基于坐标的声速恢复用于像差校正光声计算机断层扫描
Coordinate-based Speed of Sound Recovery for Aberration-Corrected Photoacoustic Computed Tomography
AI总结：
本文提出一种自监督联合重建方法，通过坐标参数化声速并反向传播梯度，实现环形阵列光声断层扫描的像差校正，比现有方法快35倍。
摘要：
光声计算机断层扫描（PACT）是一种非侵入性成像模态，类似于超声，具有广泛的医学应用。常规PACT图像因组织中声速（SOS）不均匀引起的波前畸变而退化。考虑这些效应可以改善图像质量并提供医学上有用的信息，但直接测量SOS是繁重的，且现有的联合重建方法计算成本高。传统监督学习技术在此数据匮乏领域目前不可用。在本工作中，我们引入了一种高效的自监督联合重建方法，用于环形阵列PACT系统恢复SOS和高质量图像。为解决这一半盲逆问题，我们使用像素网格或神经场（NF）对SOS进行参数化，并通过可微成像前向模型反向传播梯度直接更新它。我们的方法比当前最先进技术更准确地去除SOS像差，且速度快35倍。我们在模拟中定量证明了我们方法的成功，并在实验采集和体内数据上定性证明了其成功。我们的代码和合成数值体模可在我们的项目页面获取：https://lukeli0425.github.io/Coord-SoS-PACT/。
英文摘要：
Photoacoustic computed tomography (PACT) is a non-invasive imaging modality, similar to ultrasound, with wide-ranging medical applications. Conventional PACT images are degraded by wavefront distortion caused by the heterogeneous speed of sound (SOS) in tissue. Accounting for these effects can improve image quality and provide medically useful information, but measuring the SOS directly is burdensome and the existing joint reconstruction method is computationally expensive. Traditional supervised learning techniques are currently inaccessible in this data-starved domain. In this work, we introduce an efficient, self-supervised joint reconstruction method that recovers SOS and high-quality images for ring array PACT systems. To solve this semi-blind inverse problem, we parametrize the SOS using either a pixel grid or a neural field (NF) and update it directly by backpropagating the gradients through a differentiable imaging forward model. Our method removes SOS aberrations more accurately and 35x faster than the current SOTA. We demonstrate the success of our method quantitatively in simulation and qualitatively on experimentally-collected and in vivo data. Our code and synthetic numerical phantoms are available on our project page: https://lukeli0425.github.io/Coord-SoS-PACT/.
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