Variational Deep Learning for Low-Dose Computed Tomography

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

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

Original languageEnglish
Title of host publication2018 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2018 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers
Pages6687-6691
Number of pages5
Volume2018-April
ISBN (Print)9781538646588
DOIs
Publication statusPublished - 10 Sep 2018
Event2018 IEEE International Conference on Acoustics, Speech, and Signal Processing: ICASSP 2018 - Calgary, Canada
Duration: 15 Apr 201820 Apr 2018

Conference

Conference2018 IEEE International Conference on Acoustics, Speech, and Signal Processing
Abbreviated titleICASSP
CountryCanada
CityCalgary
Period15/04/1820/04/18

Keywords

  • Compressed sensing
  • Computed tomography
  • Machine learning
  • Medical imaging
  • Variational networks

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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