Purpose To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain and T2 mapping. Methods BCS models the evolution of magnetization at every pixel as a sparse linear combination of bases in a dictionary. Unlike compressed sensing, the dictionary and the sparse coefficients are jointly estimated from undersampled data. Large number of non-orthogonal bases in BCS accounts for more complex signals than low rank representations. The low degree of freedom of BCS, attributed to sparse coefficients, translates to fewer artifacts at high acceleration factors (R). Results From 2D retrospective undersampling experiments, the mean square errors in and T2 maps were observed to be within 0.1% up to R = 10. BCS was observed to be more robust to patient-specific motion as compared to other compressed sensing schemes and resulted in minimal degradation of parameter maps in the presence of motion. Our results suggested that BCS can provide an acceleration factor of 8 in prospective 3D imaging with reasonable reconstructions. Conclusion BCS considerably reduces scan time for multiparameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current compressed sensing and principal component analysis-based techniques.
Bibliographical notePublisher Copyright:
© 2015 Wiley Periodicals, Inc.
- 3D multi-parameter mapping
- T imaging
- Tρ imaging
- blind compressed sensing (BCS)
- dictionary learning