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Related Experiment Video

Updated: May 27, 2026

A Cognitive Fusion-guided Prostate Biopsy Using Multiparametric Magnetic Resonance Imaging and Transrectal Ultrasound
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Physics-Informed Prostate MR-US Registration by Biomechanical Fields Prediction Network.

Shixing Ma, Zhaoxi Lin, Xinzhe Du

    IEEE Journal of Biomedical and Health Informatics
    |May 25, 2026
    PubMed
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    This study introduces a Biomechanical Fields Prediction Physics-Informed Neural Network (BFP-PINN) for prostate image registration. The Deformation strategy balances accuracy and plausibility, outperforming other physics-embedding methods.

    Area of Science:

    • Medical Image Analysis
    • Computational Biomechanics
    • Machine Learning in Medicine

    Background:

    • Physiologically credible soft-tissue motion in medical image registration requires biomechanical priors.
    • Optimal strategies for integrating these priors into learning-based models are not well-established.

    Purpose of the Study:

    • To propose and evaluate a unified framework, the Biomechanical Fields Prediction Physics-Informed Neural Network (BFP-PINN), for prostate MR-TRUS point-set registration.
    • To systematically investigate and compare different physics-embedding mechanisms within a learning-based registration framework.

    Main Methods:

    • Developed a BFP-PINN framework for prostate MR-TRUS point-set registration.
    • Implemented and compared three physics-embedding strategies: Deformation (Navier-Cauchy residuals), Strain (displacement and strain prediction), and StressStrain (displacement, stress, and strain prediction).

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    Last Updated: May 27, 2026

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  • Conducted experiments on simulated and clinical datasets to assess geometric accuracy and biomechanical plausibility.

    • Main Results:

    • The Deformation strategy, using strong-form Navier-Cauchy residuals, demonstrated the best balance between geometric accuracy and biomechanical plausibility.
    • Compared to Strain and StressStrain strategies, the Deformation approach showed superior performance in the evaluated setting.
    • Extensive experiments validated the effectiveness of the proposed framework and strategies.

    Conclusions:

    • The Deformation strategy offers a practical and effective approach for enforcing biomechanical priors in learning-based medical image registration.
    • Direct residual constraints, as used in the Deformation strategy, provide better optimization stability for clinical registration tasks compared to coupled-field predictions.
    • The BFP-PINN framework offers a unified approach for investigating physics-embedding mechanisms in medical image registration.