Open AccessA CT denoising neural network with image properties parameterization and control
Author(s) -
Wenying Wang,
Grace J. Gang,
J. Webster Stayman
Publication year - 2021
Publication title -
pubmed central
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.192
H-Index - 176
pISSN - 0277-786X
DOI - 10.1117/12.2582145
Subject(s) - noise reduction , computer science , noise (video) , artificial intelligence , artificial neural network , task (project management) , reduction (mathematics) , image resolution , noise measurement , image (mathematics) , range (aeronautics) , computer vision , machine learning , pattern recognition (psychology) , engineering , mathematics , geometry , systems engineering , aerospace engineering
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.