  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.
