Neural Fields for Adaptive Photoacoustic Computed Tomography

Tianao Li, Manxiu Cui, Cheng Ma, Emma Alexander
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Abstract

Photoacoustic computed tomography (PACT) is a non-invasive imaging modality with wide medical applications. Conventional PACT image reconstruction algorithms suffer from wavefront distortion caused by the heterogeneous speed of sound (SOS) in tissue, which leads to image degradation. Accounting for these effects improves image quality, but measuring the SOS distribution is experimentally expensive. An alternative approach is to perform joint reconstruction of the initial pressure image and SOS using only the PA signals. Existing joint reconstruction methods come with limitations: high computational cost, inability to directly recover SOS, and reliance on inaccurate simplifying assumptions. Implicit neural representation, or neural fields, is an emerging technique in computer vision to learn an efficient and continuous representation of physical fields with a coordinate-based neural network. In this work, we introduce NF-APACT, an efficient self-supervised framework utilizing neural fields to estimate the SOS in service of an accurate and robust multi-channel deconvolution. Our method removes SOS aberrations an order of magnitude faster and more accurately than existing methods. We demonstrate the success of our method on a novel numerical phantom as well as an experimentally collected phantom and in vivo data. Our code and numerical phantom are available at https://github.com/Lukeli0425/NF-APACT.
用于自适应光声计算机断层扫描的神经场
光声计算机断层扫描(PACT)是一种无创成像模式,在医学上有着广泛的应用。传统的光声计算机断层扫描图像重建算法受到组织中异质声速(SOS)引起的波前失真影响,导致图像质量下降。考虑到这些影响可以提高图像质量,但测量 SOS 分布的实验成本很高。现有的联合重建方法有其局限性:计算成本高、无法直接恢复 SOS 以及依赖不准确的简化假设。隐式神经表示或神经场是计算机视觉领域的一种新兴技术,通过基于坐标的神经网络学习物理场的高效连续表示。在这项工作中,我们引入了 NF-APACT,这是一种高效的自我监督框架,利用神经场来估计 SOS,从而实现准确、稳健的多通道解卷积。与现有方法相比,我们的方法能更快更准确地消除 SOS 畸变。我们在一个新的数值模型以及实验收集的模型和体内数据上展示了我们方法的成功。我们的代码和数值模型可在 https://github.com/Lukeli0425/NF-APACT 网站上获得。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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