Dependent Nonparametric Bayesian Group Dictionary Learning for online reconstruction of Dynamic MR images
This work addresses the challenge of improving image quality in dynamic MRI for medical imaging applications, representing an incremental advancement in dictionary learning techniques.
The paper tackles the problem of real-time reconstruction of highly undersampled dynamic MR images by introducing a dictionary learning approach that integrates global and local sparsity with prior information, achieving superior reconstruction quality compared to state-of-the-art methods.
In this paper, we introduce a dictionary learning based approach applied to the problem of real-time reconstruction of MR image sequences that are highly undersampled in k-space. Unlike traditional dictionary learning, our method integrates both global and patch-wise (local) sparsity information and incorporates some priori information into the reconstruction process. Moreover, we use a Dependent Hierarchical Beta-process as the prior for the group-based dictionary learning, which adaptively infers the dictionary size and the sparsity of each patch; and also ensures that similar patches are manifested in terms of similar dictionary atoms. An efficient numerical algorithm based on the alternating direction method of multipliers (ADMM) is also presented. Through extensive experimental results we show that our proposed method achieves superior reconstruction quality, compared to the other state-of-the- art DL-based methods.