  Multi-echo magnetic resonance (MR) images are acquired by changing the echo
times (for T2 weighted) or relaxation times (for T1 weighted) of scans. The
resulting (multi-echo) images are usually used for quantitative MR imaging.
Acquiring MR images is a slow process and acquiring multi scans of the same
cross section for multi-echo imaging is even slower. In order to accelerate the
scan, compressed sensing (CS) based techniques have been advocating partial
K-space (Fourier domain) scans; the resulting images are reconstructed via
structured CS algorithms. In recent times, it has been shown that instead of
using off-the-shelf CS, better results can be obtained by adaptive
reconstruction algorithms based on structured dictionary learning. In this
work, we show that the reconstruction results can be further improved by using
structured deep dictionaries. Experimental results on real datasets show that
by using our proposed technique the scan-time can be cut by half compared to
the state-of-the-art.
