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

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.