Denoising of ASL Data Using Deep Learning Priors Generated From Distribution Remapping

作者：Ziyang Xu, Rong Guo, Ziwen Ke, Yudu Li, Yibo Zhao, Wen Jin, Ruihao Liu, Ziyu Meng, Yao Li, Zhi‐Pei Liang · 发表于：Magnetic Resonance in Medicine · 年份：2026 · DOI：10.1002/mrm.70471 · 研究领域：Advanced MRI Techniques and Applications、Cerebrovascular and Carotid Artery Diseases、Cardiovascular Health and Disease Prevention

ABSTRACT Purpose To develop an effective deep learning (DL)–based method to denoise arterial spin labeling (ASL) data. Methods Conventional DL–based ASL denoising methods often suffer from overfitting and poor generalization when training data are limited. The proposed method overcame this problem using two strategies: (i) perform data augmentation to create large training data and (ii) denoise in‐distribution and out‐of‐distribution components of the target perfusion‐weighted image separately. Specifically, Image‐to‐Image Schrödinger Bridge (I 2 SB)–based distribution remapping transforms were applied to the large public ASL datasets so that their intensity distribution matched that of the data to be denoised. U‐Net–based DL denoisers were trained on the remapped data to capture in‐distribution features. High‐SNR outputs from the DL‐denoiser were incorporated into a Bayesian model to reconstruct the out‐of‐distribution features with sparsity constraints, generating denoised images for cerebral blood flow (CBF) quantification. Results Simulation studies highlighted the importance of distribution remapping for effective data augmentation in limited‐data scenarios. Both simulation and in vivo experiments showed that the proposed method outperformed state‐of‐the‐art approaches, achieving an average SNR improvement of approximately 7 dB. Evaluations on multiple datasets confirmed robust and generalizable performance across different ASL sequences and imaging protocols. To demonst...