DeepGrasp-Quant: A General Framework for Deep Learning-Enabled Quantitative Imaging Based on Golden-Angle Radial Sparse Parallel MRI

Motivation: Quantitative MRI typically involves a multi-step imaging pipeline from data acquisition to parameter estimation. Deep learning holds great promise to improve and streamline the entire workflow for quantitative MRI. Goal(s): This work presents a general deep learning-based rapid quantitative MRI framework, called DeepGrasp-Quant, for efficient and accurate quantification of MRI parameters based on Golden-angle RAdial Sparse Parallel (GRASP) MRI. Approach: DeepGrasp-Quant was designed with cascaded deep learning modules for reconstruction and parameter fitting, enabling direct estimation of MR parameters from undersampled images. Results: Two examples of DeepGrasp-Quant (DeepGrasp-T1 and DeepGrasp-T1-Dixon) were demonstrated for rapid accurate T1 mapping of the brain and the liver. Impact: DeepGrasp-Quant is expected to be a promising technique for efficient and accurate quantification of MRI parameters from highly-accelerated free-breathing data acquisition. In addition to T1 mapping, it can also be integrated with other quantitative MRI methods for different clinical applications.