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Abstract #2105

Accelerating the Whole-Brain Multi-Parametric Imaging through Joint Deep Learning Reconstruction and Physical Model Integration

Jiaying Zhao1,2, Yongquan Ye3, Jing Cheng4, Yuanyuan Liu4, Ye Li4, Jian Xu3, Dong Liang1,4, and Sen Jia4
1Medical AI Research Center, Shenzhen Institutes of Advanced Technology, Shenzhen, China, 2University of Chinese Academy of Sciences, Beijing, China, 3UIH America, Inc., Houston, TX, United States, 4Paul C. Lauterbur Research Center for Biomedical lmaging, Shenzhen Institute of Advanced Technology, Shenzhen, China

Synopsis

Keywords: Quantitative Imaging, Quantitative Imaging, Multitasking

Motivation: The long scan time of whole brain multi-parametric imaging limits the achievable spatial resolution and clinical application

Goal(s): To develop a deep learning method that enhance the accuracy of reconstruction and quantification in 3D high-resolution multi-parametric imaging while significantly reducing scan time.

Approach: This work introduced Joint DeepMTP, a multi-contrast joint deep learning model integrated with MR physical model, to accelerate the acquisition and imaging time

Results: The proposed method achieved comparable reconstruction and quantification performance to the reference at 9-fold CAIPI acceleration, with a reconstruction time of 3 minutes

Impact: The proposed method accelerated 3D whole-brain multi-parametric imaging while simultaneously quantifying T1/T2*/QSM/PD, benefiting clinicians with faster, high-resolution scans.

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