OADAT：用于标准化图像处理的实验与合成临床光声数据
OADAT: Experimental and Synthetic Clinical Optoacoustic Data for Standardized Image Processing
AI总结：
针对光声成像缺乏标准化数据集导致处理方法难以客观比较的问题，本文发布了OADAT数据集，包含多参数的实验与合成光声数据及训练好的神经网络，还设44项基准实验助力先进处理方法开发。
摘要：
光声（OA）成像基于纳秒级激光脉冲激发生物组织，随后检测由光吸收介导的热弹性膨胀产生的超声波。OA成像兼具丰富的光学对比度和深部组织高分辨率的强大优势，这推动了其在临床和实验室场景中多项有吸引力的新应用探索。然而，目前尚无由不同类型实验装置生成的标准化数据集及配套处理方法，来促进OA在临床场景中更广泛应用的发展，这使得新的与成熟的数据处理方法之间难以进行客观比较，往往导致定性结果和对数据的随意解读。本文中，我们提供了由不同实验参数和断层采集几何结构生成的实验与合成OA原始信号及重建图像域数据集。我们还提供了经过训练的神经网络，用于解决OA图像处理相关的三个重要挑战：有限视角断层条件下的精确重建、空间欠采样伪影去除，以及用于改进图像重建的解剖分割。具体而言，我们定义了与上述挑战对应的44项实验作为基准，为更先进处理方法的开发提供参考。
英文摘要：
Optoacoustic (OA) imaging is based on excitation of biological tissues with nanosecond-duration laser pulses followed by subsequent detection of ultrasound waves generated via light-absorption-mediated thermoelastic expansion. OA imaging features a powerful combination between rich optical contrast and high resolution in deep tissues. This enabled the exploration of a number of attractive new applications both in clinical and laboratory settings. However, no standardized datasets generated with different types of experimental set-up and associated processing methods are available to facilitate advances in broader applications of OA in clinical settings. This complicates an objective comparison between new and established data processing methods, often leading to qualitative results and arbitrary interpretations of the data. In this paper, we provide both experimental and synthetic OA raw signals and reconstructed image domain datasets rendered with different experimental parameters and tomographic acquisition geometries. We further provide trained neural networks to tackle three important challenges related to OA image processing, namely accurate reconstruction under limited view tomographic conditions, removal of spatial undersampling artifacts and anatomical segmentation for improved image reconstruction. Specifically, we define 44 experiments corresponding to the aforementioned challenges as benchmarks to be used as a reference for the development of more advanced processing methods.
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