Dual‐step iterative temperature estimation method for accurate and precise fat‐referenced PRFS temperature imaging

PURPOSE: The aim of this study was to propose dual-step iterative temperature estimation (DITE) of a fat-referenced proton resonance frequency shift (PRFS) method to improve both the accuracy and precision of temperature estimations in fat-containing tissues. METHODS: A fat-water signal model with multiple fat peaks was used to simultaneously estimate the temperature, fat/water intensity and T 2 ∗ , and field offset. In DITE, model fitting was implemented with alternating 2-step minimizations. The estimated temperature map was smoothed between the 2-step minimizations, which is considered to be the most important step for improving the temperature precision. The performance of DITE was evaluated with a Monte Carlo simulation, fat/water phantoms, and ex vivo brown adipose tissue experiments and then compared with the performance of previous fat-referenced proton resonance frequency shift methods. RESULTS: In fat/water phantom experiment with a smooth temperature profile, the temperatures estimated by DITE are consistent with the thermometer results and present a better accuracy and precision than those of previous fat-referenced proton resonance frequency shift methods. In the brown adipose tissue heating experiment, the average mean error, SD, and RMS error were -0.08oC, 0.46oC, and 0.56oC, respectively, over all of the measurements within the region of interest. CONCLUSION: Our proposed DITE method improves both the accuracy and precision of temperature measurements in tissues with fat fractions between 20% and 80% under smooth distribution of the temperature profile and represents a simple fat-referenced thermometry method.