Abstract #1072

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

Haoyang Pei^1,2,3, Jingjia Chen^1,2, Yuhui Huang^1,2, Xiang Xu⁴, Ding Xia⁴, Yao Wang³, Fang Liu⁵, Hersh Chandarana^1,2, Daniel K Sodickson^1,2, and Li Feng^1,2

¹Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York City, NY, United States, ²Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York City, NY, United States, ³Department of Electrical and Computer Engineering, NYU Tandon School of Engineering, New York City, NY, United States, ⁴Biomedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York City, NY, United States, ⁵Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging, Deep Learning

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.

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