Keywords: Machine Learning/Artificial Intelligence, Arterial spin labelling, Denoising, Diffusion Model
Motivation: ASL images often have low SNR. Improving their SNR can enhance the practical utility of ASL in various brain imaging applications.
Goal(s): To develop a deep learning-based ASL denoising method with very limited training data available.
Approach: To generate diverse sets of ASL training images for deep-learning denoiser for special ASL sequences, we employed Schrödinger-Bridge-based networks to map the large public ASL datasets to match both intensity and noise distributions of target ASL sequence. The training data was used to train deep-learning models for ASL denoising.
Results: The proposed method was evaluated using simulation and in vivo experimental data, showing excellent denoising performance.
Impact: Our proposed method addresses the issue of limited target ASL training datasets for deep learning-based ASL denoising and demonstrates excellent denoising performance. It can be generalized for the practical utility in both research and clinical applications.
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