Vectorial total generalized variation for accelerated multi-channel multi-contrast MRI.
To develop and implement an efficient reconstruction technique to improve accelerated multi-channel multi-contrast MRI.



The vectorial total generalized variation (TGV) operator is used as a regularizer for the sensitivity encoding (SENSE) technique to improve image quality of multi-channel multi-contrast MRI. The alternating direction method of multipliers (ADMM) is used to efficiently reconstruct the data. The performance of the proposed method (MC-TGV-SENSE) is assessed on two healthy volunteers at several acceleration factors.



As demonstrated on the in vivo results, MC-TGV-SENSE had the lowest root-mean-square error (RMSE), highest structural similarity index, and best visual quality at all acceleration factors, compared to other methods under consideration. MC-TGV-SENSE yielded up to 17.3% relative RMSE reduction compared to the widely used total variation regularized SENSE. Furthermore, we observed that the reconstruction time of MC-TGV-SENSE is reduced by approximately a factor of two with comparable RMSEs by using the proposed ADMM-based algorithm as opposed to the more commonly used Chambolle-Pock primal-dual algorithm for the TGV-based reconstruction.



MC-TGV-SENSE is a better alternative than the existing reconstruction methods for accelerated multi-channel multi-contrast MRI. The proposed method exploits shared information among the images (MC), mitigates staircasing artifacts (TGV), and uses the encoding power of multiple receiver coils (SENSE).
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA. Electronic address: ichatnun@mit.edu.
Itthi
I
Chatnuntawech
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Applied Mathematics, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain. Electronic address: adrian.martin@urjc.es.
Adrian
A
Martin
A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: berkin@nmr.mgh.harvard.edu.
Berkin
B
Bilgic
A. A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: kawin@nmr.mgh.harvard.edu.
Kawin
K
Setsompop
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA, USA; Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA; Institute of Medical Engineering & Science, MIT, Cambridge, MA, USA. Electronic address: elfar@mit.edu.
Elfar
E
Adalsteinsson
Applied Mathematics, Universidad Rey Juan Carlos, Mostoles, Madrid, Spain. Electronic address: emanuele.schiavi@urjc.es.
Emanuele
E
Schiavi
D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000000981:diagnostic imaging; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D012016:Reference Values
Alternating direction method of multipliers (ADMM); Compressed sensing; Multi-contrast magnetic resonance imaging; Parallel imaging; Total generalized variation