Variational Deep Learning for Low-Dose Computed Tomography - Kobler, Erich; Muckley, Matthew; Chen, Baiyu; Knoll, Florian; Hammernik, Kerstin; Pock, Thomas; Sodickson, Daniel; Otazo, Ricardo (ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings, 2018)
In this work, we propose a learning-based variational network (VN) approach for reconstruction of low-dose 3D computed tomography data. We focus on two methods to decrease the radiation dose: (1) x-ray tube current reduction, which reduces the signal-to-noise ratio, and (2) x-ray beam interruption, which undersamples data and results in images with aliasing artifacts. While the learned VN denoises the current-reduced images in the first case, it reconstructs the undersampled data in the second case. Different VNs for denoising and reconstruction are trained on a single clinical 3D abdominal data set. The VNs are compared against state-of-the-art model-based denoising and sparse reconstruction techniques on a different clinical abdominal 3D data set with 4-fold dose reduction. Our results suggest that the proposed VNs enable higher radiation dose reductions and/or increase the image quality for a given dose.
CRIS-Team Scopus Importer:2021-08-17
Keywords: Compressed sensing; Computed tomography; Machine learning; Medical imaging; Variational networks