Resolution level and reconstruction quality in nano-computed tomography (nano-CT) are in part limited by the stability of microscopes, because the magnitude of mechanical vibrations during scanning becomes comparable to the imaging resolution, and the ability of the samples to resist radiation induced deformations during data acquisition. In such cases, there is no incentive in recovering the sample state at different time steps like in time-resolved reconstruction methods, but instead the goal is to retrieve a single reconstruction at the highest possible spatial resolution and without any imaging artifacts. Here we propose a distributed optimization solver for tomographic imaging of samples at the nanoscale. Our approach solves the tomography problem jointly with projection data alignment, nonrigid sample deformation correction, and regularization. Projection data consistency is regulated by dense optical flow estimated by Farneback's algorithm, leading to sharp sample reconstructions with less artifacts. Synthetic data tests show robustness of the method to Poisson and low-frequency background noise. We accelerated the solver on multi-GPU systems and validated the method on three nano-imaging experimental data sets.

中文翻译：

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非刚性纳米层析成像的分布式优化

纳米计算机断层扫描（nano-CT）的分辨率水平和重建质量部分受限于显微镜的稳定性，因为扫描过程中的机械振动幅度变得与成像分辨率相当，并且样品具有抵抗辐射诱发的能力数据采集​​过程中的变形。在这种情况下，没有动力像在时间分辨的重建方法中那样在不同的时间步长恢复样本状态，而是目标是以尽可能高的空间分辨率检索单个重建，并且没有任何成像伪像。在这里，我们提出了一种用于纳米级样品层析成像的分布式优化求解器。我们的方法通过投影数据对齐，非刚性样本变形校正，和正则化。投影数据的一致性由Farneback的算法估算的密集光流调节，从而实现了清晰的样本重建，且伪像更少。综合数据测试显示了该方法对泊松和低频背景噪声的鲁棒性。我们在多GPU系统上加速了求解器，并在三个纳米成像实验数据集上验证了该方法。