Liu Mengnan, Han Yu, Xi Xiaoqi, Zhu Linlin, Yang Shuangzhan, Tan Siyu, Chen Jian, Li Lei, Yan Bin
Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China.
Entropy (Basel). 2022 Jul 13;24(7):967. doi: 10.3390/e24070967.
The resolution of 3D structure reconstructed by laboratory nanoCT is often affected by changes in ambient temperature. Although correction methods based on projection alignment have been widely used, they are time-consuming and complex. Especially in piecewise samples (e.g., chips), the existing methods are semi-automatic because the projections lose attenuation information at some rotation angles. Herein, we propose a fast correction method that directly processes the reconstructed slices. Thus, the limitations of the existing methods are addressed. The method is named multiscale dense U-Net (MD-Unet), which is based on MIMO-Unet and achieves state-of-the-art artifacts correction performance in nanoCT. Experiments show that MD-Unet can significantly boost the correction performance (e.g., with three orders of magnitude improvement in correction speed compared with traditional methods), and MD-Unet+ improves 0.92 dB compared with MIMO-Unet in the chip dataset.
实验室纳米计算机断层扫描（nanoCT）重建的三维结构分辨率常常受到环境温度变化的影响。尽管基于投影对齐的校正方法已被广泛使用，但它们既耗时又复杂。特别是在分段样本（如芯片）中，现有方法是半自动的，因为投影在某些旋转角度会丢失衰减信息。在此，我们提出一种直接处理重建切片的快速校正方法。因此，解决了现有方法的局限性。该方法名为多尺度密集U型网络（MD-Unet），它基于多输入多输出U型网络（MIMO-Unet），并在纳米CT中实现了一流的伪影校正性能。实验表明，MD-Unet可以显著提高校正性能（例如，与传统方法相比，校正速度提高了三个数量级），并且在芯片数据集中，MD-Unet+比MIMO-Unet提高了0.92分贝。
