SignificancePhotoacoustic imaging is an emerging imaging modality that combines the high contrast of optical imaging and the high penetration of acoustic imaging. However, the strong focusing of the laser beam in optical-resolution photoacoustic microscopy (OR-PAM) leads to a limited depth-of-field (DoF).AimHere, a volumetric photoacoustic information fusion method was proposed to achieve large volumetric photoacoustic imaging at low cost.ApproachFirst, the initial decision map was built through the focus detection based on the proposed three-dimensional Laplacian operator. Majority filter-based consistency verification and Gaussian filter-based map smoothing were then utilized to generate the final decision map for the construction of photoacoustic imaging with extended DoF.ResultsThe performance of the proposed method was tested to show that our method can expand the limited DoF by a factor of 1.7 without the sacrifice of lateral resolution. Four sets of multi-focus vessel data at different noise levels were fused to verify the effectiveness and robustness of the proposed method.ConclusionsThe proposed method can efficiently extend the DoF of OR-PAM under different noise levels.
Sparse reconstruction in photoacoustic tomography has always faced the problem of artifacts. To address this issue, a diffusion model-based method for sparse data reconstruction in photoacoustic tomography was proposed. During the training phase, the gradient of the probability density of the image was learned as the data prior by adding noise and denoising at each step. During the testing phase, ultrasonic signals are generated by illuminating with pulsed laser and acquired by ultrasonic transducers surrounding the object, which was implemented using the k-Wave toolbox. The reconstructed image was finally obtained by reserve-time Stochastic Differential Equation (SDE). Experimental results on vascular data show that the proposed algorithm can effectively remove artifacts and improve image quality compared with conventional reconstruction methods under 32 and 64 detectors, respectively.
Artifact in photoacoustic tomography is always an issue to be solved. Here, a deep learning based physical model method to remove artifact for limited-data photoacoustic tomography was proposed, termed as PD net. A virtual photoacoustic tomography platform was constructed based on k-Wave, and the dataset required for deep learning was obtained using this virtual platform. The U-Net was used to build a deep learning network to remove artifacts in sparse-view and limited-view photoacoustic tomography. Under sparsity condition, when the number of ultrasonic transducers is 64, the improvement rates of SSIM and PSNR of the network are 274% and 66.34%, respectively, compared with the input of the network, which verifies that this method can remove artifacts in sparse-view photoacoustic tomography. The proposed method can reduce artifacts and enhance anatomical contrast when the number of ultrasonic transducers used is limited, and effectively reduce manufacturing costs of photoacoustic tomography.
Phase technology is widely utilized in the field of optics. By applying phase technology, the required pattern can be obtained by remodeling the light field in the focal area of the objective lens, which has significant value in laser manufacturing, biomedicine and optical imaging. Gerchberg-Saxton algorithm is commonly used in imaging systems to restructure the light field, which is achieved by converting light intensity distribution of the Fourier plane optical field into the phase distribution on the focal plane through the inverse Fourier transform. Nevertheless, for a high numerical aperture objective lens, the accuracy of the relationship between the phase and the intensity of the light field may be compromised by depolarization effects, which causes the Fourier transform unable to accurately generate the required lattice pattern from the known light intensity distribution. To obtain the intensity of the light field and phase information during the optical transmission process from the rear focal plane to the front focal plane of the objective lens, we utilize the Debye diffraction in place of the Fourier transform in the Gerchberg-Saxton algorithm. Image skeletonization is a morphology-based image processing technology used to extract the backbone structure and shape information in the image, which extracts the main structure of the image and generates a more simplified representation by eliminating redundant information in the image. Image skeletonization technology has applications in many fields, including computer vision and medical image processing, among others. In this paper, we demonstrated the generation of lattice patterns from arbitrary images in the strong focusing of light field using Debye diffraction theory and image skeletonization technology.
Photoacoustic imaging technology is an emerging functional imaging method in the field of biomedical applications. It combines the light absorption characteristics of tissues and ultrasonic detection, and has the advantages of strong contrast, high sensitivity, and deep imaging depth. Light absorption and heat transfer are important processes of photoacoustic effect. Therefore, this article uses the finite element software COMSOL Multiphysics to study the photothermal effect of the interaction between laser pulses and mouse brain tissue. In COMSOL, a laser with a pulse width of 5ns is used as the excitation light source. The coefficient form partial differential equation module, the biological heat transfer module, etc. are used to simulate the light transmission and light-to-heat conversion process in the photoacoustic imaging of rat brain tissue. From this we can explore the photothermal effect produced when the laser interacts with biological tissues.
Photoacoustic tomography technology is a new non-invasive, non-ionizing biomedical imaging method. This technology combines the high contrast of optical imaging and the high-resolution characteristics of ultrasound imaging, which can obtain high-resolution images in deeper tissues. In recent years, it has developed rapidly and won widespread attention. Traditional sampling method must follow the Nyquist sampling theorem, which wastes a lot of sensing time and storage space. In order to improve the sampling efficiency, compressed sensing (CS) theory is used to collect and process photoacoustic data. The advantage of CS theory is that it can combine data acquisition and data compression. So that only the sparse features of the original signal need to be collected, and a high-quality original target image can be successfully reconstructed with very few samples, which greatly reduces data redundancy. More than that, the requirements for equipment are reduced. This paper uses MATLAB's k-wave simulation toolbox to establish a virtual photoacoustic field, collect the photoacoustic signals of biological tissues, and reconstruct the image through the segmented weak orthogonal matching pursuit (StOMP) algorithm. The results show that the MATLAB virtual compressed sensing photoacoustic tomography simulation platform based on k-wave can realize high-quality photoacoustic tomography with less data. The superiority of the compressed sensing theory and the efficiency of the k-wave virtual platform are verified.
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