Synthetic MRI Signal Standardization: Application to Multi-atlas Analysis

  • Juan Eugenio Iglesias
  • Ivo Dinov
  • Jaskaran Singh
  • Gregory Tong
  • Zhuowen Tu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6363)


From the image analysis perspective, a disadvantage of MRI is the lack of image intensity standardization. Differences in coil sensitivity, pulse sequence and acquisition parameters lead to very different mappings from tissue properties to image intensity levels. This presents challenges for image analysis techniques because the distribution of image intensities for different brain regions can change substantially from scan to scan. Though intensity correction can sometimes alleviate this problem, it fails in more difficult scenarios in which different types of tissue are mapped to similar gray levels in one scan but different intensities in another. Here, we propose using multi-spectral data to create synthetic MRI scans matched to the intensity distribution of a given dataset using a physical model of acquisition. If the multi-spectral data are manually annotated, the labels can be transfered to the synthetic scans to build a dataset-tailored gold standard. The approach was tested on a multi-atlas based hippocampus segmentation framework using a publicly available database, significantly improving the results obtained with other intensity correction methods.


Coil Sensitivity Joint Histogram Weighted Average Distance Magnetic Resonance Imaging Segmentation Magnetic Resonance Imaging Pulse Sequence 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan Eugenio Iglesias
    • 1
  • Ivo Dinov
    • 2
  • Jaskaran Singh
    • 2
  • Gregory Tong
    • 2
  • Zhuowen Tu
    • 2
  1. 1.Medical Imaging InformaticsUniversity of CaliforniaLos Angeles
  2. 2.Laboratory of NeuroimagingUniversity of CaliforniaLos Angeles

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