Dependent nonparametric bayesian group dictionary learning for online reconstruction of dynamic MR images
作者：
Highlights：
• A novel dictionary learning-based algorithm for online reconstruction of dynamic MR images is proposed.
• The algorithm consists of both patch-based (local) and global sparsity terms.
• The group patching is employed to classify the patches based on their similarities.
• A modified dependent hierarchical beta process (dHBP) is utilized as the prior for the dictionary learning process.
摘要
Highlights•A novel dictionary learning-based algorithm for online reconstruction of dynamic MR images is proposed.•The algorithm consists of both patch-based (local) and global sparsity terms.•The group patching is employed to classify the patches based on their similarities.•A modified dependent hierarchical beta process (dHBP) is utilized as the prior for the dictionary learning process.
论文关键词：Dynamic 3D MRI,Image reconstruction,Dictionary learning,Compressive sensing
论文评审过程：Received 30 January 2016, Revised 23 June 2016, Accepted 21 September 2016, Available online 28 September 2016, Version of Record 27 November 2016.
论文官网地址：https://doi.org/10.1016/j.patcog.2016.09.038
