We have developed a novel 2D-constrained Lucy-Richardson deconvolution algorithm to derive precise column density maps of dust clouds in the Milky Way from X-ray dust echoes. X-ray dust tomography employs observations of dust echoes caused by bright, variable X-ray sources and the object’s light curve to constrain dust distances and physical properties. Heinz et al. 2015 developed a 1D deconvolution algorithm that was used successfully on individual observations of echoes. The goal of this project is to extend that algorithm to perform the joint deconvolution of several profiles simultaneously in order to achieve better signal-to-noise and make use of short observations which would give no meaningful data if deconvolved individually. This work was supported by the National Science Foundation’s REU program in Astrophysics through NSF award AST-1852136 and NASA/Chandra grant GO9-20124X.