Statistical Wavelet Structure Based MRI Compressed Sensing Reconstruction Using a Hidden Markov Tree Model

Enhao Gong¹, Xiao Wang¹, Kui Ying², Shi Wang²

Compressed sensing (CS) is an emerging acceleration technique and recently applied for MRI. Conventional CS reconstruction techniques are based on simplistic sparsity of signals and use uniform L1-norm penalty regardless of whether the coefficients contribute to significant information for pathological diagnosis. This results in reconstruction errors, like blurring details. We proposed a new algorithm that uses Hidden Markov Tree model to extract structural information in wavelet domain. Sparsity is regulated by exploiting statistical structural matrices, such that important coefficients are enhanced and artifacts are further reduced. Phantom simulation and in-vivo experiments show the validity and advantages of our algorithm.