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Abstract #1137

Learning Implicit Neural Representation with Explicit Physical Priors for Accelerated Quantitative $$$\text{T}_{1\rho}$$$ Mapping

Jinwen Xie1,2, Yuanyuan Liu2, Yue Wang2, Zhuo-Xu Cui2, Qingyong Zhu2, Jing Cheng2, Cuihong Li3, Dong Liang2, and Yanjie Zhu2
1Software Engineering Institute, East China Normal University, Shanghai, China, 2Paul C.Lauterbur Research Center For Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Science, Shenzhen, China, 3Hunan University of Information Technology, Hunan, China

Synopsis

Keywords: AI/ML Image Reconstruction, AI/ML Image Reconstruction, Implicit neural representation, Relaxation and self-consistency priors, Parameter mapping

Motivation: Magnetic resonance $$$\text {T}_{1\rho}$$$ mapping provides critical insights into tissue properties for early disease detection, but its clinical use is hindered by long scan times needed for acquiring multiple $$$\text {T}_{1\rho}$$$-weighted images.

Goal(s): This study proposes an unsupervised implicit neural representation (INR) framework for precise $$$\text{T}_{1\rho}$$$ map generation.

Approach: A subject-specific unsupervised method that learns an implicit neural representation of the $$$\text {T}_{1\rho}$$$-weighted images, simultaneously capturing the relationships among $$$\text {T}_{1\rho}$$$-weighted images and multi-channel $$$k$$$-space data.

Results: LINEAR achieves 14-fold acceleration with high accuracy in $$$\text {T}_{1\rho}$$$ map generation, outperforming state-of-the-art unsupervised and traditional methods in artifact suppression and error reduction.

Impact: This study enables accelerated, high-quality $$$\text {T}_{1\rho}$$$ mapping, improving diagnostic efficiency and providing a foundation for future advancements in rapid quantitative imaging, with potential applications across diverse clinical and research fields.

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Keywords