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Total liver fat quantification using a 3D respiratory self-navigated Magnetic Resonance Imaging sequence, Magnetic Resonance in medicine

  • Carolina Arboleda
  • , Daniel Aguirre-Reyes
  • , María Paz García
  • , Cristián Tejos
  • , Loreto Andrea Muñoz Hernandez
  • , Juan Francisco Miquel
  • , Pablo Irarrazaval
  • , Marcelo E. Andia
  • , Sergio Uribe
  • Pontificia Universidad Católica de Chile
  • Universidad Técnica Particular de Loja

Research output: Contribution to journalArticlepeer-review

8 Scopus citations

Abstract

Purpose: MRI can produce quantitative liver fat fraction (FF) maps noninvasively, which can help to improve diagnoses of fatty liver diseases. However, most sequences acquire several two-dimensional (2D) slices during one or more breath-holds, which may be difficult for patients with limited breath-holding capacity. A whole-liver 3D FF map could also be obtained in a single acquisition by applying a reliable breathing-motion correction method. Several correction techniques are available for 3D imaging, but they use external devices, interrupt acquisition, or jeopardize the spatial resolution. To overcome these issues, a proof-of-concept study introducing a self-navigated 3D three-point Dixon sequence is presented here. Methods: A respiratory self-gating strategy acquiring a center k-space profile was integrated into a three-point Dixon sequence. We obtained 3D FF maps from a water-fat emulsions phantom and fifteen volunteers. This sequence was compared with multi-2D breath-hold and 3D free-breathing approaches. Results: Our 3D three-point Dixon self-navigated sequence could correct for respiratory-motion artifacts and provided more precise FF measurements than breath-hold multi-2D and 3D free-breathing techniques. Conclusion: Our 3D respiratory self-gating fat quantification sequence could correct for respiratory motion artifacts and yield more-precise FF measurements. Magn Reson Med 76:1400–1409, 2016.

Original languageEnglish
Pages (from-to)1400-1409
Number of pages10
JournalMagnetic Resonance in Medicine
Volume76
Issue number5
DOIs
StatePublished - 2016

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 3 - Good Health and Well-being
    SDG 3 Good Health and Well-being

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