Variational Deep Learning for Low-Dose Computed Tomography

Kobler E, Muckley M, Chen B, Knoll F, Hammernik K, Pock T, Sodickson D, Otazo R (2018)

Publication Type: Conference contribution

Publication year: 2018

Journal

2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Publisher: Institute of Electrical and Electronics Engineers Inc.

Book Volume: 2018-April

Pages Range: 6687-6691

Conference Proceedings Title: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Event location: Calgary, AB, CAN

ISBN: 9781538646588

DOI: 10.1109/ICASSP.2018.8462312

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.

Authors with CRIS profile

Florian Knoll

Involved external institutions

Technische Universität Graz AT Austria (AT) New York University (NYU) US United States (USA) (US)

How to cite

APA:

Kobler, E., Muckley, M., Chen, B., Knoll, F., Hammernik, K., Pock, T.,... Otazo, R. (2018). Variational Deep Learning for Low-Dose Computed Tomography. In ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings (pp. 6687-6691). Calgary, AB, CAN: Institute of Electrical and Electronics Engineers Inc..

MLA:

Kobler, Erich, et al. "Variational Deep Learning for Low-Dose Computed Tomography." Proceedings of the 2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018, Calgary, AB, CAN Institute of Electrical and Electronics Engineers Inc., 2018. 6687-6691.

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