Improving magnetic resonance resolution with supervised learning

Amod Jog, Aaron Carass, Jerry L. Prince

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

Abstract

Despite ongoing improvements in magnetic resonance (MR) imaging (MRI), considerable clinical and, to a lesser extent, research data is acquired at lower resolutions. For example 1 mm isotropic acquisition of T1-weighted (T1-w) Magnetization Prepared Rapid Gradient Echo (MPRAGE) is standard practice, however T2-weighted (T2-w) - because of its longer relaxation times (and thus longer scan time) - is still routinely acquired with slice thicknesses of 2-5 mm and in-plane resolution of 2-3 mm. This creates obvious fundamental problems when trying to process T1-w and T2-w data in concert. We present an automated supervised learning algorithm to generate high resolution data. The framework is similar to the brain hallucination work of Rousseau, taking advantage of new developments in regression based image reconstruction. We present validation on phantom and real data, demonstrating the improvement over state-of-the-art super-resolution techniques.

Original languageEnglish (US)
Title of host publication2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages987-990
Number of pages4
ISBN (Electronic)9781467319591
DOIs
StatePublished - Jul 29 2014
Event2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 - Beijing, China
Duration: Apr 29 2014May 2 2014

Publication series

Name2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014

Other

Other2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014
CountryChina
CityBeijing
Period4/29/145/2/14

Keywords

  • Brain
  • Image reconstruction
  • MRI
  • Regression
  • Super-resolution

ASJC Scopus subject areas

  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

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  • Cite this

    Jog, A., Carass, A., & Prince, J. L. (2014). Improving magnetic resonance resolution with supervised learning. In 2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014 (pp. 987-990). [6868038] (2014 IEEE 11th International Symposium on Biomedical Imaging, ISBI 2014). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/isbi.2014.6868038