Dynamic compressive magnetic resonance imaging using a Gaussian scale mixtures model

Yookyung Kim, Mariappan S. Nadar, Ali Bilgin

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

Abstract

Dynamic magnetic resonance imaging (MRI) is commonly used to observe dynamic physiological changes in tissue or to study organs with mobile structures such as the heart. In order to accurately capture spatiotemporal changes, it is desirable to have dynamic images with high temporal resolution in addition to high spatial resolution. Due to the nature of data acquisition in current MRI systems, there exists a trade-off between temporal and spatial resolution. In this work, we present two methods for improving the spa-tiotemporal resolution in dynamic MRI using compressive sampling (CS). Experimental results illustrate that the proposed Bayes least squares-Gaussian scale mixtures (BLS-GSM) model-based CS algorithm compares favorably with other state-of-the-art compressive dynamic MRI techniques.

Original languageEnglish (US)
Title of host publicationProceedings - International Conference on Image Processing, ICIP
Pages2293-2296
Number of pages4
DOIs
Publication statusPublished - 2011
Event2011 18th IEEE International Conference on Image Processing, ICIP 2011 - Brussels, Belgium
Duration: Sep 11 2011Sep 14 2011

Other

Other2011 18th IEEE International Conference on Image Processing, ICIP 2011
CountryBelgium
CityBrussels
Period9/11/119/14/11

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Keywords

  • compressed sensing
  • dynamic MRI
  • Gaussian scale mixtures
  • wavelets

ASJC Scopus subject areas

  • Software
  • Computer Vision and Pattern Recognition
  • Signal Processing

Cite this

Kim, Y., Nadar, M. S., & Bilgin, A. (2011). Dynamic compressive magnetic resonance imaging using a Gaussian scale mixtures model. In Proceedings - International Conference on Image Processing, ICIP (pp. 2293-2296). [6116097] https://doi.org/10.1109/ICIP.2011.6116097