Abstract

Accurate reconstruction of 3D photoacoustic (PA) images requires detection of photoacoustic signals from many angles. Several groups have adopted staring ultrasound arrays, but assessment of array performance has been limited. We previously reported on a method to calibrate a 3D PA tomography (PAT) staring array system and analyze system performance using singular value decomposition (SVD). The developed SVD metric, however, was impractical for large system matrices, which are typical of 3D PAT problems. The present study consisted of two main objectives. The first objective aimed to introduce the crosstalk matrix concept to the field of PAT for system design. Figures-of-merit utilized in this study were root mean square error, peak signal-to-noise ratio, mean absolute error, and a three dimensional structural similarity index, which were derived between the normalized spatial crosstalk matrix and the identity matrix. The applicability of this approach for 3D PAT was validated by observing the response of the figures-of-merit in relation to well-understood PAT sampling characteristics (i.e. spatial and temporal sampling rate). The second objective aimed to utilize the figures-of-merit to characterize and improve the performance of a near-spherical staring array design. Transducer arrangement, array radius, and array angular coverage were the design parameters examined. We observed that the performance of a 129-element staring transducer array for 3D PAT could be improved by selection of optimal values of the design parameters. The results suggested that this formulation could be used to objectively characterize 3D PAT system performance and would enable the development of efficient strategies for system design optimization.

Highlights

  • BackgroundPhotoacoustic tomography (PAT) is a hybrid imaging modality that combines the high spatial resolution of ultrasound (US) imaging with the high-contrast of optical imaging [1]

  • The figures of merit (FoM) used in this work were root mean square error (RMSE), peak signal-to-noise ratio (PSNR), mean absolute error (MAE) and 3D-SSIM3D-structural similarity (SSIM)

  • The response of the FoM matched these trends, suggesting they were useful for characterizing 3D PA tomography (PAT) system designs

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Summary

Introduction

Photoacoustic tomography (PAT) is a hybrid imaging modality that combines the high spatial resolution of ultrasound (US) imaging with the high-contrast of optical imaging [1]. It is based on the detection of pressure waves generated by an object when exposed to laser light pulsed on the nanosecond timescale [2]. Since the optical properties between tissue types generally differ to a greater degree than their mechanical and elastic properties, PAT can provide improved tissue delineation and specificity when compared to imaging techniques that use US or photon propagation alone [4] Objects such as human arm vasculature [5], whole-body small animal vasculature [6], human breast cancer [7,8], brain in small animals [9,10], and many others have been successfully visualized by PAT

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