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实体 Bayesian Magnetic Resonance Joint Image Reconstruction and Uncertainty Quantification using Sparsity Prior Models and Markov Chain Monte Carlo Sampling

Bayesian Magnetic Resonance Joint Image Reconstruction and Uncertainty Quantification using Sparsity Prior Models and Markov Chain Monte Carlo Sampling

PulseAugur coverage of Bayesian Magnetic Resonance Joint Image Reconstruction and Uncertainty Quantification using Sparsity Prior Models and Markov Chain Monte Carlo Sampling — every cluster mentioning Bayesian Magnetic Resonance Joint Image Reconstruction and Uncertainty Quantification using Sparsity Prior Models and Markov Chain Monte Carlo Sampling across labs, papers, and developer communities, ranked by signal.

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  1. RESEARCH · CL_93897 ·

    新的AI方法增强了MRI重建和不确定性量化

    两篇新研究论文提出了先进的磁共振成像(MRI)重建方法。第一篇论文介绍了一个利用稀疏性先验和马尔可夫链蒙特卡洛采样的贝叶斯框架,以改进图像重建和量化不确定性,其性能优于基于优化的方法和一些深度学习方法。第二篇论文提出了一种物理驱动的零样本自监督学习方法,该方法结合了物理一致性和非局部图像先验,以增强MRI重建,特别是在高加速因子下,并取得了最先进的结果。