With a gradual increase in breast cancer incidence and mortality rates and an urgent need to improve patient prognosis and cosmetology, magnetic resonance imaging (MRI)-guided radiofrequency ablation (RFA) therapy has attracted wide attention as a new treatment method for breast cancer. MRI-RFA results in a higher complete ablation rate and extremely low recurrence and complication rates. Thus, it may be used as an independent treatment for breast cancer or adjuvant to breast-conserving surgery to reduce the extent of breast resection. Furthermore, with MRI guidance, accurate control of RFA can be achieved, and breast cancer treatment can enter a new stage of minimally invasive, safe, and comprehensive therapy. With progress in MR thermometry technology, the applications of MRI are expected to broaden.

Proton resonance frequency (PRF) thermometry encodes information in the phase of MRI signals. A multiplicative factor converts phase changes into temperature changes, and this factor includes the TE. However, phase variations caused by B0 and/or B1 inhomogeneities can effectively change TE in ways that vary from pixel to pixel. This work presents how spatial phase variations affect temperature maps and how to correct for corresponding errors.
A method called \"k-space energy spectrum analysis\" was used to map regions in the object domain to regions in the k-space domain. Focused ultrasound heating experiments were performed in tissue-mimicking gel phantoms under two scenarios: with and without proper shimming. The second scenario, with deliberately de-adjusted shimming, was meant to emulate B0 inhomogeneities in a controlled manner. The TE errors were mapped and compensated for using k-space energy spectrum analysis, and corrected results were compared with reference results. Furthermore, a volunteer was recruited to help evaluate the magnitude of the errors being corrected.
The in vivo abdominal results showed that the TE and heating errors being corrected can readily exceed 10%. In phantom results, a linear regression between reference and corrected temperatures results provided a slope of 0.971 and R2 of 0.9964. Analysis based on the Bland-Altman method provided a bias of -0.0977°C and 95% limits of agreement that were 0.75°C apart.
Spatially varying TE errors, such as caused by B0 and/or B1 inhomogeneities, can be detected and corrected using the k-space energy spectrum analysis method, for increased accuracy in proton resonance frequency thermometry.

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.
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.
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.
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.

OBJECTIVE: To describe how B0 inhomogeneities can cause errors in proton resonance frequency (PRF) shift thermometry, and to correct for these errors.
METHODS: With PRF thermometry, measured phase shifts are converted into temperature measurements through the use of a scaling factor proportional to the echo time, TE. However, B0 inhomogeneities can deform, spread, and translate MR echoes, potentially making the \"true\" echo time vary spatially within the imaged object and take on values that differ from the prescribed TE value. Acquisition and reconstruction methods able to avoid or correct for such errors are presented.
RESULTS: Tests were performed in a gel phantom during sonication, and temperature measurements were made with proper shimming as well as with intentionally introduced B0 inhomogeneities. Errors caused by B0 inhomogeneities were observed, described, and corrected by the proposed methods. No statistical difference was found between the corrected results and the reference results obtained with proper shimming, while errors by more than 10% in temperature elevation were corrected for. The approach was also applied to an abdominal in vivo dataset.
CONCLUSIONS: Field variations induce errors in measured field values, which can be detected and corrected. The approach was validated for a PRF thermometry application.