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In this talk, we present our latest work on learning reconstructions of low-dose computed tomography data. We focus on two methods to decrease the radiation dose: x-ray tube current reduction, reducing the signal-to-noise ratio, and x-ray beam interruption, which undersamples data and results in images with aliasing artifacts. Our reconstruction results using trainable variational networks enable higher radiation dose reductions and/or increase the image quality for a given dose.
This presentation is part of Minisymposium “MS50 - Analysis, Optimization, and Applications of Machine Learning in Imaging (3 parts)”
organized by: Michael Moeller (University of Siegen) , Gitta Kutyniok (Technische Universität Berlin) .