Learning-Based Image Restorations of Sparse-View CT Data: Is It Reliable? - Philip Maurice Trapp; Elias Eulig; Joscha Maier; Frederic Ballach; Raoul Christoph; Ralf Christoph; Marc Kachelrieß (e-Journal of Nondestructive Testing)
Learning-based methods for the restoration of computed tomography (CT) images promise very good image quality even in areas with insufficient data sampling and thus suggest enormous savings in measurement time. This work shows by means of restorations of sparse-view CT data that such methods must be evaluated thoroughly and in a task-specific manner, as details of the workpiece may not be exactly reconstructed. In addition, this work examines the influence of these methods on metrological specification measurements of CTs and the conclusions that can be drawn with regard to the objective specification of such algorithms.