Abstract #0080
Total Generalized Variation Based Joint Multi-Contrast, Parallel Imaging Reconstruction of Undersampled k-space Data
Adrian Martin 1,2 , Itthi Chatnuntawech 1 , Berkin Bilgic 3 , Kawin Setsompop 3,4 , Elfar Adalsteinsson 1,5 , and Emanuele Schiavi 6
1
Department of Electrical Engineering and
Computer Science, Massachusetts Institute of Technology,
Cambridge, MA, United States,
2
Applied
Mathematics, Universidad Rey Juan Carlos, Mostoles,
Madrid, Spain,
3
A.
A. Martinos Center for Biomedical Imaging, Department of
Radiology, Massachusetts General hospital, Charlestown,
MA, United States,
4
Harvard
Medical School, Boston, MA, United States,
5
Harvard-MIT
Health Sciences and Technology, Massachusetts Institute
of Technology, Cambridge, MA, United States,
6
Universidad
Rey Juan Carlos, Mostoles, Madrid, Spain
Typical clinical MRI routines include multiple imaging
of the same region of interest under different contrast
settings. In this work we extend the Total Generalized
Variation (TGV) operator to jointly reconstruct multiple
MRI contrasts from undersampled k-space data using one
or more receiver coils. The multi-contrast TGV operator
exploits the structural similarities of the
multi-contrast images to preserve these details in the
reconstruction process. The proposed technique yields to
improved reconstruction accuracy when compared to widely
used parallel imaging reconstruction methods such as
SENSE and Total Variation regularized SENSE.
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