Stochastic tomography and its applications in 3D imaging of mixing fluids

Abstract

We present a novel approach for highly detailed 3D imaging of turbulent fluid mixing behaviors. The method is based on visible light computed tomography, and is made possible by a new stochastic tomographic reconstruction algorithm based on random walks. We show that this new stochastic algorithm is competitive with specialized tomography solvers such as SART, but can also easily include arbitrary convex regularizers that make it possible to obtain high-quality reconstructions with a very small number of views. Finally, we demonstrate that the same stochastic tomography approach can also be used to directly re-render arbitrary 2D projections without the need to ever store a 3D volume grid.

Progressive Stochastic Reconstruction Technique (PSRT) for cryo electron tomography

“…To obtain results with sufficient quality, they employed both a priori information and regularization into their objective function. Method based on MH algorithm was also developed for tomographic high contrast fluid imaging by Gregson et al (2012). This technique, called Stochastic Tomography (ST) uses spherical samples of constant size and energy and requires additional regularization to obtain reconstructions with reasonable quality.…”

“…(1) using series of random walks was introduced by Gregson et al (2012) as the Stochastic Tomography (ST). In this subsection we review this principle, as it is essential for understanding the core of our algorithm.…”

“…This idea was further extended in the work of Gregson et al (2012) for visible light 3D imaging of fluid dynamics. In both cases, the ability of MCM to easily incorporate a priori information and/or regularization was the main reason for choosing this approach.…”

“…In this work we adapt the basic algorithm of Gregson et al (2012) to reconstruction of cryoET tilt series. We further extend it with progressive coarse-to-fine iterative scheme and a novel memory efficient solution to the specimen-level interior problem that removes all the associated artifacts.…”

Stochastic Deconvolution

“…This paper presents Stochastic Deconvolution, a new framework for non-blind image deconvolution based on stochastic random walks. Stochastic Deconvolution is based on an adaptation of a recent stochastic optimization method for solving computed tomography problems [6] to the problem of deconvolution. The resulting algorithm amounts to a variant of coordinate-descend optimization, where the descent direction is chosen using a random walk that utilizes spatial coherence.…”