Design of Novel Loss Functions for Deep Learning in X-ray CT

Obaidullah Rahman, Ken D. Sauer, Madhuri Nagare, Charles A. Bouman, Roman Melnyk, Jie Tang, Brian Nett

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Deep learning (DL) shows promise of advantages over conventional signal processing techniques in a variety of imaging applications. The networks' being trained from examples of data rather than explicitly designed allows them to learn signal and noise characteristics to most effectively construct a mapping from corrupted data to higher quality representations. In inverse problems, one has options of applying DL in the domain of the originally captured data, in the transformed domain of the desired final representation, or both. X-ray computed tomography (CT), one of the most valuable tools in medical diagnostics, is already being improved by DL methods. Whether for removal of common quantum noise resulting from the Poisson-distributed photon counts, or for reduction of the ill effects of metal implants on image quality, researchers have begun employing DL widely in CT. The selection of training data is driven quite directly by the corruption on which the focus lies. However, the way in which differences between the target signal and measured data is penalized in training generally follows conventional, pointwise loss functions. This work introduces a creative technique for favoring reconstruction characteristics that are not well described by norms such as mean-squared or mean-absolute error. Particularly in a field such as X-ray CT, where radiologists' subjective preferences in image characteristics are key to acceptance, it may be desirable to penalize differences in DL more creatively. This penalty may be applied in the data domain, here the CT sinogram, or in the reconstructed image. We design loss functions for both shaping and selectively preserving frequency content of the signal.

Original languageEnglish
Title of host publication7th International Conference on Image Formation in X-Ray Computed Tomography
EditorsJoseph Webster Stayman
PublisherSPIE
ISBN (Electronic)9781510656697
DOIs
StatePublished - 2022
Externally publishedYes
Event7th International Conference on Image Formation in X-Ray Computed Tomography - Virtual, Online
Duration: Jun 12 2022Jun 16 2022

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12304
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference7th International Conference on Image Formation in X-Ray Computed Tomography
CityVirtual, Online
Period06/12/2206/16/22

Keywords

  • Deep learning
  • neural network
  • novel loss functions
  • spectral shaping
  • X-ray CT

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