DCE-MRI Analysis Using Sparse Adaptive Representations

  • Gabriele Chiusano
  • Alessandra Staglianò
  • Curzio Basso
  • Alessandro Verri
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7009)

Abstract

Dynamic contrast-enhanced MRI (DCE-MRI) plays an important role as an imaging method for the diagnosis and evaluation of several diseases. Indeed, clinically relevant, per-voxel quantitative information may be extracted through the analysis of the enhanced MR signal. This paper presents a method for the automated analysis of DCE-MRI data that works by decomposing the enhancement curves as sparse linear combinations of elementary curves learned without supervision from the data. Experimental results show that performances in denoising and unsupervised segmentation improve over parametric methods.

Keywords

Juvenile Idiopathic Arthritis Sparse Representation Sparse Code Dictionary Learning Unsupervised Segmentation 
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 2011

Authors and Affiliations

  • Gabriele Chiusano
    • 1
  • Alessandra Staglianò
    • 1
  • Curzio Basso
    • 1
  • Alessandro Verri
    • 1
  1. 1.DISI, Università di GenovaGenovaItaly

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