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Abstract #3820

Coordinate-Based Neural Representation for Motion-Robust 3D Multiparametric Quantitative MRI with Fat Navigators

Guoyan Lao1, Xiaopeng Zong2, Yuyao Zhang3, and Hongjiang Wei1,4
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2School of Biomedical Engineering, ShanghaiTech University, Shanghai, China, 3School of Information Science and Technology, ShanghaiTech University, Shanghai, China, 4National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction

Motivation: Multiparametric quantitative MRI is susceptible to potential motion due to the lengthy scan times.

Goal(s): To develop a motion-robust multiparametric quantitative mapping technique with fat navigators for neuroimaging.

Approach: We developed a multiparametric quantitative MRI sequence integrated with fat navigators. Motion information was extracted from highly-accelerated fat images and incorporated into the reconstruction process. We modeled the quantitative maps as continuous functions of motion-informed coordinates and directly decoded the motion-corrected maps from the corrupted k-space in an unsupervised manner.

Results: Our method can yield motion-robust T1, T2, and T2* maps with significantly reduced artifacts.

Impact: The proposed method can simultaneously generate motion-robust multiparametric quantitative maps of the whole brain without the need for k-space correction, increasing the clinical usability of multiparametric quantitative MRI.

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