Kernel Principal Component Analysis of Coil Compression in Parallel Imaging.

Yuchou Chang,Haifeng Wang

doi:10.1155/2018/4254189

Abstract

A phased array with many coil elements has been widely used in parallel MRI for imaging acceleration. On the other hand, it results in increased memory usage and large computational costs for reconstructing the missing data from such a large number of channels. A number of techniques have been developed to linearly combine physical channels to produce fewer compressed virtual channels for reconstruction. A new channel compression technique via kernel principal component analysis (KPCA) is proposed. The proposed KPCA method uses a nonlinear combination of all physical channels to produce a set of compressed virtual channels. This method not only reduces the computational time but also improves the reconstruction quality of all channels when used. Taking the traditional GRAPPA algorithm as an example, it is shown that the proposed KPCA method can achieve better quality than both PCA and all channels, and at the same time the calculation time is almost the same as the existing PCA method.

Highlights

Parallel imaging methods [1, 2] have been widely used to accelerate MRI acquisitions
We present a principal component analysis (PCA)-based approach, which is a nonlinear extension of the conventional PCA method [10, 19]
A coronary brain image was acquired by using a 2D spin echo (SE) sequence

Summary

Introduction

Parallel imaging methods [1, 2] have been widely used to accelerate MRI acquisitions. A number of coil compression methods have been proposed [6,7,8,9,10,11,12,13,14,15,16,17,18,19] to reduce computational time They can be divided into two categories, one based on the hardware approach [5] and the other based on the software approach [6,7,8,9,10,11,12,13,14,15,16,17,18,19]. The least-squares method is usually used to calculate the coefficients:

Methods

Results

Conclusion