Photoacoustic tomography is a new hybrid imaging technology, which combines the high contrast of optics imaging with the high penetration of acoustics imaging, moreover, which provides deep imaging of biological tissue. At present, photoacoustic tomography has been widely applied in biomedical research, such as brain imaging, cancer diagnosis, and vascular imaging. However, constructing a photoacoustic imaging system is costly and complex. In this study, a simulation platform for photoacoustic tomography was established based on the k-wave toolbox. Using this virtual photoacoustic tomography platform, the process of ultrasonic signal generation is simulated, the signals are collected by the same number of ultrasonic transducers at different center frequencies, and multiple groups of reconstructed images are obtained using the back projection algorithm. The relationship between the center frequency and the quality of the reconstructed image is analyzed to show that when an appropriate center frequency is selected, the ultrasonic transducer can effectively receive the photoacoustic signal; when the selected center frequency is too large or too small, the ultrasonic transducer may not effectively receive the photoacoustic signal, leading to the loss of some detailed features of the original image, which can result in an increase in the number of artifacts. Therefore, by selecting an appropriate center frequency of ultrasonic transducers, the quality of reconstructed images can be effectively improved.
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.
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 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.
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 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.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.