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Abstract #2601

Improved L1-SPIRiT Using Tensor-Based Sparsity Basis

Zhen Feng1, Feng Liu2, Stuart Crozier2, He Guo1

1Dalian University of Technology, Dalian, Liaoning, China; 2The University of Queensland, Brisbane, Queensland, Australia

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.