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  3. Referenceless Proton Resonance Frequency Thermometry Using Deep Learning With Self-attention.
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  3. Referenceless Proton Resonance Frequency Thermometry Using Deep Learning With Self-attention.

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Referenceless Proton Resonance Frequency Thermometry Using Deep Learning with Self-Attention.

Yueran Zhao, Chang-Sheng Mei, Nathan J McDannold

    IEEE Transactions on Bio-Medical Engineering
    |April 30, 2026

    View abstract on PubMed

    Summary
    This summary is machine-generated.

    A new deep learning method enhances referenceless Proton Resonance Frequency (PRF) MR thermometry for focused ultrasound (FUS) monitoring. This approach improves temperature accuracy in heated regions and reduces errors caused by motion and background phase shifts.

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    Area of Science:

    • Medical Imaging
    • Biophysics
    • Artificial Intelligence

    Background:

    • Proton Resonance Frequency (PRF) MR thermometry is crucial for MR-guided focused ultrasound (FUS) temperature feedback.
    • Conventional methods struggle with inter-scan motion and background phase variations, impacting transcranial FUS accuracy.
    • Existing referenceless techniques are sensitive to susceptibility artifacts, affecting heated regions and causing false positives.

    Purpose of the Study:

    • To develop and evaluate a deep learning-based referenceless PRF thermometry method for transcranial FUS.
    • To enhance accuracy in heated regions and ensure stable background estimation.
    • To improve intra-procedural temperature monitoring reliability.

    Main Methods:

    • A retrospective study included 32 patients undergoing transcranial FUS for essential tremor.
  • A complex-valued, self-attention-augmented U-Net was designed for background complex MR image reconstruction.
  • The model was trained and validated on data from 28 patients, with testing on 4 patients.

    • Main Results:

    • The deep learning method achieved Mean Absolute Error (MAE) of 0.64°C in heated regions, outperforming existing referenceless techniques.
    • High accuracy was demonstrated with Dice coefficient of 0.76 for the 43°C isotherm and minimal background temperature change (MAE = 0.20°C).
    • Bland-Altman analysis showed good agreement (limits of agreement -1.37°C to +1.77°C, R² = 0.99).

    Conclusions:

    • The developed deep learning referenceless PRF thermometry method significantly improves accuracy and stability for transcranial FUS.
    • This technique offers more reliable intra-procedural temperature control, especially in motion-prone applications.
    • The method shows potential for broader use in scenarios where baseline-referenced thermometry is less effective.