Improved L1-SPIRiT Using Tensor-Based Sparsity Basis

Zhen Feng¹, Feng Liu², Stuart Crozier², He Guo¹

In the sequential combination of parallel imaging (PI) and compressed sensing (CS) MRI, the CS procedure is conventionally performed on individual coils. In fact, the individual coil data are sensitivity-weighted maps of the whole MRI image, therefore signal overlapping exists between coil data. In this work, we propose a novel sparsity basis to improve CS reconstruction through the exploitation of the inter-coil spatial redundancies. In addition, by introducing a new filter that separates the measured and reconstructed data during L2-norm optimization, noise and errors can be minimized in the sequential PI-CS method. The brain image study showed the promise of the new PI-CS scheme.