- Abstract: In Computed Tomography (CT) scout scans are a first initial acquisition that determine scan parameters and final imaging regions. Current state-of-the art scout scans are 2D X-ray images acquired with the stationary CT scanner gantry and the moving table. A 3D scout volume based on a low-dose, sparse-view helical acquisition would be better for the scan planing, but image volumes usually suffer from high noise and especially angular under-sampling artifacts. We therefore evaluate the usage of a convolutional neuronal network to perform a non-linear upsampling and denoising of the input projection data. We compare our approach to a least-squares optimal linear interpolation and evaluate both algorithms with scout scan data synthesized from real patient datasets.
- Author Affiliation: Philips Research, Hamburg, Germany and Technische Universität München, Munich, Germany