Skip to main navigation Skip to search Skip to main content

Adaptive beam hardening correction based on projection data consistency condition

  • Shaojie Tang
  • , Xuanqin Mou
  • , Qiong Xu
  • , Yanbo Zhang
  • , Hengyong Yu
  • Wake Forest University
  • Xi'an Jiaotong University
  • VT-WFU School of Biomedical Engineering and Sciences

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

1 Scopus citations

Abstract

In the medical diagnostic computed tomography (CT) systems, the x-ray tube usually emits photons with a polychromatic spectrum, resulting in beam hardening artifacts in the reconstructed images. Although the bone correction method is extensively used to compensate for the beam hardening artifacts, its performance crucially depends on the empirical choice of a scaling factor. To overcome this shortcoming, here we propose two adaptive correction methods, which utilize the Helgasson-Ludwig (H-L) consistency condition to determine the optimal scaling factor and the corresponding coefficient vector. Our numerical simulation results demonstrate the effectiveness of the proposed methods.

Original languageEnglish
Title of host publicationIEEE Nuclear Science Symposuim and Medical Imaging Conference, NSS/MIC 2010
Pages2325-2329
Number of pages5
DOIs
StatePublished - 2010
Event2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010 - Knoxville, TN, United States
Duration: 30 Oct 20106 Nov 2010

Publication series

NameIEEE Nuclear Science Symposium Conference Record
ISSN (Print)1095-7863

Conference

Conference2010 IEEE Nuclear Science Symposium, Medical Imaging Conference, NSS/MIC 2010 and 17th International Workshop on Room-Temperature Semiconductor X-ray and Gamma-ray Detectors, RTSD 2010
Country/TerritoryUnited States
CityKnoxville, TN
Period30/10/106/11/10

Fingerprint

Dive into the research topics of 'Adaptive beam hardening correction based on projection data consistency condition'. Together they form a unique fingerprint.

Cite this