Computational methods for 3D imaging of neural activity in light-field microscopy

Abstract:

Light Field Microscopy (LFM) is a 3D imaging technique that captures spatial and angular information from light in a single snapshot. LFM is an appealing technique for applications in biological imaging due to its relatively simple implementation and fast 3D imaging speed. For instance, LFM can help to understand how neurons process information, as shown for functional neuronal calcium imaging. However, traditional volume reconstruction approaches for LFM suffer from low lateral resolution, high computational cost, and reconstruction artifacts near the native object plane. Therefore, in this thesis, we propose computational methods to improve the reconstruction performance of 3D imaging for LFM with applications to imaging neural activity. First, we study the image formation process and propose methods for discretization and simplification of the LF system. Typical approaches for discretization are performed by computing the discrete impulse response at different input locations defined by a sampling grid. Unlike conventional methods, we propose an approach that uses shift-invariant subspaces to generalize the discretization framework used in LFM. Our approach allows the selection of diverse sampling kernels and sampling intervals. Furthermore, the typical discretization method is a particular case of our formulation. Moreover, we propose a description of the system based on filter banks that fit the physics of the system. The periodic-shift invariant property per depth guarantees that the system can be accurately described by using filter banks. This description leads to a novel method to reduce the computational time using singular value …

Año de publicación:

2023

Keywords:

Fuente:

google