Cryogenic electron tomography (cryo-ET) is a technique for imaging biological samples such as viruses, cells, and proteins in 3D. A microscope collects a series of 2D projections of the sample, and the goal is to reconstruct the 3D density of the sample called the tomogram. This is difficult as the 2D projections have a missing wedge of information and are noisy. Tomograms reconstructed with conventional methods, such as filtered back-projection, suffer from the noise, and from artifacts and anisotropic resolution due to the missing wedge of information. To improve the visual quality and resolution of such tomograms, we propose a deep-learning approach for simultaneous denoising and missing wedge reconstruction called DeepDeWedge. DeepDeWedge is based on fitting a neural network to the 2D projections with a self-supervised loss inspired by noise2noise-like methods. The algorithm requires no training or ground truth data. Experiments on synthetic and real cryo-ET data show that DeepDeWedge achieves competitive performance for deep learning-based denoising and missing wedge reconstruction of cryo-ET tomograms.

翻译：低温电子断层扫描（cryo-ET）是一种对病毒、细胞和蛋白质等生物样本进行三维成像的技术。显微镜采集一系列样本的二维投影，目标是重建样本的三维密度图，即断层图。由于二维投影存在信息缺失楔且含有噪声，这一过程颇具挑战性。采用传统方法（如滤波反投影）重建的断层图会受到噪声影响，并因信息缺失楔而产生伪影和各向异性分辨率问题。为提升此类断层图的视觉质量与分辨率，我们提出一种名为DeepDeWedge的深度学习方法，用于同时进行去噪与缺失楔重建。DeepDeWedge基于噪声对噪声（noise2noise）类方法的自监督损失，将神经网络拟合至二维投影。该算法无需训练或真实数据。在合成与真实低温电子断层扫描数据上的实验表明，DeepDeWedge在基于深度学习的低温电子断层图去噪与缺失楔重建中达到了具有竞争力的性能。