A compressed sensing approach for MR tissue contrast synthesis

Snehashis Roy, Aaron Carass, Jerry Prince

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

54 Scopus citations


The tissue contrast of a magnetic resonance (MR) neuroimaging data set has a major impact on image analysis tasks like registration and segmentation. It has been one of the core challenges of medical imaging to guarantee the consistency of these tasks regardless of the contrasts of the MR data. Inconsistencies in image analysis are attributable in part to variations in tissue contrast, which in turn arise from operator variations during image acquisition as well as software and hardware differences in the MR scanners. It is also a common problem that images with a desired tissue contrast are completely missing in a given data set for reasons of cost, acquisition time, forgetfulness, or patient comfort. Absence of this data can hamper the detailed, automatic analysis of some or all data sets in a scientific study. A method to synthesize missing MR tissue contrasts from available acquired images using an atlas containing the desired contrast and a patch-based compressed sensing strategy is described. An important application of this general approach is to synthesize a particular tissue contrast from multiple studies using a single atlas, thereby normalizing all data sets into a common intensity space. Experiments on real data, obtained using different scanners and pulse sequences, show improvement in segmentation consistency, which could be extremely valuable in the pooling of multi-site multi-scanner neuroimaging studies.

Original languageEnglish (US)
Title of host publicationInformation Processing in Medical Imaging - 22nd International Conference, IPMI 2011, Proceedings
Number of pages13
StatePublished - 2011
Event22nd International Conference on Information Processing in Medical Imaging, IPMI 2011 - Kloster Irsee, Germany
Duration: Jul 3 2011Jul 8 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6801 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Other22nd International Conference on Information Processing in Medical Imaging, IPMI 2011
CityKloster Irsee


  • compressed sensing
  • histogram equalization
  • histogram matching
  • image synthesis
  • intensity normalization
  • magnetic resonance imaging (MRI)
  • phantom
  • segmentation
  • standardization

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science


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