近日，美国加州大学伯克利分校Laura Waller等研究人员合作开发出用于动态多次成像的神经时空模型。该研究于2024年9月24日在线发表于国际一流学术期刊《自然—方法学》。

研究人员提出了一种神经时空模型（NSTM），该模型在没有数据先验或预训练的情况下联合估计场景及其运动动态。因此，研究人员可以在使用传统重建的相同原始测量集的基础上，同时去除运动伪影并解析样本动态。

研究人员在三种计算成像系统中演示了NSTM：差分相位对比显微镜、三维结构照明显微镜和滚动快门扩散相机。研究人员表明，NSTM能够恢复亚细胞运动动态，从而减少运动伪影对活体系统误解的影响。

研究人员表示，来自多个顺序捕获的测量的计算成像重建在场景动态时常常会受到运动伪影的影响。

附：英文原文

Title: Neural space–time model for dynamic multi-shot imaging

Author: Cao, Ruiming, Divekar, Nikita S., Nuez, James K., Upadhyayula, Srigokul, Waller, Laura

Issue&Volume: 2024-09-24

Abstract: Computational imaging reconstructions from multiple measurements that are captured sequentially often suffer from motion artifacts if the scene is dynamic. We propose a neural space–time model (NSTM) that jointly estimates the scene and its motion dynamics, without data priors or pre-training. Hence, we can both remove motion artifacts and resolve sample dynamics from the same set of raw measurements used for the conventional reconstruction. We demonstrate NSTM in three computational imaging systems: differential phase-contrast microscopy, three-dimensional structured illumination microscopy and rolling-shutter DiffuserCam. We show that NSTM can recover subcellular motion dynamics and thus reduce the misinterpretation of living systems caused by motion artifacts.

DOI: 10.1038/s41592-024-02417-0

Source: https://www.nature.com/articles/s41592-024-02417-0