Compressed sensing in hyperpolarized3He Lung MRI

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Abstract
In this work, the application of compressed sensing techniques to the acquisition and reconstruction of hyperpolarized 3He lung MR images was investigated. The sparsity of 3He lung images in the wavelet domain was investigated through simulations based on fully sampled Cartesian two-dimensional and three-dimensional 3He lung ventilation images, and the k-spaces of 2D and 3D images were undersampled randomly and reconstructed by minimizing the L1 norm. The simulation results show that temporal resolution can be readily improved by a factor of 2 for two-dimensional and 4 to 5 for three-dimensional ventilation imaging with 3He with the levels of signal to noise ratio (SNR) (∼19) typically obtained. The feasibility of producing accurate functional apparent diffusion coefficient (ADC) maps from undersampled data acquired with fewer radiofrequency pulses was also demonstrated, with the preservation of quantitative information (mean ADCcs ∼ mean ADCfull ∼ 0.16 cm2 sec−1). Prospective acquisition of 2-fold undersampled two-dimensional 3He images with a compressed sensing k-space pattern was then demonstrated in a healthy volunteer, and the results were compared to the equivalent fully sampled images (SNRcs = 34, SNRfull = 19). Magn Reson Med 63:1059–1069, 2010.

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