As a functional imaging technique, photoacoustic imaging has the ability to produce high contrast and high resolution image. However, photoacoustic microscopy shows the disadvantage of limited depth of field, due to the strong focusing process. Only some part of the whole image is clear while some other information might be lost or misleading. To achieve a large volumetric and high resolution photoacoustic, we propose a depth-of-field expansion motivated multi-focus photoacoustic microscopy image fusion algorithm which fuses the information from imaging of different focal positions into single one to obtain all-in-focus image. To achieve this goal, the volumes obtained at different focus positions are sliced into 2D and the 2D image fusion based on cross bilateral filter is performed for correspond slices. Finally, 3D reconstruction can be performed on these fused slices. Experiment results show that our method can produce a 3D fusion result which maintains useful photoacoustic signal information for further analysis and visualization.
Photoacoustic microscopy suffers from limited depth-of-field due to the strong focus of laser beam, which implies that only some part of the imaging result is clear. Such shortage limitsthe further application of this powerful imaging technique, which is the problem we hope to address. In this work, we consider to solve the target problem through information fusion method. By fusing multi-focus 3D photoacoustic images, a large volumetric and high resolution photoacoustic microscopy can be obtained. However, the task of fusing photoacoustic signal is different from general 2-D multi-focus image fusion problem. The core challenge for our work is the fusion of different 3D photoacoustic volumes. We simplify the task as 2D problem, which is achieved by slicing the 3D data to 2D and reconstruction of the 2D slices. We propose a 3D fusion method which involves proper data preprocessing, slicing of the 3D data, fusion of slices and 3D reconstruction. Experiment results verify that the fused data shows larger depth of field and contains more useful information, which supports our thought of extending depth-of-field through information fusion method. To further demonstrate the superior of deep-learning (DL) method, a few non-DL 2D algorithms are selected for a comparison study based on objective assessments to show the generalization ability of convolutional neural network based image fusion algorithm.
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(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.
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.
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