标题： 基于空间光谱重建的生物成像增强高光谱断层扫描 显示英文标题

标题： Enhanced hyperspectral tomography for bioimaging by spatiospectral reconstruction

摘要： 在这里，我们应用高光谱亮场成像来获取具有出色能量分辨率（800电子伏特）的计算机断层扫描图像，并首次将其应用于通过其特征K边映射固定生物样本中的染料分布。 传统上，由于在每个像素检测到的光子分布在多达200个能量通道中，因此能量选择性图像的特点是计数率低且信噪比差。 这意味着需要高X射线暴露、长时间扫描和高剂量才能对独特的光谱标记进行成像。 在这里，我们使用专用的迭代重建算法从低剂量、噪声数据集中获得了高质量的能量分辨断层扫描图像。 该算法利用了光谱域内的空间平滑性和通道间结构相关性，采用了两个精心选择的正则化项。 对于多相体模，扫描时间减少了36倍。 对一离体单碘染色生物样本进行了光谱分析方法（包括K边减法和吸收步长拟合）的评估，其中低化学浓度和不均匀分布可能会影响软组织分割和可视化。 重建算法可通过开源核心成像库获得。 总之，这些工具为可视化和元素映射提供了新的能力，对多重染色的生物标本有潜在的应用价值。 显示英文摘要

摘要： Here we apply hyperspectral bright field imaging to collect computed tomographic images with excellent energy resolution (800 eV), applying it for the first time to map the distribution of stain in a fixed biological sample through its characteristic K-edge. Conventionally, because the photons detected at each pixel are distributed across as many as 200 energy channels, energy-selective images are characterised by low count-rates and poor signal-to-noise ratio. This means high X-ray exposures, long scan times and high doses are required to image unique spectral markers. Here, we achieve high quality energy-dispersive tomograms from low dose, noisy datasets using a dedicated iterative reconstruction algorithm. This exploits the spatial smoothness and inter-channel structural correlation in the spectral domain using two carefully chosen regularisation terms. For a multi-phase phantom, a reduction in scan time of 36 times is demonstrated. Spectral analysis methods including K-edge subtraction and absorption step-size fitting are evaluated for an ex vivo, single (iodine)-stained biological sample, where low chemical concentration and inhomogeneous distribution can affect soft tissue segmentation and visualisation. The reconstruction algorithms are available through the open-source Core Imaging Library. Taken together, these tools offer new capabilities for visualisation and elemental mapping, with promising applications for multiply-stained biological specimens.