Accelerated whole‐brain multi‐parameter mapping using blind compressed sensing

Abstract

Purpose To introduce a blind compressed sensing (BCS) framework to accelerate multi-parameter MR mapping, and demonstrate its feasibility in high-resolution, whole-brain T1ρ 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 (CS), the dictionary and the sparse coefficients are jointly estimated from under-sampled 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 under-sampling experiments, the mean square errors in T1ρ 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 CS 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 multi-parameter mapping of the whole brain with minimal artifacts, and is more robust to motion-induced signal changes compared to current CS and PCA based techniques.

Quantitative rotating frame relaxometry methods in MRI

“…Additional recent applications of these techniques can be found in refs. . The main strength of T 1 ρ and T 2 ρ MRI, i.e.…”

T2 shuffling: Sharp, multicontrast, volumetric fast spin‐echo imaging

“…The linearization is attractive because it lends itself to a convex formulation and PCA can be applied to training signals that account for stimulated echoes and imperfect slice profiles. Data-driven variants (25, 26) can also be used to build robustness to motion (27) and other non-ideal imaging considerations. The linear subspace constraint is robust to partial voluming and implicitly accounts for multi-compartmental models, as linear combinations of signal evolutions remain in the subspace.…”

Rapid compositional mapping of knee cartilage with compressed sensing MRI

“…Methods like blind CS are important steps in terms of learning sparsifying basis within a more stable optimization. Blind CS has been applied for brain mapping …”

“…Regularization functions with learned sparsifying transforms or dictionaries have been reported in the literature. 94 Learning dictionaries are already a reality 66 with knee cartilage mapping. However, instability due to nonconvexity still needs to be resolved.…”

“…Blind CS has been applied for brain mapping. 94 Other Emerging Rapid Imaging Methods Model-based reconstruction is an interesting approach that has already produced good results for brain mapping using monoexponential models with 4-to 6-fold AF. 97,98 Essentially, the system matrix E from Eq.…”