Mathematical Modeling for 2D Light-Sheet Fluorescence Microscopy image reconstruction

We study an inverse problem for Light Sheet Fluorescence Microscopy (LSFM), where the density of fluorescent molecules needs to be reconstructed. Our first step is to present a mathematical model to describe the measurements obtained by an optic camera during an LSFM experiment. Two meaningful stages are considered: excitation and fluorescence. We propose a paraxial model to describe the excitation process which is directly related with the Fermi pencil-beam equation. For the fluorescence stage, we use the transport equation to describe the transport of photons towards the detection camera. For the mathematical inverse problem that we obtain after the modeling, we present a uniqueness result, recasting the problem as the recovery of the initial condition for the heat equation in $\mathbb{R}\times(0,\infty)$ from measurements in a space-time curve. Additionally, we present numerical experiments to recover the density of the fluorescent molecules by discretizing the proposed model and facing this problem as the solution of a large and sparse linear system. Some iterative and regularized methods are used to achieve this objective. The results show that solving the inverse problem achieves better reconstructions than the direct acquisition method that is currently used

Lipschitz stability for Backward Heat Equation with application to Fluorescence Microscopy

In this work, we study a Lipschitz stability result in the reconstruction of a compactly supported initial temperature for the heat equation in $\mathbb{R}^n$, from measurements along a positive time interval and over an open set containing its support. We take advantage of the explicit dependency of solutions to the heat equation with respect to the initial condition. By means of Carleman estimates we obtain an analogous result for the case when the observation is made along an exterior region $\omega\times(\tau,T)$, such that the unobserved part $\mathbb{R}^n\backslash\omega$ is bounded. In the latter setting, the method of Carleman estimates gives a general conditional logarithmic stability result when initial temperatures belong to a certain admissible set, and without the assumption of compactness of support. Furthermore, we apply these results to deduce a similar result for the heat equation in $\mathbb{R}$ for measurements available on a curve contained in $\mathbb{R}\times [0,\infty)$, from where a stability estimate for an inverse problem arising in 2D Fluorescence Microscopy is deduced as well. In order to further understand this Lipschitz stability, in particular, the magnitude of its stability constant with respect to the noise level of the measurements, a numerical reconstruction is presented based on the construction of a linear system for the inverse problem in Fluorescence Microscopy. We investigate the stability constant with the condition number of the corresponding matrix

PET Reconstruction with non-Negativity Constraint in Projection Space: Optimization Through Hypo-Convergence

International audienceStandard positron emission tomography (PET) reconstruction techniques are based on maximum-likelihood (ML) optimization methods, such as the maximum-likelihood expectation-maximization (MLEM) algorithm and its variations. Most of these methodologies rely on a positivity constraint on the activity distribution image. Although this constraint is meaningful from a physical point of view, it can be a source of bias for low-count/high-background PET, which can compromise accurate quantification. Existing methods that allow for negative values in the estimated image usually utilize a modified log-likelihood, and therefore break the data statistics. In this work we propose to incorporate the positivity constraint on the projections only, by approximating the (penalized) log-likelihood function by an adequate sequence of objective functions that are easily maximized without constraint. This sequence is constructed such that there is hypo-convergence (a type of convergence that allows the convergence of the maximizers under some conditions) to the original log-likelihood, hence allowing us to achieve maximization with positivity constraint on the projections using simple settings. A complete proof of convergence under weak assumptions is given. We provide results of experiments on simulated data where we compare our methodology with the alternative direction method of multipliers (ADMM) method, showing that our algorithm converges to a maximizer which stays in the desired feasibility set, with faster convergence than ADMM. We also show that this approach reduces the bias, as compared with MLEM images, in necrotic tumors-which are characterized by cold regions surrounded by hot structures-while reconstructing similar activity values in hot regions