Improved MRI mining by integrating support vector machine priors in the bayesian restoration

  • Authors:

  • D. A. Karras;B. G. Mertzios;D. Graveron-Demilly;D. van Ormondt

  • Affiliations:

  • Chalkis Institute of Technology, Dept. Automation and Hellenic Open University, Athens, Greece;Thessaloniki Institute of Technology, Hellas and Democritus University, Laboratory of Automatic Control Systems, Xanthi, Thessaloniki, Hellas;Laboratoire de RMN, CNRS, UPRESA 5012, Universite LYON I-CPE, France;Applied Physics Department, Delft University of Technology, Delft, The Netherlands

  • Venue:
  • MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
  • Year:
  • 2005

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Abstract

The goal of this paper is to present the development of a new image mining methodology for extracting Magnetic Resonance Images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of Support Vector Machine (SVM) models and Bayesian restoration, in the problem of MR image mining from sparsely sampled k-space, following several different sampling schemes, including spiral and radial. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to make measurement time smaller by reducing scanning trajectories as much as possible. In this way, however, underdetermined equations are introduced and poor image extraction follows. It is suggested here that significant improvements could be achieved, concerning quality of the extracted image, by judiciously applying SVM and Bayesian estimation methods to the k-space data. More specifically, it is demonstrated that SVM neural network techniques could construct efficient priors and introduce them in the procedure of Bayesian restoration. These Priors are independent of specific image properties and probability distributions. They are based on training SVM neural filters to estimate the missing samples of complex k-space and thus, to improve k-space information capacity. Such a neural filter based prior is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction. It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the traditional Bayesian MRI reconstruction approach as well as by the pure Neural Network (NN) filter based reconstruction approach.