A ct denoising neural network with image properties parameterization and control

Wenying Wang, Jianan Gang, J. Webster Stayman

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


A wide range of dose reduction strategies for x-ray computed tomography (CT) have been investigated. Recently, denoising strategies based on machine learning have been widely applied, often with impressive results, and breaking free from traditional noise-resolution trade-offs. However, since typical machine learning strategies provide a single denoised image volume, there is no user-tunable control of a particular trade-off between noise reduction and image properties (biases) of the denoised image. This is in contrast to traditional filtering and model-based processing that permits tuning of parameters for a level of noise control appropriate for the specific diagnostic task. In this work, we propose a novel neural network that includes a spatial-resolution parameter as additional input permits explicit control of the noise-bias trade-off. Preliminary results show the ability to control image properties through such parameterization as well as the possibility to tune such parameters for increased detectability in task-based evaluation.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Wei Zhao, Lifeng Yu
ISBN (Electronic)9781510640191
StatePublished - 2021
EventMedical Imaging 2021: Physics of Medical Imaging - Virtual, Online, United States
Duration: Feb 15 2021Feb 19 2021

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
ISSN (Print)1605-7422


ConferenceMedical Imaging 2021: Physics of Medical Imaging
Country/TerritoryUnited States
CityVirtual, Online

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Atomic and Molecular Physics, and Optics
  • Biomaterials
  • Radiology Nuclear Medicine and imaging


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