Manifold reconstruction of differences: A model-based iterative statistical estimation algorithm with a data-driven prior

Matthew Tivnan, J. Webster Stayman

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

Abstract

Manifold learning using deep neural networks been shown to be an effective tool for noise reduction in low-dose CT. We propose a new iterative CT reconstruction algorithm, called Manifold Reconstruction of Differences (MRoD), which combines physical models with a data-driven prior. The MRoD algorithm involves estimating a manifold component, approximating common features among all patients, and the difference component which has the freedom to fit the measured data. While the manifold captures typical patient features (e.g. healthy anatomy), the difference image highlights patient-specific elements (e.g. pathology). We present a framework which combines trained manifold-based modules with physical modules. We present a simulation study using anthropomorphic lung data showing that the MRoD algorithm can both isolate differences between a particular patient and the typical distribution, but also provide significant noise reduction with relatively low bias.

Original languageEnglish (US)
Title of host publicationMedical Imaging 2021
Subtitle of host publicationPhysics of Medical Imaging
EditorsHilde Bosmans, Wei Zhao, Lifeng Yu
PublisherSPIE
ISBN (Electronic)9781510640191
DOIs
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
Volume11595
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2021: Physics of Medical Imaging
CountryUnited States
CityVirtual, Online
Period2/15/212/19/21

ASJC Scopus subject areas

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

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