Three-dimensional photoacoustic tomography based on graphics-processing-unit-accelerated finite element method

Abstract

Compared with commonly used analytical reconstruction methods, the frequency-domain finite element method (FEM) based approach has proven to be an accurate and flexible algorithm for photoacoustic tomography. However, the FEM-based algorithm is computationally demanding, especially for three-dimensional cases. To enhance the algorithm's efficiency, in this work a parallel computational strategy is implemented in the framework of the FEM-based reconstruction algorithm using a graphic-processing-unit parallel frame named the "compute unified device architecture." A series of simulation experiments is carried out to test the accuracy and accelerating effect of the improved method. The results obtained indicate that the parallel calculation does not change the accuracy of the reconstruction algorithm, while its computational cost is significantly reduced by a factor of 38.9 with a GTX 580 graphics card using the improved method.

Semi-quantitative Multispectral Optoacoustic Tomography (MSOT) for volumetric PK imaging of gastric emptying

“…The presented work uses the photoacoustic effect to generate sound waves, which are far less subject to scattering in tissue. While some approaches exist that exploit this effect using microwave excitation in the near-field [18–20] or amplitude modulated light sources to employ optoacoustic imaging in the frequency domain [21,22] , time domain optoacoustic imaging is the most commonly used implementation. With existing implementations in photoacoustic microscopy [23–26] and high-resolution tomography using frequencies above 10 MHz [27–29] , this paper applies lower detection frequencies for whole body optoacoustic tomography [30–32] .…”

Parallel Computing for Quantitative Blood Flow Imaging in Photoacoustic Microscopy

“…Parallel computing based on GPU is proven effective to improve computational efficiency and is extensively adopted in PAT imaging systems, for which the image reconstruction algorithm is usually designed with a high computation complexity to obtain a better image quality [19][20][21][22][23]. In PAM system, GPU has been applied for the real-time structure imaging (MAP) of blood vessels in a mouse's ear [24,25].…”

“…Computer unified device architecture (CUDA) for NVIDIA GPU facilitates the programming and makes GPU one of the most popular acceleration hardware. With these advantages, GPU has been widely adopted to accelerate computation in photoacoustic imaging system, especially in PAT [19][20][21][22][23] for which the image reconstruction algorithm is much more complicated than PAM and needs high performance computation. Currently, several parallel computing methods with GPU have been implemented to reconstruct images in PAT systems such as back-projection (BP)-based PAT [19], finite element method (FEM)-based time-domain quantitative PAT [21] and double-state delay-multiply-and-sum (DS-DMAS)-based PAT [23].…”

Real-time GPU-accelerated processing and volumetric display for wide-field laser-scanning optical-resolution photoacoustic microscopy

“…The imaging speed in OR-PAM depends on factors such as the pulse repetition rate of the laser, scanning methods, field of view (FOV), and signal processing time. The imaging speed in the first generation OR-PAM was relatively slow because of the mechanical scanning involved and the low pulse repetition rate (10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20) of the laser utilized. The higher acquisition speed can minimize motion artifacts, reduce anesthetic duration for animal experiments, and facilitate quantitative analysis of 3D and 4D image data.…”