A Model-Based Iterative Reconstruction Approach to Tunable Diode Laser Absorption Tomography

Abstract

Tunable diode laser absorption tomography (TDLAT) has emerged as a popular nonintrusive technique for simultaneous sensing of gas concentration and temperature. However, TDLAT imaging of concentration and temperature is an ill-posed, nonlinear inverse problem. Major challenges of TDLAT imaging include a highly nonlinear forward model, few projection measurements, and limited training data. We propose a novel model-based iterative reconstruction (MBIR) framework for TDLAT imaging. To do this, we formulate a nonlinear forward model for TDLAT that incorporates the physics of light absorbance through gaseous media, and we couple it with a non-Gaussian prior model based on a Gaussian mixture distribution that can be trained using a sparse training set. We show that the resulting MAP estimation problem can be solved using majorization minimization together with a novel multigrid optimization algorithm that solves the resulting optimization problem using an orthogonal basis set. Reconstructions using simulated TDLAT datasets show that our TDLAT-MBIR method can reduce reconstruction error while also resulting in a very computationally efficient algorithm.