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Effects of Different Imaging Models on Least-Squares Image Reconstruction Accuracy in Photoacoustic Tomography

机译:不同成像模式对光声层析成像中最小二乘图像重建精度的影响

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摘要

In the classic formulation of photoacoustic tomography (PAT), two distinct descriptions of the imaging model have been employed for developing reconstruction algorithms. We demonstrate that the numerical and statistical properties of unweighted least-squares reconstruction algorithms associated with each imaging model are generally very different. Specifically, some PAT reconstruction algorithms, including many of the iterative algorithms previously explored, do not work directly with the raw measured pressure wavefields, but rather with an integrated data function that is obtained by temporally integrating the photoacoustic wavefield. The integration modifies the statistical distribution of the data, introducing statistical correlations among samples. This change is highly significant for iterative algorithms, many of which explicitly or implicitly seek to minimize a statistical cost function. In this work, we demonstrate that iterative reconstruction by least-squares minimization yields better resolution-noise tradeoffs when working with the raw pressure data than with the integrated data commonly employed. In addition, we demonstrate that the raw-data based approach is less sensitive to certain deterministic errors, such as dc offset errors.
机译:在光声层析成像(PAT)的经典公式中,成像模型的两个不同描述已用于开发重建算法。我们证明与每个成像模型相关的未加权最小二乘重建算法的数值和统计属性通常存在很大差异。具体而言,某些PAT重构算法(包括先前探索的许多迭代算法)并不直接与原始测得的压力波场一起工作,而是与通过对光声波场进行时间积分而获得的积分数据函数一起工作。积分修改了数据的统计分布,从而在样本之间引入了统计相关性。对于迭代算法,此更改非常重要,其中许多迭代算法显式或隐式地寻求最小化统计成本函数。在这项工作中,我们证明了在处理原始压力数据时,通过最小二乘最小化进行的迭代重建会产生比通常使用的集成数据更好的分辨率-噪声折衷。此外,我们证明了基于原始数据的方法对某些确定性误差(例如直流偏移误差)不那么敏感。

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