Multi-echo Reconstruction from Partial K-space Scans via Adaptively Learnt Basis
This work addresses time-consuming multi-echo imaging for medical diagnostics, representing an incremental advancement by adapting existing basis learning methods to incorporate scan structure.
The paper tackles the problem of accelerating multi-echo MRI scans by improving reconstruction from partial k-space data, achieving significant improvements over compressed sensing and other adaptive sparse recovery methods.
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.