Time Reversal Reconstruction Algorithm Based on PSO Optimized SVM Interpolation for Photoacoustic Imaging

Mingjian Sun,Ting Liu,Guanqun Wu,Zhenghua Wu,Naizhang Feng,Jiajun Hu

doi:10.1155/2015/795092

Mingjian Sun, Ting Liu + Show 4 more

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https://doi.org/10.1155/2015/795092

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Abstract

Photoacoustic imaging is an innovative imaging technique to image biomedical tissues. The time reversal reconstruction algorithm in which a numerical model of the acoustic forward problem is run backwards in time is widely used. In the paper, a time reversal reconstruction algorithm based on particle swarm optimization (PSO) optimized support vector machine (SVM) interpolation method is proposed for photoacoustics imaging. Numerical results show that the reconstructed images of the proposed algorithm are more accurate than those of the nearest neighbor interpolation, linear interpolation, and cubic convolution interpolation based time reversal algorithm, which can provide higher imaging quality by using significantly fewer measurement positions or scanning times.

Highlights

Photoacoustic tomography (PAT) has emerged as a powerful imaging technology for many biomedical applications [1, 2]
It shows that the reconstructed photoacoustic tomography (PAT) image has been significantly blurred due to the discontinuous boundary condition, and some artifacts have been caused by vestigial waves
The particle swarm optimization (PSO) optimized support vector machine (SVM) interpolation method is used in the time reversal reconstruction algorithm

Summary

Introduction

Photoacoustic tomography (PAT) has emerged as a powerful imaging technology for many biomedical applications [1, 2]. PAT is able to provide functional imaging of physiological parameters. Photoacoustic imaging combines electromagnetic and ultrasonic wave synergistically, providing relatively deep speckle-free imaging with high electromagnetic contrast at high ultrasonic resolution. In PAT, the developing reconstruction methods have been an important task [3, 4]. Various reconstruction methods, such as filtered backprojection, Fourier transform, alternative algorithm, time reversal, inversion of the linear Radon transform, and delay and sum beamforming, have been developed under different assumptions and approximation [5,6,7,8,9]. Different shaped measurement surfaces will lead to different shaped final artifacts

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