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Anatomy registration via patient sensing for chest x-ray digital tomosynthesis

  • Yang Zhao*
  • , Alex Chen
  • , Eric Tkaczyk
  • , Jie Liu
  • *Corresponding author for this work
  • Harbin Institute of Technology Shenzhen
  • California State University Dominguez Hills
  • General Electric

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

Abstract

Chest X-ray is one of the most commonly performed radiologic procedures for respiratory diseases. Digital tomosynthesis (DTS) provides volumetric anatomic information at lower cost and dose compared to computed tomography (CT). However, current DTS system provides insufficient patient positioning feedback and requires a large number of reconstructed slices in order to ensure imaging the entirety of targeted anatomy. We propose an anatomy registration prototype using measurements from RGB-D cameras to 1) assist acquisition workflow, 2) provide individual-specific anatomical information to improve tomosynthesis reconstruction. Our experiments show that anatomy registration can provide real-time feedback of patient 2D position and body thickness. Our reconstruction simulations show that the anatomy and body information can speed up DTS reconstruction, reduce the number of redundant tomosynthesis slices, and help reduce image interpretation time.

Original languageEnglish
Title of host publicationMedical Imaging 2023
Subtitle of host publicationPhysics of Medical Imaging
EditorsLifeng Yu, Rebecca Fahrig, John M. Sabol
PublisherSPIE
ISBN (Electronic)9781510660311
DOIs
StatePublished - 2023
Externally publishedYes
EventMedical Imaging 2023: Physics of Medical Imaging - San Diego, United States
Duration: 19 Feb 202323 Feb 2023

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12463
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2023: Physics of Medical Imaging
Country/TerritoryUnited States
CitySan Diego
Period19/02/2323/02/23

Keywords

  • Anatomy registration
  • X-ray tomosynthesis
  • patient sensing

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