Assessment of liver fibrosis using fast strain-encoded MRI driven by inherent cardiac motion

Ahmed A. Harouni, Ahmed M. Gharib, Nael F. Osman, Caryn Morse, Theo Heller, Khaled Z. Abd-Elmoniem

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

Purpose An external driver-free MRI method for assessment of liver fibrosis offers a promising noninvasive tool for diagnosis and monitoring of liver disease. Lately, the heart's intrinsic motion and MR tagging have been utilized for the quantification of liver strain. However, MR tagging requires multiple breath-hold acquisitions and substantial postprocessing. In this study, we propose the use of a fast strain-encoded (FSENC) MRI method to measure the peak strain (Sp) in the liver's left lobe, which is in close proximity and caudal to the heart. Additionally, we introduce a new method of measuring heart-induced shear wave velocity (SWV) inside the liver. Methods Phantom and in vivo experiments (11 healthy subjects and 11 patients with liver fibrosis) were conducted. Reproducibility experiments were performed in seven healthy subjects. Results Peak liver strain, Sp, decreased significantly in fibrotic liver compared with healthy liver (6.46% ± 2.27% vs 12.49% ± 1.76%; P < 0.05). Heart-induced SWV increased significantly in patients compared with healthy subjects (0.15 ± 0.04 m/s vs 0.63 ± 0.32 m/s; P < 0.05). Reproducibility analysis yielded no significant difference in Sp (P = 0.47) or SWV (P = 0.56). Conclusion Accelerated external driver-free noninvasive assessment of left liver lobe strain and SWV is feasible using strain-encoded MRI. The two measures significantly separate healthy subjects from patients with fibrotic liver. Magn Reson Med 74:106-114, 2015.

Original languageEnglish (US)
Pages (from-to)106-114
Number of pages9
JournalMagnetic resonance in medicine
Volume74
Issue number1
DOIs
StatePublished - Jul 1 2015

Keywords

  • SENC
  • fibrosis
  • liver motion
  • liver stiffness
  • strain-encoded MRI

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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