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Complex confounder-corrected R2* mapping for liver iron quantification with MRI

European Radiology volume 31, pages 264–275 (2021)Cite this article

Abstract

Objectives

MRI-based R2* mapping may enable reliable and rapid quantification of liver iron concentration (LIC). However, the performance and reproducibility of R2* across acquisition protocols remain unknown. Therefore, the objective of this work was to evaluate the performance and reproducibility of complex confounder-corrected R2* across acquisition protocols, at both 1.5 T and 3.0 T.

Methods

In this prospective study, 40 patients with suspected iron overload and 10 healthy controls were recruited with IRB approval and informed written consent and imaged at both 1.5 T and 3.0 T. For each subject, acquisitions included four different R2* mapping protocols at each field strength, and an FDA-approved R2-based method performed at 1.5 T as a reference for LIC. R2* maps were reconstructed from the complex data acquisitions including correction for noise effects and fat signal. For each subject, field strength, and R2* acquisition, R2* measurements were performed in each of the nine liver Couinaud segments and the spleen. R2* measurements were compared across protocols and field strength (1.5 T and 3.0 T), and R2* was calibrated to LIC for each acquisition and field strength.

Results

R2* demonstrated high reproducibility across acquisition protocols (p > 0.05 for 96/108 pairwise comparisons across 2 field strengths and 9 liver segments, ICC > 0.91 for each field strength/segment combination) and high predictive ability (AUC > 0.95 for four clinically relevant LIC thresholds). Calibration of R2* to LIC was LIC = − 0.04 + 2.62 × 10−2 R2* at 1.5 T and LIC = 0.00 + 1.41 × 10−2 R2* at 3.0 T.

Conclusions

Complex confounder-corrected R2* mapping enables LIC quantification with high reproducibility across acquisition protocols, at both 1.5 T and 3.0 T.

Key Points

• Confounder-corrected R2* of the liver provides reproducible R2* across acquisition protocols, including different spatial resolutions, echo times, and slice orientations, at both 1.5 T and 3.0 T.

• For all acquisition protocols, high correlation with R2-based liver iron concentration (LIC) quantification was observed.

• The calibration between confounder-corrected R2* and LIC, at both 1.5 T and 3.0 T, is determined in this study.

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Abbreviations

AA:

Aplastic anemia

ALL:

Acute lymphoblastic leukemia

AML:

Acute myeloid leukemia

CSE:

Chemical shift-encoded

GRE:

Gradient-recalled echo

HH:

Hereditary hemochromatosis

IRB:

Institutional review board

LIC:

Liver iron concentration

MDS:

Myelodysplastic syndrome

NOS:

Not otherwise specified

SCD:

Sickle cell disease

TH:

Transfusional hemosiderosis

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Funding

This study has received funding from the WARF Accelerator Program, from the NIH (R01 DK100651, K24 DK102595, R01 DK083380, R01 DK117354), as well as from GE Healthcare who provides research support to UW-Madison. Furthermore, Dr. Reeder is a Romnes Faculty Fellow and has received an award provided from the University of Wisconsin-Madison Office of the Vice-Chancellor of Research and Graduate Education with funding from the Wisconsin Alumni Research Foundation.

Author information

Authors and Affiliations

  1. Department of Radiology, University of Wisconsin-Madison, Wisconsin Institutes for Medical Research, Rm 2474, 1111 Highland Ave, Madison, WI, 53705, USA

    Diego Hernando, Naila Qazi & Scott B. Reeder

  2. Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA

    Diego Hernando & Scott B. Reeder

  3. Department of Medicine, University of Wisconsin-Madison, Madison, WI, USA

    Rachel J. Cook & Scott B. Reeder

  4. Department of Medicine, Oregon Health Sciences University, Portland, OR, USA

    Rachel J. Cook

  5. Department of Radiology, Avera Medical Group, Yankton, SD, USA

    Naila Qazi

  6. Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, USA

    Colin A. Longhurst

  7. Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA

    Carol A. Diamond

  8. Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA

    Scott B. Reeder

  9. Emergency Medicine, University of Wisconsin-Madison, Madison, WI, USA

    Scott B. Reeder

Authors
  1. Diego Hernando
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  2. Rachel J. Cook
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  3. Naila Qazi
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  4. Colin A. Longhurst
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  5. Carol A. Diamond
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  6. Scott B. Reeder
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Corresponding author

Correspondence to Diego Hernando.

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Guarantor

The scientific guarantor of this publication is Diego Hernando, PhD.

Conflict of interest

The authors of this manuscript declare relationships with the following companies: Dr. Hernando is a cofounder of Calimetrix. Dr. Reeder consults for ArTara Therapeutics and HeartVista. Dr. Reeder is a cofounder of Calimetrix.

Statistics and biometry

One of the coauthors (Colin Longhurst, MS, UW Department of Biostatistics and Medical Informatics) has significant statistical expertise.

Informed consent

Written informed consent was obtained from all subjects (patients) in this study.

Ethical approval

Institutional Review Board approval was obtained.

Study subjects or cohorts overlap

Some study subjects or cohorts have been previously reported. Hernando et al (Magnetic resonance in medicine 70 (3), 648–656) demonstrated the feasibility of quantifying liver iron concentration from measured B0 field maps. Sharma et al (Magnetic resonance in medicine 74 (3), 673–683) validated a method for quantitative susceptibility mapping in the liver. Horng et al (Journal of Magnetic Resonance Imaging 45 (2), 428–439) evaluated the accuracy of a single-R2* signal model for fat quantification in the presence of liver iron overload.

Methodology

• prospective

• cross-sectional

• single-institution study.

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Hernando, D., Cook, R.J., Qazi, N. et al. Complex confounder-corrected R2* mapping for liver iron quantification with MRI. Eur Radiol 31, 264–275 (2021). https://doi.org/10.1007/s00330-020-07123-x

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  • DOI: https://doi.org/10.1007/s00330-020-07123-x

Keywords

  • Magnetic resonance imaging
  • Liver
  • Iron overload
  • Biomarkers