Rapid Multi-Parametric Quantitative MRI via Deep Learning-Based Synthetic-to-Real Reconstruction and 3D SSFP-MOLED Imaging.
Authors
Affiliations (8)
Affiliations (8)
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian 361102, China.
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian 361102, China; Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China.
- Department of Radiological Sciences, University of California Irvine, Irvine, CA, 92697, USA.
- Department of Magnetic Resonance Center, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
- Department of Radiology, Zhongshan Hospital (Xiamen), Fudan University, Xiamen, China.
- the Clinical and Technical Solutions, Philips Healthcare, Shenzhen, China.
- Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, Fujian 361102, China. Electronic address: [email protected].
Abstract
Multi-parametric quantitative magnetic resonance imaging (mqMRI) holds significant clinical potential through multi-parametric tissue characterization, yet its adoption is hindered by prolonged scan time and sensitivity to non-ideal signal conditions, especially in high-resolution whole-brain protocols. To address these challenges, we propose a novel signal encoding method integrating phase-modulated three dimensional steady-state free precession with multiple overlapping-echo detachment (3D SSFP-MOLED). This method simultaneously encodes six physiological parameters (M<sub>0</sub>, T<sub>1</sub>, T<sub>2</sub>, T<sub>2</sub>*, B<sub>1</sub><sup>+</sup>, ΔB<sub>0</sub>) into k-space by controlling overlapping echo detachment in signal acquisition. A physics-constrained synthetic data pipeline was developed to simulate MR signal evolutions with realistic field variations (ΔB<sub>0</sub>, B<sub>1</sub><sup>+</sup> inhomogeneities), enabling robust training of network for real-time parameter mapping. Whole-brain parametric maps (1×1×2 mm³ resolution) can be delivered within 3 minutes with only 2x parallel acquisition acceleration. Validation was performed on phantom, healthy volunteers, and clinical cases with tumors/hemorrhage. Experimental results show that our method can achieve rapid multi-parametric quantitation with high accuracy and reproducibility. By synergizing adaptive signal encoding, physics-informed synthetic training, and reproducible deep learning reconstruction, this work establishes a new paradigm for efficient and reliable mqMRI in clinical signal processing applications.