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A Compressed Sensing Approach for MR Tissue Contrast Synthesis

  • Conference paper
Information Processing in Medical Imaging (IPMI 2011)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 6801))

Abstract

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.

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Author information

Authors and Affiliations

  1. Image Analysis and Communication Laboratory, Dept. of Electrical and Computer Engg., The Johns Hopkins University, USA

    Snehashis Roy, Aaron Carass & Jerry Prince

Authors
  1. Snehashis Roy
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  2. Aaron Carass
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  3. Jerry Prince
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Editor information

Editors and Affiliations

  1. Swiss Federal Institute of Technology, Computer Vision Laboratory, Medical Image Analysis and Visualization Group,, ETH-Zentrum, Sternwartstr. 7, 8092, Zurich, Switzerland

    Gábor Székely

  2. Fraunhofer MEVIS, Universitätsallee 29, 28359, Bremen, Germany

    Horst K. Hahn

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© 2011 Springer-Verlag Berlin Heidelberg

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Roy, S., Carass, A., Prince, J. (2011). A Compressed Sensing Approach for MR Tissue Contrast Synthesis. In: Székely, G., Hahn, H.K. (eds) Information Processing in Medical Imaging. IPMI 2011. Lecture Notes in Computer Science, vol 6801. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22092-0_31

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  • DOI: https://doi.org/10.1007/978-3-642-22092-0_31

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-22091-3

  • Online ISBN: 978-3-642-22092-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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