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

To propose a new method using a physics-guided neural network for quantitative parameter mapping in balanced steady-state free precession (bSSFP) imaging.

We trained physics-guided neural networks with a multilayer perceptron using simulated bSSFP signals generated from tissue parameters ( $T_1$ , $T_2$ , $M_{{\mathrm{eff}}_c}$ , ∆f and ${\phi }_{\mathrm{RF}}$ ) uniformly sampled from predefined ranges corresponding to gray matter, white matter, and cerebrospinal fluid. Over 80 million samples were simulated using six phase-cycling angles with L2 loss combining parameter and reconstructed signal terms. The model output was obtained both without and with test-data-specific adaptation. PLANET and CELF were used as comparison methods. Evaluation was conducted using 10 brain digital phantoms from the BrainWeb database and in vivo bSSFP datasets acquired from 10 human subjects on a 3 T scanner using the same scan parameters.

The proposed approaches improved mapping accuracy and consistency visually and quantitatively in both digital phantom and in vivo data compared with PLANET and CELF. Performance with test-data adaptation was better than that without adaptation. The estimated parameters enabled more accurate reconstruction of images at unseen phase-cycling angles and flip angles, suggesting the models' generalization capability.

Trained entirely on simulated data, the proposed physics-guided neural networks enable accurate and efficient multiparameter mapping with high spatial resolution (1.3 × 1.3 × 2.6  ${\mathrm{mm}}^3$ ) from phase-cycled bSSFP within a clinically reasonable scan time of 7 min, offering a promising alternative for MR parameter mapping and data augmentation in training and validating quantitative MRI.