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Abstract #2260

Reconstruction of Compressed Sensing MRI by Computing Finite Differences of Wavelet Coefficients

Md Mashud Hyder1, 2, Bradley Peterson1, 2, Zhengchao Dong1, 2

1Brain Imaging Lab, Columbia University, New York, NY, United States; 2New York State Psychiatric Inst., New York, NY, United States

The theory of compressed sensing (CS) states that MRI images can be recovered efficiently from randomly undersampled k-space data under certain conditions. Many MRI images have sparse representation when we calculate their spatial finite-differences or wavelet coefficients. In this work we show that spatial finite-differences of a set of wavelet coefficients can increase the sparsity of MRI image, which results in improved recovery of CS MRI.

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Keywords

absolute actuation application approximate axial basis better brain certain coefficients compressed compressibility compute computing conditions consider consideration considered construct core critical dataset deceased denoted denotes depends details dimension domains dong edges efficiently environment error evaluation every excite expected family features filter finite formulation full fully functions hence idea improved increasing indicates inst investigated iteration mainly many measured medical memory minx needs noise norm operating operator optimization optimizing outputs particular pattern percentage performance phantom potential problem problems procedure processing processor propose proposed psychiatric random randomly reconstructed reconstruction reconstructions recover recovered recovery reduce representation represents respectively reveal root sample sampled scanner selecting sensing significantly simulations situation solve space sparse sparsity spatial squired subjected system theory trans transform transformed unable variation variations version wavelet zero