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Multipeak fat-corrected complex R2* relaxometry: Theory, optimization, and clinical validation

Authors

  • Diego Hernando,

    Corresponding author
    1. Departments of Radiology, University of Wisconsin, Madison, Wisconsin, USA
    • Department of Radiology, University of Wisconsin, L1115 WIMR, 1111 Highland Avenue, Madison, WI 53705. E-mail: dhernando@wisc.edu

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  • J. Harald Kramer,

    1. Departments of Radiology, University of Wisconsin, Madison, Wisconsin, USA
    2. Institute for Clinical Radiology, Ludwig-Maximilians-University Hospital, Munich, Germany
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  • Scott B. Reeder

    1. Departments of Radiology, University of Wisconsin, Madison, Wisconsin, USA
    2. Department of Medical Physics, University of Wisconsin, Madison, Wisconsin, USA
    3. Department of Biomedical Engineering, University of Wisconsin, Madison, Wisconsin, USA
    4. Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA
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Abstract

Purpose

To develop R2* mapping techniques corrected for confounding factors and optimized for noise performance.

Theory and Methods

Conventional R2* mapping is affected by two key confounding factors: noise-related bias and the presence of fat in tissue. Noise floor effects introduce bias in magnitude-based reconstructions, particularly at high R2* values. The presence of fat, if uncorrected, introduces severe protocol-dependent bias. In this work, the bias/noise properties of different R2* mapping reconstructions (magnitude- and complex-fitting, fat-uncorrected, and fat-corrected) are characterized using Cramer-Rao Bound analysis, simulations, and in vivo data. A framework for optimizing the choice of echo times is provided. Finally, the robustness of liver R2* mapping in the presence of fat is evaluated in 28 subjects.

Results

Fat-corrected R2* mapping removes fat-related bias without noise penalty over a wide range of R2* values. Complex nonlinear least-squares fitted and fat-corrected R2* reconstructions that account for the spectral complexity of fat provide robust R2* estimates with low bias and optimized noise performance over a wide range of echo times combinations and R2* values.

Conclusion

The use of complex fitting and fat-correction improves the robustness, noise performance, and accuracy of R2* measurements, and are necessary to establish R2* as quantitative imaging biomarker in the liver. Magn Reson Med 70:1319–1331, 2013. © 2013 Wiley Periodicals, Inc.

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