed in recent years. It has the advantages of high resolution and sensitivity to the functional characteristics of biological tissues. However, whether for photoacoustic tomography or photoacoustic microscopy imaging, the imaging resolution depends on the frequency and bandwidth of the received ultrasonic signal. This leads to a large amount of data being collected under the Nyquist’s law. So storage medium and DSP processor are under unprecedented pressure. The problem of large amount of data is usually solved by using compression. Therefore, the combination of compressed sensing theory and photoacoustic imaging can not only restore images with high quality, but also reduce the amount of data as much as possible. It saves storage space and the time of further data processing. This paper introduces signal sparsity and measurement matrix of compressed sensing theory briefly. The virtual photoacoustic imaging of blood vessels is carried out in the simulation environment constructed by the k-wave toolbox of MATLAB. The collected data are restored by using the gradient projection for sparse reconstruction. The results show that high quality photoacoustic imaging images can be reconstructed while a small amount of data is stored. The performance of the simulation platform is verified. And it is of great significance to solve the problem of high data volume of photoacoustic imaging.
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.
As a new non-destructive biomedical imaging modality, photoacoustic imaging not only has high contrast of optical imaging but also has high penetration depth of ultrasonic imaging. And it has developed rapidly in recent years and has been widely used in the fields of biomedical clinical diagnosis and volume imaging, attracted the eager attention of more and more researchers in the biomedical field. In biomedicine, image reconstruction needs to process the huge amount of information obtained. How to compress the data without distortion in this process has become an important research topic. In this paper, based on photoacoustic imaging technology and compression sensing reconstruction algorithm, a virtual simulation platform for compression sensing photoacoustic tomography is constructed by using k-wave simulation toolbox. Through this platform, a simulation model of photoacoustic propagation was established, we analyzed the photoacoustic signal generated by the simulation model. Finally, image reconstruction is completed by using compression sensing reconstruction algorithm. Then, in order to test the performance of the platform, we reconstructed part of the blood vessel network image based on the simulation platform. The results show that the virtual simulation platform successfully realizes the compressed sensing photoacoustic tomography with small amount of data but high reconstruction quality, which has practical significance and theoretical value for the research of the application of compress sensing in photoacoustic imaging.
In recent years, photoacoustic imaging, an emerging nondestructive biomedical imaging technology, has shown great potential for early diagnosis of diseases with its advantages of highly sensitive optical contrast and high resolution. It is a hard project to collect a large number of pathological medical images by using photoacoustic imaging. How to compress large amounts of data, rapid transmission and storage of important value information has become an urgent problem to be solved. In this paper, build a virtual simulation platform for compressed sensing photoacoustic tomography by combining compressed sensing reconstruction algorithms with photoacoustic imaging based on the k-wave simulation toolbox. On the one hand, compressed sensing can reduce sample rates, accelerated the speed of imaging. On the other hand, it can modify the demands for hardware devices and facilitate to transmit and store of data. The k-wave simulation toolbox is used to build simulation models for simulating the propagation of photoacoustic fields, recording of photoacoustic signals, and image reconstruction. We validated the performance of the simulation platform by imaging the vascular network. The results show that the virtual simulation platform compressed sensing photoacoustic tomography can achieve high-quality photoacoustic imaging with less data. The virtual platform can provide theoretical guidance for the application of compressed sensing in photoacoustic imaging.
Ultrasonic transducer is a sensor that realizes the mutual conversion of ultrasonic and electrical signals, and it is widely used in quality inspection, biomedical imaging and other fields. Commonly used ultrasonic transducers have a small detection range and low sensitivity due to the diffraction of sound waves. Focused transducers are used to improve detection sensitivity. Unfortunately, focused transducers have narrow depth of field. Here, we developed a Bessel ultrasonic transducer for large depth of field by using conical acoustic lens. An acoustic lens is attached to a unfocused ultrasonic. And the acoustic lens is a cuboid prism with a concave cone on the bottom, made of fused silica. Similar to an axicon that can generate a Bessel beam, the Bessel ultrasonic transducer can produce nondiffracting Bessel ultrasonic beams. Therefore, extended depth of field with uniformly high resolution and high detection sensitivity can be obtained. We used COMSOL to simulate the transmission of ultrasonic field of the designed conical acoustic lens, and compare it with the spherical focused ultrasonic transducer. The results show that the depth of field of the Bessel ultrasonic transducer is about 8 times that of the conventional spherical focused ultrasonic transducer. And the depth of field of the Bessel ultrasonic transducer can be further adjusted by adjusting the cone angle of the conical acoustic lens. The Bessel ultrasonic transducer will help improve the capabilities of the ultrasound probe and expand its application range. For example, an ultrasonic probe with a large depth of field will expand the imaging depth of photoacoustic microscopy and enhance its ability in non-destructive testing.
In recent years, photoacoustic imaging, an emerging nondestructive biomedical imaging technology, has shown great potential for early diagnosis of diseases with its advantages of highly sensitive optical contrast and high resolution. It is a hard project to collect a large number of pathological medical images by using photoacoustic imaging. How to compress large amounts of data, rapid transmission and storage of important value information has become an urgent problem to be solved. In this paper, build a virtual simulation platform for compressed sensing photoacoustic tomography by combining compressed sensing reconstruction algorithms with photoacoustic imaging based on the k-wave simulation toolbox. On the one hand, compressed sensing can reduce sample rates, accelerated the speed of imaging. On the other hand, it can modify the demands for hardware devices and facilitate to transmit and store of data. The k-wave simulation toolbox is used to build simulation models for simulating the propagation of photoacoustic fields, recording of photoacoustic signals, and image reconstruction. We validated the performance of the simulation platform by imaging the vascular network. The results show that the virtual simulation platform compressed sensing photoacoustic tomography can achieve high-quality photoacoustic imaging with less data. The virtual platform can provide theoretical guidance for the application of compressed sensing in photoacoustic imaging.
The study of the relationship between the spectral characteristics of the photoacoustic signal and the size, density and shape of the absorber is of great significance to image reconstruction and is of great practical significance to the better application of photoacoustic imaging in the medical field. This paper uses the simulation software COMSOL Multiphysics to design a two-dimensional simulation model based on finite element to study the relationship between the spectral characteristics of the photoacoustic signal and the properties of the absorber. In this study, the model consists of three parts: 1) water layer; 2) short pulse laser source (wavelength of 840nm); 3) gastric tumor tissue. The laser point source is located in the middle of the upper water layer. By solving the diffusion equation and the biological heat equation, respectively, the propagation of light in the water layer and the temperature change in biological tissues are simulated. When the absorber is irradiated by a Gaussian pulsed laser, due to the extremely short time, the absorber can be regarded as adiabatic expansion after absorbing energy, thereby generating ultrasonic waves. Using the finite element analysis method, the complex situation of photoacoustic imaging is transformed into the coupling of multiple physical fields, and the photoacoustic signal is obtained by numerical calculation of partial differential equations. Analyzing the simulation results, it is found that the spectral characteristics of the photoacoustic signal are closely related to the size of the absorber. The relationship obtained in this simulation experiment is: the spectral intercept and the size of the absorber have a power function relationship, the larger the size, the larger the spectral intercept , And the growth rate increases with the increase of the size; the spectral slope and the size of the absorber also have a power function relationship, the larger the size, the smaller the spectral slope, and the rate of change of the slope decreases with the increase of the size. For the relationship between the spectral characteristics and density of the photoacoustic signal, the intercept and slope characteristics are opposite. As the density of the photoacoustic absorber increases, the spectral slope of the photoacoustic signal increases, and the spectral intercept of the photoacoustic signal decreases. The photoacoustic signals and spectrograms of different shapes of absorbers have their own characteristics. The research results in this paper can promote the research of photoacoustic imaging and can better apply photoacoustic imaging in the medical field.
Human female breast is composed of skin, fibrous tissue, breast glands and fat. Breast cancer is a malignant tumor that occurs in the epithelial tissue of breast glands. The breast is not an important organ for maintaining human life. Breast cancer in situ is not fatal; however, because breast cancer cells lose the characteristics of normal cells, the connections between cells are loose and easy to fall off. Once the cancer cells fall off, the free cancer cells can spread throughout the body with the blood or lymph fluid, forming metastases, and endangering life. Breast cancer has become a common tumor threatening women's physical and mental health. Therefore, studying the interaction between laser and breast tissue and breast tumors has important theoretical and practical significance for the treatment of breast cancer. To this end, this research uses the commercial finite element simulation software COMSOL Multiphysics to develop a two-dimensional numerical simulation model based on finite element, which studies the propagation and heat transfer of light in the breast of breast cancer patients. In this study, the model consists of four parts: 1) water layer; 2) breast; 3) breast tumor; 4) short pulse laser source (wavelength is 840nm). The laser point source is located in the middle of the water layer above the breast tissue to irradiate the breast and tumor. Simulate the propagation of light in the breast and tumor by solving the diffusion equation. The temperature changes of breast tissue and breast tumors are obtained by solving the biological heat transfer equation. This research helps to understand the spread of light in human breasts and breast tumors and the interaction between the two, and has certain theoretical guiding significance for the research and treatment of breast cancer.
The study of the relationship between the spectral characteristics of the photoacoustic signal and the shape and size of the absorber has important practical significance for image reconstruction. Using the commercial finite element simulation software COMSOL Multiphysics, a two-dimensional simulation model based on finite element was designed, which studied the relationship between the spectral characteristics of the photoacoustic signal and the shape and size of the absorber. In this study, the model consists of three parts: 1) water layer; 2) short pulse laser source (wavelength of 840nm); 3) gastric tumor tissue. The laser point source is located in the middle of the upper water layer. Simulate the propagation of light in the water layer by solving the diffusion equation. The temperature changes in biological tissues are obtained by solving the biothermal equation. When the absorber is irradiated by Gaussian pulses, due to the extremely short time, the absorber can be regarded as adiabatic expansion after absorbing energy, thereby generating ultrasonic waves. Using the finite element analysis method, the complex situation of photoacoustic imaging is transformed into the coupling of multiple physical fields and the numerical calculation of partial differential equations to obtain the photoacoustic signal. Fitting the simulation results shows that the spectral characteristics of the photoacoustic signal change regularly with the size of the absorber. The size of the absorber obtained in this paper has a power function relationship with the spectral intercept. The larger the size, the larger the spectral intercept, and the growth rate increases with the increase of the size. The size of the absorber and the spectral slope also have a power function relationship. The slope of the large spectrum is smaller, and the rate of change of the slope decreases as the size increases. At the same time, analyzing the photoacoustic spectrum of absorbers of different shapes also shows that absorbers of different shapes have their own characteristics. This research is helpful to understand the relationship between spectral characteristics and the shape and size of the absorber, and has certain theoretical guiding significance for image reconstruction.
Human female mammary gland is by skin, fibrous tissue, mammary gland and adipose composition, breast cancer is the malignant tumor that occurs in mammary gland epithelial tissue. Mammary gland is not an important organ to maintain human life activities. Breast cancer in situ is not fatal. However, due to the loss of the characteristics of normal cells, the cells are loosely connected and easy to fall off. Once cancer cells are shed, free cancer cells can spread throughout the body with blood or lymph, forming metastases and endangering life. At present, breast cancer has become a common tumor threatening women's physical and mental health. Therefore, studying the interaction of laser with breast tissue and breast tumor has important theoretical and practical significance for the treatment of breast cancer. For this reason, this study developed a two-dimensional numerical simulation model based on finite element using COMSOL Multiphysics, a commercial finite element simulation software, which studied the transmission and heat transfer of light in breast cancer patients. In this study, the model consists of four parts: 1) Water layer; 2) Breast; 3) Breast tumor; 4) short pulse laser source (wavelength: 840nm). The laser point source is located in the middle of the water layer above the breast tissue to irradiate the breast and the tumor. The propagation of light in breast and tumor was simulated by solving the diffusion equation. The temperature changes of breast tissue and breast tumor were obtained by solving the biological heat transfer equation. This study is helpful to understand the transmission of light in human breast and breast tumor as well as the interaction between the two, and has certain theoretical guiding significance for the research and treatment of breast cancer.
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.
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