Recent Development of Dual-Dictionary Learning Approach in Medical Image Analysis and Reconstruction

Abstract

As an implementation of compressive sensing (CS), dual-dictionary learning (DDL) method provides an ideal access to restore signals of two related dictionaries and sparse representation. It has been proven that this method performs well in medical image reconstruction with highly undersampled data, especially for multimodality imaging like CT-MRI hybrid reconstruction. Because of its outstanding strength, short signal acquisition time, and low radiation dose, DDL has allured a broad interest in both academic and industrial fields. Here in this review article, we summarize DDL's development history, conclude the latest advance, and also discuss its role in the future directions and potential applications in medical imaging. Meanwhile, this paper points out that DDL is still in the initial stage, and it is necessary to make further studies to improve this method, especially in dictionary training.

Multi-Level Canonical Correlation Analysis for Standard-Dose PET Image Estimation

“…For comparison, the bench-mark methods include SR, coupled dictionary learning (CDL) [20], regression forest (RF) [18]. Fig.…”

“…Recently there have been rapid development in sparse representation (SR) and dictionary learning for medical images [20]. For example, estimating S-PET image from L-PET image can be achieved in patch-based SR by learning a pair of coupled dictionaries from L-PET and S-PET training patches.…”