ChestXsim: An Open-Source Framework for Realistic Chest X-Ray Tomosynthesis Simulations

Deep Learning approaches show promise for improving tomosynthesis reconstruction but require paired CT and tomosynthesis datasets that are difficult to obtain. This work presents ChestXsim, an open-source Python framework that enable the simulation of digital chest tomosynthesis (DCT) from chest CT data. Its modular design includes the preprocessing pipeline to adapt helical chest CT volumes to a standard tomosynthesis positioning and the simulation of polychromatic projections with noise modelling. Additionally, ChestXsim provides reconstruction techniques (FDK/SART) and leverages GPU acceleration through open-source ASTRA kernels. ChestXsim offers a fast and efficient method for simulating DCT from standard chest CT data, making it a valuable tool for generating paired datasets.