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Abstract #0123

Calibration-free DCE-MRI with Sub-second Temporal Resolution Using Interpretable Implicit Neural Representation

Jie Feng1, Jingjia Chen2,3, Yuyao Zhang4, Li Feng2,3, Dong Liang5, and Hongjiang Wei1
1School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China, 2Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 3Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University Grossman School of Medicine, New York, NY, United States, 4School of Information Science and Technology, ShanghaiTech University, Shanghai, China, 5Research Center for Medical AI, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China

Synopsis

Keywords: Motion Correction, Motion Correction, Implicit Neural Representation

Motivation: Dynamic Contrast-Enhanced MRI (DCE-MRI) requires high temporal resolution to effectively capture motion and contrast dynamics. Current methods rely on pre-estimating temporal information from calibration data, limiting imaging performance.

Goal(s): We aim to develop a novel reconstruction method for achieving sub-second temporal resolution in DCE-MRI without the need for calibration data.

Approach: We introduce an unsupervised Implicit Neural Representation (INR) framework, where spatial coordinates and learnable temporal latent variables are used to model and reconstruct the DCE-MRI data directly.

Results: Our method reconstructs artifact-free images, with even one spoke per frame, and the learned latent variables accurately capture both contrast changes and respiratory motion.

Impact: The proposed method paves the way for artifact-free, high-temporal-resolution DCE-MRI for clinical applications. Moreover, the proposed INR framework makes calibrationless MRI reconstruction feasible and interpretable, offering a powerful tool for a wide range of imaging challenges.

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Keywords