  Quantitative imaging in MRI usually involves acquisition and reconstruction
of a series of images at multi-echo time points, which possibly requires more
scan time and specific reconstruction technique compared to conventional
qualitative imaging. In this work, we focus on optimizing the acquisition and
reconstruction process of multi-echo gradient echo pulse sequence for
quantitative susceptibility mapping as one important quantitative imaging
method in MRI. A multi-echo sampling pattern optimization block extended from
LOUPE-ST is proposed to optimize the k-space sampling patterns along echoes.
Besides, a recurrent temporal feature fusion block is proposed and inserted
into a backbone deep ADMM network to capture the signal evolution along echo
time during reconstruction. Experiments show that both blocks help improve
multi-echo image reconstruction performance.
