Improved PRF-based MR thermometry using k-space energy spectrum analysis.

Shenyan Zong, Guofeng Shen, Chang-Sheng Mei, Bruno Madore

Magnetic resonance in medicine 2020 Dec

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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. © 2020 International Society for Magnetic Resonance in Medicine.

Citation

Shenyan Zong, Guofeng Shen, Chang-Sheng Mei, Bruno Madore. Improved PRF-based MR thermometry using k-space energy spectrum analysis. Magnetic resonance in medicine. 2020 Dec;84(6):3325-3332