Variational Deep Learning for Low-Dose Computed Tomography

Erich Kobler, Matthew Muckley, Baiyu Chen, Florian Knoll, Kerstin Hammernik, Thomas Pock, Daniel Sodickson, Ricardo Otazo

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband


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.

Titel2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers
ISBN (Print)9781538646588
PublikationsstatusVeröffentlicht - 10 Sep 2018
Veranstaltung2018 IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2018 - Calgary, Kanada
Dauer: 15 Apr 201820 Apr 2018


Konferenz2018 IEEE International Conference on Acoustics, Speech, and Signal Processing

ASJC Scopus subject areas

  • Software
  • !!Signal Processing
  • !!Electrical and Electronic Engineering

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