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

Bigong Wang,Liang Li

doi:10.1155/2015/152693

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.

Highlights

Compressive sensing (CS) is a novel theory in information acquisition and processing [1]
After it is applied in medical imaging reconstruction, CS theory is proven to be a method that effectively retains high image quality using undersampling measurement data in different imaging modalities including computed tomography (CT) and magnetic resonance imaging (MRI) [5,6,7]
According to the CS theory, an undersampling image reconstruction problem is to solve an underdetermined system of linear equations Fux = y by minimizing the l0 quasi norm of the sparsified transform Ψx; it means the image x is sparse after a completed sparse transform Ψ ∈ RM×N

Summary

Introduction

Compressive sensing (CS) is a novel theory in information acquisition and processing [1]. Some groups focus on studies of CS applications and have developed various braches such as Bayesian CS and 1-Bit CS [2,3,4] After it is applied in medical imaging reconstruction, CS theory is proven to be a method that effectively retains high image quality using undersampling measurement data in different imaging modalities including computed tomography (CT) and magnetic resonance imaging (MRI) [5,6,7]. Given an initial value x0 (initial dictionary), do dictionary learning using appropriate training method and obtain the sparse representation, and update x under specific transform (i.e., wavelet, Fourier) and output the result after several iterations at last

DDL Algorithm in Image Analysis

DDL Algorithm in Medical Image Reconstruction