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Abstract #2031

PIC-INR: Scan-specific unsupervised IVIM parameter mapping using physics-informed convolutional implicit neural representation

Jiechao Wang1, Lu Wang2, Congbo Cai2, and Shuhui Cai2
1School of Ocean Information Engineering, Fujian Provincial Key Laboratory of Oceanic Information Perception and Intelligent Processing, Jimei University, Xiamen, China, 2Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, Xiamen University, Xiamen, China

Synopsis

Keywords: IVIM, IVIM, Implicit neural representation

Motivation: To eliminate the reliance on abundant high-quality reference maps and to address the inconsistent distribution induced from external training data in learning-based IVIM parameter mapping.

Goal(s): To develop a scan-specific unsupervised learning method that accurately maps IVIM parameters from noisy diffusion-weighted images without depending on external training data.

Approach: The proposed method employs a physics-informed mechanism and convolutional implicit neural representation to generate IVIM parameter maps solely from diffusion-weighted images themselves.

Results: Numerical phantom and in vivo human brain results demonstrate that our proposed method generates accurate IVIM parameter maps with improved quality.

Impact: Our scan-specific unsupervised method, employing physics-informed convolutional implicit neural representation, has successfully achieved accurate IVIM parameter mapping without relying on reference maps and external data during network training.

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