We present a new computed tomography method, the low third derivative (LTD) method, that is particularly suited for reconstructing the spatial distribution of gas concentrations from path-integral data for a small number of optical paths. The method finds a spatial distribution of gas concentrations that (1) has path integrals that agree with measured path integrals, and (2) has a low third spatial derivative in each direction, at every point. The trade-off between (1) and (2) is controlled by an adjustable parameter, which can be set based on analysis of the path-integral data. The method produces a set of linear equations, which can be solved with a single matrix multiplication if the constraint that all concentrations must be positive is ignored; the method is therefore extremely rapid. Analysis of experimental data from thousands of concentration distributions shows that the method works nearly as well as smooth basis function minimization (the best method previously available), yet is about 100 times faster.
BT - Atmospheric Environment DA - 06/2001 DO - 10.1016/S1352-2310(01)00082-6 IS - 16 LA - eng N2 -We present a new computed tomography method, the low third derivative (LTD) method, that is particularly suited for reconstructing the spatial distribution of gas concentrations from path-integral data for a small number of optical paths. The method finds a spatial distribution of gas concentrations that (1) has path integrals that agree with measured path integrals, and (2) has a low third spatial derivative in each direction, at every point. The trade-off between (1) and (2) is controlled by an adjustable parameter, which can be set based on analysis of the path-integral data. The method produces a set of linear equations, which can be solved with a single matrix multiplication if the constraint that all concentrations must be positive is ignored; the method is therefore extremely rapid. Analysis of experimental data from thousands of concentration distributions shows that the method works nearly as well as smooth basis function minimization (the best method previously available), yet is about 100 times faster.
PY - 2001 SP - 2827 EP - 2835 ST - Atmospheric Environment T2 - Atmospheric Environment TI - An algorithm for real-time tomography of gas concentrations, using prior information about spatial derivatives VL - 35 SN - 13522310 ER -