TY - GEN
T1 - Learning a spatiotemporal dictionary for magnetic resonance fingerprinting with compressed sensing
AU - Gómez, Pedro A.
AU - Ulas, Cagdas
AU - Sperl, Jonathan I.
AU - Sprenger, Tim
AU - Molina-Romero, Miguel
AU - Menzel, Marion I.
AU - Menze, Bjoern H.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015
Y1 - 2015
N2 - Magnetic resonance fingerprinting (MRF) is a novel technique that allows for the fast and simultaneous quantification of multiple tissue properties, progressing from qualitative images, such as T1- or T2-weighted images commonly used in clinical routines, to quantitative parametric maps. MRF consists of two main elements: accelerated pseudorandom acquisitions that create unique signal evolutions over time and the voxel-wise matching of these signals to a dictionary simulated using the Bloch equations. In this study, we propose to increase the performance of MRF by not only considering the simulated temporal signal, but a full spatiotemporal neighborhood for parameter reconstruction. We achieve this goal by first training a dictionary from a set of spatiotemporal image patches and subsequently coupling the trained dictionary with an iterative projection algorithm consistent with the theory of compressed sensing (CS). Using data from BrainWeb, we show that the proposed patch-based reconstruction can accurately recover T1 and T2 maps from highly undersampled k-space measurements, demonstrating the added benefit of using spatiotemporal dictionaries in MRF.
AB - Magnetic resonance fingerprinting (MRF) is a novel technique that allows for the fast and simultaneous quantification of multiple tissue properties, progressing from qualitative images, such as T1- or T2-weighted images commonly used in clinical routines, to quantitative parametric maps. MRF consists of two main elements: accelerated pseudorandom acquisitions that create unique signal evolutions over time and the voxel-wise matching of these signals to a dictionary simulated using the Bloch equations. In this study, we propose to increase the performance of MRF by not only considering the simulated temporal signal, but a full spatiotemporal neighborhood for parameter reconstruction. We achieve this goal by first training a dictionary from a set of spatiotemporal image patches and subsequently coupling the trained dictionary with an iterative projection algorithm consistent with the theory of compressed sensing (CS). Using data from BrainWeb, we show that the proposed patch-based reconstruction can accurately recover T1 and T2 maps from highly undersampled k-space measurements, demonstrating the added benefit of using spatiotemporal dictionaries in MRF.
UR - http://www.scopus.com/inward/record.url?scp=84955292911&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-28194-0_14
DO - 10.1007/978-3-319-28194-0_14
M3 - Conference contribution
AN - SCOPUS:84955292911
SN - 9783319281933
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 112
EP - 119
BT - Patch-Based Techniques in Medical Imaging - First st International Workshop, Patch-MI 2015 Held in Conjunction with MICCAI 2015, Revised Selected Papers
A2 - Coupé, Pierrick
A2 - Munsell, Brent
A2 - Wu, Guorong
A2 - Zhan, Yiqiang
A2 - Rueckert, Daniel
PB - Springer Verlag
T2 - 1st International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2015
Y2 - 9 October 2015 through 9 October 2015
ER -