Graduate School of Artificial Intelligence, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
School of Interdisciplinary Bioscience and Bioengineering, Pohang University of Science and Technology (POSTECH), Pohang, 37673, Republic of Korea.
Sci Rep. 2023 Jan 31;13(1):1738. doi: 10.1038/s41598-023-27627-y.
Synchrotron X-rays can be used to obtain highly detailed images of parts of the lung. However, micro-motion artifacts induced by such as cardiac motion impede quantitative visualization of the alveoli in the lungs. This paper proposes a method that applies a neural network for synchrotron X-ray Computed Tomography (CT) data to reconstruct the high-quality 3D structure of alveoli in intact mouse lungs at expiration, without needing ground-truth data. Our approach reconstructs the spatial sequence of CT images by using a deep-image prior with interpolated input latent variables, and in this way significantly enhances the images of alveolar structure compared with the prior art. The approach successfully visualizes 3D alveolar units of intact mouse lungs at expiration and enables us to measure the diameter of the alveoli. We believe that our approach helps to accurately visualize other living organs hampered by micro-motion.
同步加速器 X 射线可用于获取肺部部分的高度详细图像。然而，心脏运动等引起的微运动伪影妨碍了对肺部肺泡的定量可视化。本文提出了一种应用神经网络对同步加速器 X 射线计算机断层扫描（CT）数据进行重建的方法，无需真实数据即可重建呼气末期完整小鼠肺部的高质量 3D 肺泡结构。我们的方法通过使用带有插值输入潜在变量的深度图像先验来重建 CT 图像的空间序列，从而与现有技术相比，显著增强了肺泡结构的图像。该方法成功地可视化了呼气末期完整小鼠肺部的 3D 肺泡单元，并使我们能够测量肺泡的直径。我们相信，我们的方法有助于准确地可视化其他受微运动影响的活体器官。
