  Multishot Magnetic Resonance Imaging (MRI) is a promising imaging modality
that can produce a high-resolution image with relatively less data acquisition
time. The downside of multishot MRI is that it is very sensitive to subject
motion and even small amounts of motion during the scan can produce artifacts
in the final MR image that may cause misdiagnosis. Numerous efforts have been
made to address this issue; however, all of these proposals are limited in
terms of how much motion they can correct and the required computational time.
In this paper, we propose a novel generative networks based conjugate gradient
SENSE (CG-SENSE) reconstruction framework for motion correction in multishot
MRI. The proposed framework first employs CG-SENSE reconstruction to produce
the motion-corrupted image and then a generative adversarial network (GAN) is
used to correct the motion artifacts. The proposed method has been rigorously
evaluated on synthetically corrupted data on varying degrees of motion, numbers
of shots, and encoding trajectories. Our analyses (both quantitative as well as
qualitative/visual analysis) establishes that the proposed method significantly
robust and outperforms state-of-the-art motion correction techniques and also
reduces severalfold of computational times.
