Regularized super-resolution of brain MRI

Avraham Ben-Ezra, Hayit Greenspan, Yossi Rubner

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

Abstract

In recent years super-resolution (S-R) methods are starting to emerge in the field of medical imaging for the reconstruction of isotropic images with increased slice resolution. Use of the maximal likelihood S-R estimator is not advisable as the S-R reconstruction is an ill-posed problem. Regularizing the S-R algorithm using specific apriori knowledge may compensate for missing measurement information and improve the resolved result. In this work two novel regularization methods are proposed, utilizing domain-specific spatial and intensity constraints on brain MRI data. Experiments indicate that the proposed methods eliminate disadvantages of common regularization methods and outperform these methods with better edge definition and increased image quality.

Original languageEnglish
Title of host publicationProceedings - 2009 IEEE International Symposium on Biomedical Imaging
Subtitle of host publicationFrom Nano to Macro, ISBI 2009
Pages254-257
Number of pages4
DOIs
StatePublished - 2009
Event2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009 - Boston, MA, United States
Duration: 28 Jun 20091 Jul 2009

Publication series

NameProceedings - 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009

Conference

Conference2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, ISBI 2009
Country/TerritoryUnited States
CityBoston, MA
Period28/06/091/07/09

Keywords

  • Biomedical image processing
  • Brain modeling
  • Magnetic resonance imaging
  • Superresolution

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