论文标题
与神经变形场的关节冷冻-ET对齐和重建
Joint Cryo-ET Alignment and Reconstruction with Neural Deformation Fields
论文作者
论文摘要
我们提出了一个框架，以共同确定变形参数并重建电子冷冻学（Cryoet）中未知体积。冷冻旨在重建二维预测中的三维生物样品。一个主要的挑战是，我们只能对有限的倾斜范围获取预测，并且每个预测在获取过程中都会发生未知的变形。没有考虑这些变形导致重建不良。现有的冷冻软件包试图将预测对齐，通常是在使用手动反馈的工作流程中。我们提出的方法通过自动计算一组未明确的投影，同时重建未知体积，从而避免了这种不便。我们通过学习未构造的测量和变形参数的连续表示来实现这一目标。我们表明，我们的方法可以恢复高频细节，这些细节被破坏而无需考虑变形。
We propose a framework to jointly determine the deformation parameters and reconstruct the unknown volume in electron cryotomography (CryoET). CryoET aims to reconstruct three-dimensional biological samples from two-dimensional projections. A major challenge is that we can only acquire projections for a limited range of tilts, and that each projection undergoes an unknown deformation during acquisition. Not accounting for these deformations results in poor reconstruction. The existing CryoET software packages attempt to align the projections, often in a workflow which uses manual feedback. Our proposed method sidesteps this inconvenience by automatically computing a set of undeformed projections while simultaneously reconstructing the unknown volume. We achieve this by learning a continuous representation of the undeformed measurements and deformation parameters. We show that our approach enables the recovery of high-frequency details that are destroyed without accounting for deformations.
