Virtual optical-resolution photoacoustic microscopy using the k-Wave method

Abstract

Deep learning has been widely used in image processing, quantitative analysis, and other applications in optical-resolution photoacoustic microscopy (OR-PAM). It requires a large amount of photoacoustic data for training and testing. However, due to the complex structure, high cost, slow imaging speed, and other factors of OR-PAM, it is difficult to obtain enough data required by deep learning, which limits the research of deep learning in OR-PAM to a certain extent. To solve this problem, a virtual OR-PAM based on k-Wave is proposed. The virtual photoacoustic microscopy mainly includes the setting of excitation light source and ultrasonic probe, scanning and signal processing, which can realize the common Gaussian-beam and Bessel-beam OR-PAMs. The system performance (lateral resolution, axial resolution, and depth of field) was tested by imaging a vertically tilted fiber, and the effectiveness and feasibility of the virtual simulation platform were verified by 3D imaging of the virtual vascular network. The ability to the generation of the dataset for deep learning was also verified. The construction of the virtual OR-PAM can promote the research of OR-PAM and the application of deep learning in OR-PAM.

High‐resolution volumetric information fusion for depth of field enhancement in photoacoustic microscopy

“…The DoF of OR‐PAM can be doubled without sacrificing lateral resolution. In this work, the virtual OR‐PAM [17] was used to obtain the multi‐focus 3D photoacoustic dataset for volumetric information fusion. The virtual OR‐PAM is based on k‐wave, a commonly used simulation tool for photoacoustic imaging, which can realize the conventional Gaussian‐beam OR‐PAM through the setting of light source and ultrasonic probe, two‐dimensional raster scanning and signal processing.…”

“…As the block size significantly influences the fusion, we updated the size of the blocks through DE algorithm to adaptively obtain the optimized block size. As shown in Figure 1A, the multi‐focus photoacoustic data P 1 and P 2 with the size of H × W × L were obtained through the virtual OR‐PAM [17]. P 1 and P 2 were decomposed into eight wavelet coefficients LLL, LLH, LHL, LHH, HHL, HHH, HLH, and HLL (where H represents high‐frequency filtering, L represents low‐frequency filtering) using 3D‐SWT, respectively.…”

“…A virtual Gaussian‐beam photoacoustic microscopy [17] based on k‐wave was adopted to obtain multi‐focus 3D photoacoustic data in our experiment. The Gaussian beam in the virtual OR‐PAM platform is generated by an objective lens with a numerical aperture of NA = 0.14, and the light wavelength was set as 532 nm.…”

A novel volumetric fusion algorithm for optical-resolution photoacoustic microscopy based on 3D-SWT and joint weighted evaluation optimization

“…The blocks detected as focused were preserved in the fused coefficients thereafter and large-volumetric and high-resolution photoacoustic data can be achieved by inverse stationary wavelet transform. As shown in Figure 1, the multi-focus photoacoustic data P1 and P2 with size of H×W×L were obtained through the virtual OR-PAM [9] . P1 and P2 were decomposed into 8 wavelet coefficients LLL, LLH, LHL, LHH, HHL, HHH, HLH and HLL (where H represents high-frequency filtering, L represents lowfrequency filtering) using 3D-SWT, respectively.…”

A graphical digital processing platform for photoacoustic signal based on GUI

“…In previous works, a photoacoustic microscopy imaging simulation platform based on k-Wave simulation toolbox was built to solve the problem of small depth of field in traditional optical microscopy imaging system, where Gaussian beam was used to stimulate the initial photoacoustic signal [7,8] . However, the complexity of the imaging platform makes it difficult to adjust parameters intuitively and conveniently.…”