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Physics-Guided Neural Network for Quantitative Parameter Mapping Using Balanced Steady State Free Precession MRI.

Hye-Ryeong Choi1, Huan Minh Luu1, Sung-Hong Park1

  • 1Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Republic of Korea.

Magnetic Resonance in Medicine
|May 9, 2026
PubMed
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Physics-guided neural networks offer accurate multiparameter mapping in balanced steady-state free precession (bSSFP) imaging. This method provides high-resolution quantitative MRI data efficiently, improving diagnostic capabilities.

Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Quantitative MRI

Background:

  • Balanced steady-state free precession (bSSFP) is a widely used MRI sequence.
  • Accurate quantitative parameter mapping (e.g., T1, T2) is crucial for diagnosing various pathologies.
  • Traditional methods for bSSFP parameter mapping can be time-consuming and may lack accuracy.

Purpose of the Study:

  • To introduce a novel physics-guided neural network (PGNN) approach for quantitative parameter mapping in bSSFP imaging.
  • To enhance the accuracy and efficiency of multiparameter mapping using simulated and in vivo data.
  • To evaluate the performance of PGNNs against existing methods like PLANET and CELF.

Main Methods:

  • Trained multilayer perceptron PGNNs using over 80 million simulated bSSFP signals.
Keywords:
balanced steady‐state free precession (bSSFP)ellipse fitting methodphase‐cyclingphysics‐guided neural networksimultaneous quantitative mapping

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  • Incorporated tissue parameters (T1, T2, M_eff_c, Δf, φ_RF) and six phase-cycling angles during training.
  • Evaluated the model on digital brain phantoms and in vivo human subject data acquired at 3T, with and without test-data adaptation.

    • Main Results:

    • The proposed PGNN method demonstrated superior accuracy and consistency in quantitative parameter mapping compared to PLANET and CELF.
    • Test-data adaptation further improved the performance of the PGNN model.
    • The estimated parameters allowed for accurate reconstruction of images at unseen parameters, indicating strong generalization capabilities.

    Conclusions:

    • Physics-guided neural networks trained on simulated data provide accurate and efficient multiparameter mapping from phase-cycled bSSFP.
    • Achieved high spatial resolution (1.3x1.3x2.6 mm³) within a 7-minute scan time.
    • PGNNs represent a promising alternative for quantitative MRI parameter mapping and data augmentation for training and validation.