TY - GEN
T1 - Calibrationless parallel dynamic MRI with joint temporal sparsity
AU - Yu, Yang
AU - Yan, Zhennan
AU - Feng, Li
AU - Metaxas, Dimitris
AU - Axel, Leon
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2016.
PY - 2016
Y1 - 2016
N2 - In this paper, we propose a novel calibrationless method for parallel dynamic magnetic resonance imaging (MRI) reconstruction, which overcomes the limitations posed by traditional MRI reconstruction methods that require accurate coil calibration. Thus, calibrationless methods, which remove the requirement of coil sensitivity profiles for MRI reconstruction, are suitable for dynamic MRI. Dynamic MRI contains rich temporal redundant information, i.e., the pixel intensities change smoothly over time. This property can be modeled as various types of temporal sparse priors, in the Fourier transform domain, or in the image domain using finite differences. In addition, the temporally changing patterns of pixels are similar in the various coils, since their signals are different due to the coil sensitivity profiles. Therefore, we model the parallel dynamic MRI problems as joint temporal sparsity tasks, and develop a class of algorithms to solve them efficiently. Experiments on parallel dynamic MRI datasets demonstrate that our proposed methods outperform the state-of-the-art parallel MRI reconstruction algorithms.
AB - In this paper, we propose a novel calibrationless method for parallel dynamic magnetic resonance imaging (MRI) reconstruction, which overcomes the limitations posed by traditional MRI reconstruction methods that require accurate coil calibration. Thus, calibrationless methods, which remove the requirement of coil sensitivity profiles for MRI reconstruction, are suitable for dynamic MRI. Dynamic MRI contains rich temporal redundant information, i.e., the pixel intensities change smoothly over time. This property can be modeled as various types of temporal sparse priors, in the Fourier transform domain, or in the image domain using finite differences. In addition, the temporally changing patterns of pixels are similar in the various coils, since their signals are different due to the coil sensitivity profiles. Therefore, we model the parallel dynamic MRI problems as joint temporal sparsity tasks, and develop a class of algorithms to solve them efficiently. Experiments on parallel dynamic MRI datasets demonstrate that our proposed methods outperform the state-of-the-art parallel MRI reconstruction algorithms.
UR - http://www.scopus.com/inward/record.url?scp=84981303126&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-42016-5_9
DO - 10.1007/978-3-319-42016-5_9
M3 - Conference contribution
AN - SCOPUS:84981303126
SN - 9783319420158
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 95
EP - 102
BT - Medical Computer Vision
A2 - Kelm, Michael
A2 - Müller, Henning
A2 - Menze, Bjoern
A2 - Zhang, Shaoting
A2 - Metaxas, Dimitris
A2 - Langs, Georg
A2 - Montillo, Albert
A2 - Cai, Weidong
PB - Springer Verlag
T2 - International Workshop on Medical Image Computing for Computer Assisted Intervention, 2015 MICCAI
Y2 - 9 October 2015 through 9 October 2015
ER -