ArticleMagnetic resonance in medicine2026
Physics-Guided Neural Network for Quantitative Parameter Mapping Using Balanced Steady State Free Precession MRI.
Article in Magnetic resonance in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
purposeTo propose a new method using a physics-guided neural network for quantitative parameter mapping in balanced steady-state free precession (bSSFP) imaging. THEORY AND
methodsWe trained physics-guided neural networks with a multilayer perceptron using simulated bSSFP signals generated from tissue parameters (
resultsThe 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.
conclusionTrained 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
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