Guidance to Noise Simulation in X-ray Imaging

In medical imaging, noise is an inherent occurring signal corruption, especially for the X-ray imaging where dose exposure to the patient should be minimal. Besides potential image degeneration, which may hinder accurate diagnoses, the noise can have negative impact on signal processing and evaluation algorithms, especially in deep learning (DL) methods. Furthermore, for the training of DL based noise reduction or to bolster DL methods against degeneration due to unseen types of noise or noise levels, it is inevitable to have a thorough and correct noise simulation available. This paper introduces a comprehensive noise simulation method that integrates the strengths of existing techniques into a more complete solution. Simultaneously, our approach aims to minimize the reliance on device-specific measurements and data, by proposing an automatic detector gain estimation.