SPArse Reconstruction using a ColLEction of bases (SPARCLE)
نویسندگان
چکیده
Introduction: The recently introduced Compressed Sensing (CS) theory has demonstrated that MR images can be reconstructed from a small number of k-space measurements [1-3]. The key assumption in CS MRI is that the image has a sparse representation in a predetermined basis. Selection of this sparsity basis is critically important in CS. In this work, we introduce a new sparse reconstruction framework (SPARCLE) where sparsity is enforced within a collection of bases rather than a single one. Reconstruction results indicate that this new framework can yield significantly improved image quality. Theory: Let Ψ denote the sparsity transform, Ω F the undersampled Fourier measurement matrix,
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