Multi-echo Reconstruction from Partial K-space Scans via Adaptively Learnt Basis

TL;DR

This paper introduces an adaptive basis learning approach, using dictionary and transform learning models, to improve multi-echo MRI reconstruction from partial k-space data, outperforming traditional compressed sensing methods.

Contribution

It adapts basis learning techniques specifically for multi-echo MRI, incorporating scan structure to enhance reconstruction quality beyond existing methods.

Findings

Significant improvement over compressed sensing techniques.

Effective incorporation of multi-echo scan structure.

Enhanced reconstruction quality demonstrated.

Abstract

In multi echo imaging, multiple T1/T2 weighted images of the same cross section is acquired. Acquiring multiple scans is time consuming. In order to accelerate, compressed sensing based techniques have been proposed. In recent times, it has been observed in several areas of traditional compressed sensing, that instead of using fixed basis (wavelet, DCT etc.), considerably better results can be achieved by learning the basis adaptively from the data. Motivated by these studies, we propose to employ such adaptive learning techniques to improve reconstruction of multi-echo scans. This work will be based on two basis learning models synthesis (better known as dictionary learning) and analysis (known as transform learning). We modify these basic methods by incorporating structure of the multi echo scans. Our work shows that we can indeed significantly improve multi-echo imaging over compressed sensing based techniques and other unstructured adaptive sparse recovery methods.