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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