Rapid automated liver quantitative susceptibility mapping. Academic Article uri icon

Overview

abstract

  • BACKGROUND: Accurate measurement of the liver iron concentration (LIC) is needed to guide iron-chelating therapy for patients with transfusional iron overload. In this work, we investigate the feasibility of automated quantitative susceptibility mapping (QSM) to measure the LIC. PURPOSE: To develop a rapid, robust, and automated liver QSM for clinical practice. STUDY TYPE: Prospective. POPULATION: 13 healthy subjects and 22 patients. FIELD STRENGTH/SEQUENCES: 1.5 T and 3 T/3D multiecho gradient-recalled echo (GRE) sequence. ASSESSMENT: Data were acquired using a 3D GRE sequence with an out-of-phase echo spacing with respect to each other. All odd echoes that were in-phase (IP) were used to initialize the fat-water separation and field estimation (T2 *-IDEAL) before performing QSM. Liver QSM was generated through an automated pipeline without manual intervention. This IP echo-based initialization method was compared with an existing graph cuts initialization method (simultaneous phase unwrapping and removal of chemical shift, SPURS) in healthy subjects (n = 5). Reproducibility was assessed over four scanners at two field strengths from two manufacturers using healthy subjects (n = 8). Clinical feasibility was evaluated in patients (n = 22). STATISTICAL TESTS: IP and SPURS initialization methods in both healthy subjects and patients were compared using paired t-test and linear regression analysis to assess processing time and region of interest (ROI) measurements. Reproducibility of QSM, R2 *, and proton density fat fraction (PDFF) among the four different scanners was assessed using linear regression, Bland-Altman analysis, and the intraclass correlation coefficient (ICC). RESULTS: Liver QSM using the IP method was found to be ~5.5 times faster than SPURS (P < 0.05) in initializing T2 *-IDEAL with similar outputs. Liver QSM using the IP method were reproducibly generated in all four scanners (average coefficient of determination 0.95, average slope 0.90, average bias 0.002 ppm, 95% limits of agreement between -0.06 to 0.07 ppm, ICC 0.97). DATA CONCLUSION: Use of IP echo-based initialization enables robust water/fat separation and field estimation for automated, rapid, and reproducible liver QSM for clinical applications. LEVEL OF EVIDENCE: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:725-732.

publication date

  • January 13, 2019

Research

keywords

  • Image Interpretation, Computer-Assisted
  • Iron
  • Iron Overload
  • Liver
  • Magnetic Resonance Imaging

Identity

PubMed Central ID

  • PMC6929208

Scopus Document Identifier

  • 85059930628

Digital Object Identifier (DOI)

  • 10.1002/jmri.26632

PubMed ID

  • 30637892

Additional Document Info

volume

  • 50

issue

  • 3