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Learning Real Noise for Ultra-Low Dose Lung CT Denoising

  • Michael Green
  • Edith M. Marom
  • Eli Konen
  • Nahum Kiryati
  • Arnaldo Mayer
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11075)

Abstract

Neural image denoising is a promising approach for quality enhancement of ultra-low dose (ULD) CT scans after image reconstruction. The availability of high-quality training data is instrumental to its success. Still, synthetic noise is generally used to simulate the ULD scans required for network training in conjunction with corresponding normal dose scans. This reductive approach may be practical to implement but ignores any departure of the real noise from the assumed model. In this paper, we demonstrate the training of denoising neural networks with real noise. For this purpose, a special training set is created from a pair of ULD and normal-dose scans acquired on each subject. Accurate deformable registration is computed to ensure the required pixel-wise overlay between corresponding ULD and normal-dose patches. To our knowledge, it is the first time real CT noise is used for the training of denoising neural networks. The benefits of the proposed approach in comparison to synthetic noise training are demonstrated both qualitatively and quantitatively for several state-of-the art denoising neural networks. The obtained results prove the feasibility and applicability of real noise learning as a way to improve neural denoising of ULD lung CT.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Michael Green
    • 1
  • Edith M. Marom
    • 2
  • Eli Konen
    • 2
  • Nahum Kiryati
    • 1
  • Arnaldo Mayer
    • 2
  1. 1.Department of Electrical EngineeringTel-Aviv UniversityTel AvivIsrael
  2. 2.Diagnostic Imaging, Sheba Medical Center, Affiliated to the Sackler School of MedicineTel-Aviv UniversityTel AvivIsrael

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