Variational Deep Learning for Low-Dose Computed Tomography
Erich Kobler, Matthew J. Muckley, Baiyu Chen, Florian Knoll, Kerstin Hammernik, Thomas Pock, Daniel K. Sodickson, Ricardo Otazo
Abstract
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.
BibTeX
@inproceedings{icassp2018_variationaldeepl,
title = {Variational Deep Learning for Low-Dose Computed Tomography},
author = {Erich Kobler and Matthew J. Muckley and Baiyu Chen and Florian Knoll and Kerstin Hammernik and Thomas Pock and Daniel K. Sodickson and Ricardo Otazo},
booktitle = {ICASSP 2018},
year = {2018}
}