Shift-Invariant-Subspace Discretization and Volume Reconstruction for Light Field Microscopy
Shift-Invariant-Subspace Discretization and Volume Reconstruction for Light Field Microscopy
复制标题
光场显微镜的平移不变子空间离散化和体积重建
DOI：
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复制
发表时间：
2022
影响因子：
5.4
通讯作者：
P. Dragotti
中科院分区：
文献类型：
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作者：
Herman Verinaz;P. Song;Carmel L. Howe;Amanda J. Foust;P. Dragotti
光场显微镜（LFM）是一种成像技术，可以通过单个2D图像捕获3D空间信息。线性调频是有吸引力的，因为它相对简单的实现和快速的体积采集速率。以相机帧速率捕获体积时间序列可以使许多生物系统的行为的研究成为可能。例如，它可以提供对活的3D神经网络的通信动力学的见解。然而，用于LFM的常规3D重建算法通常遭受高计算成本、低横向分辨率和重建伪影。在这项工作中，我们研究了这些问题的起源，并提出了新的技术，以提高重建过程的性能。首先，我们提出了一个离散化方法，使用平移不变子空间推广的LFM中使用的典型离散化框架。然后，我们研究了在理想条件下，平移不变子空间假设作为体重建的先验。此外，我们还提出了一种利用奇异值分解（SVD）来减少正演模型计算时间的方法。最后，我们建议使用迭代的方法，将额外的先验执行伪影免费的3D重建从真实的光场图像。我们的实验表明，我们的方法比Richardson-Lucy为基础的策略在计算时间，图像质量和伪影减少。
Light Field Microscopy (LFM) is an imaging technique that captures 3D spatial information with a single 2D image. LFM is attractive because of its relatively simple implementation and fast volume acquisition rate. Capturing volume time series at a camera frame rate can enable the study of the behaviour of many biological systems. For instance, it could provide insights into the communication dynamics of living 3D neural networks. However, conventional 3D reconstruction algorithms for LFM typically suffer from high computational cost, low lateral resolution, and reconstruction artifacts. In this work, we study the origin of these issues and propose novel techniques to improve the performance of the reconstruction process. First, we propose a discretization approach that uses shift-invariant subspaces to generalize the typical discretization framework used in LFM. Then, we study the shift-invariant-subspace assumption as a prior for volume reconstruction under ideal conditions. Furthermore, we present a method to reduce the computational time of the forward model by using singular value decomposition (SVD). Finally, we propose to use iterative approaches that incorporate additional priors to perform artifact-free 3D reconstruction from real light field images. We experimentally show that our approach performs better than Richardson-Lucy-based strategies in computational time, image quality, and artifact reduction.