Full Matrix Wavefield Migration for Layered Photoacoustic Imaging

IF 4.2 2区 计算机科学 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC
Kang Qin;Meng Cao;Peng Ren;Fengchen Luo;Siyu Liu
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引用次数: 0

Abstract

Medium heterogeneity poses a severe challenge to image reconstruction in transcranial photoacoustic tomography, which cannot be fully addressed by the homogeneous phase shift migration method. Although the existing methods can enhancethe imaging quality to a certain extent, they are limited by the large approximation errors and low computational efficiency. To further improve imaging performance and calculation speed, this paper proposes full matrix wavefield migration, which takes into account both lateral and longitudinal variations of speed of sound (SOS). Unlike the PSM method which relies on a layer-by-layer migration framework, the proposed approach reformulates the SOS map across the propagation medium into a spatial matrix of SOS. By means of extrapolating wavefield data in the wavenumber domain and correcting phase deviations in the spatial domain, this method reduces the image distortion caused by SOS irregularity and suppresses artifacts in reconstructed images. Moreover, the calculation process is further optimized to eliminate redundancy. Simulation and experimental results demonstrate that full matrix wavefield migration method improves lateral resolution (up to 21.24%) and computational efficiency (about 19.84%) compared to the previous methods.
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来源期刊
IEEE Transactions on Computational Imaging
IEEE Transactions on Computational Imaging Mathematics-Computational Mathematics
CiteScore
8.20
自引率
7.40%
发文量
59
期刊介绍: The IEEE Transactions on Computational Imaging will publish articles where computation plays an integral role in the image formation process. Papers will cover all areas of computational imaging ranging from fundamental theoretical methods to the latest innovative computational imaging system designs. Topics of interest will include advanced algorithms and mathematical techniques, model-based data inversion, methods for image and signal recovery from sparse and incomplete data, techniques for non-traditional sensing of image data, methods for dynamic information acquisition and extraction from imaging sensors, software and hardware for efficient computation in imaging systems, and highly novel imaging system design.
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