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Abstract #3822

Multi-contrast quantitative mapping with an unsupervised reconstruction method based on implicit neural representation

Guoyan Lao1, Ruimin Feng1, and Hongjiang Wei1,2
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2The National Engineering Research Center of Advanced Magnetic Resonance Technologies for Diagnosis and Therapy (NERC-AMRT), Shanghai Jiao Tong University, Shanghai, China

Synopsis

Keywords: Quantitative Imaging, Multi-Contrast

Motivation: Multi-contrast quantitative MRI usually requires multiple scans, leading to long acquisition time and potential inter-scan misalignment.

Goal(s): To achieve the multi-contrast quantitative MRI acquisition in a single scan and improve the accuracy of the quantitative mapping.

Approach: We developed a multi-contrast quantitative mapping sequence to simultaneously obtain T1, T2, T2* maps and subvoxel QSM. Reconstruction was conducted to directly estimate the underlying quantitative maps from the highly undersampled high-dimensional k-space data. The proposed framework was validated on the simulation, phantom and healthy volunteers.

Results: The results demonstrated that our proposed method exhibited a high correlation with references on the quantitative maps.

Impact: The proposed acquisition and reconstruction framework can simultaneously provide multi-contrast quantitative maps of the whole brain within a 5.8-minute scan. This new technique is clinically promising for tissue characterization and pathological research in neurosciences.

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Keywords