Abstract

We demonstrate the use of task-based image-quality metrics to compare various photoacoustic image-reconstruction algorithms, including a method based on the pseudoinverse of the system matrix, simple backprojection, filtered backprojection, and a method based on the Fourier transform. We use a three-dimensional forward model with a linear transducer array to simulate a photoacoustic imaging system. The reconstructed images correspond with two-dimensional slices of the object and are 128×128 pixels. In order to compare the algorithms, we use channelized Hotelling observers that predict the detection ability of human observers. We use two sets of channels: constant Q and difference of Gaussian spatial frequency channels. We look at three tasks, identification of a point source in a uniform background, identification of a 0.5-mm cube in a uniform background, and identification of a point source in a lumpy background. For the lumpy background task, which is the most realistic of the tasks, the method based on the pseudoinverse performs best according to both sets of channels.

Highlights

  • Photoacoustic imaging is based on the detection of acoustic waves that are generated by thermoelastic expansion induced by optical absorption of laser light

  • The majority of photoacoustic imaging systems can be classified as either microscopy systems or computed tomography (CT) systems.[1]

  • We looked at various metrics such as resolution, streak length, and ideal observer signal-to-noise ration (SNR) for a point source object; these metrics can be misleading

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Summary

Introduction

Photoacoustic imaging is based on the detection of acoustic waves that are generated by thermoelastic expansion induced by optical absorption of laser light. The regularization level still depends on the relative strength of the signal and noise, but it is possible to calculate a few pseudoinverse matrices based on different singular-value truncations, store these in memory, and decide in real time which one to use based on image quality. To assess the image quality of the reconstruction algorithms in this study, we use the CHO with two sets of channels that have been shown to predict the performance of human observers: constant Q11 and difference of Gaussian (DOG) channels.[12] where vs is the speed of sound, pðr; tÞ is the pressure, β is the thermal coefficient of volume expansion, CP is the specific heat at constant pressure, and Hðr; tÞ is the heating function.

System Matrix
Pseudoinverse
Filtered Backprojection and Simple Backprojection
Deconvolution
Image Quality
Conclusion
Full Text
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