CT metal artifact reduction using MR image patches

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2018Researchpeer-review

Documents

DOI

View graph of relations

Metal implants give rise to metal artifacts in computed tomography (CT) images, which may lead to diagnostic errors and erroneous CT number estimates when the CT is used for radiation therapy planning. Methods for reducing metal artifacts by exploiting the anatomical information provided by coregistered magnetic resonance (MR) images are of great potential value, but remain technically challenging due to the poor contrast between bone and air on the MR image. In this paper, we present a novel MR-based algorithm for automatic CT metal artifact reduction (MAR), referred to as kerMAR. It combines kernel regression on known CT value/MR patch pairs in the uncorrupted patient volume with a forward model of the artifact corrupted values to estimate CT replacement values. In contrast to pseudo-CT generation that builds on multi-patient modelling, the algorithm requires no MR intensity normalisation or atlas registration. Image results for 7 head-and-neck radiation therapy patients with T1-weighted images acquired in the same fixation as the RT planning CT suggest a potential for more complete MAR close to the metal implants than the oMAR algorithm (Philips) used clinically. Our results further show improved performance in air and bone regions as compared to other MR-based MAR algorithms. In addition, we experimented with using kerMAR to define a prior for iterative reconstruction with the maximum likelihood transmission reconstruction algorithm, however with no apparent improvements.
Original languageEnglish
Title of host publicationProceedings of SPIE
Number of pages10
Volume10573
PublisherSPIE - International Society for Optical Engineering
Publication date2018
Article number105730P
DOIs
Publication statusPublished - 2018
EventSPIE Medical Imaging 2018 - Houston, United States
Duration: 10 Feb 201815 Feb 2018

Conference

ConferenceSPIE Medical Imaging 2018
CountryUnited States
CityHouston
Period10/02/201815/02/2018
SeriesProceedings of SPIE, the International Society for Optical Engineering
ISSN0277-786X
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Computed Tomography, Metal Artifact Reduction, Bayesian modeling, Radiation Therapy

Download statistics

No data available

ID: 151667243