Blind Compressed Sensing Enables 3d Dynamic Free Breathing Mr Imaging of Lung Volumes and Diaphragm Motion

Objectives: The objective of this study is to increase the spatial and temporal resolution of dynamic 3D MR imaging of lung volumes and diaphragm motion. To achieve this goal, we evaluate the utility of the proposed blind compressed sensing (BCS) algorithm to recover data from highly undersampled measurements. Materials and Methods: We evaluated the performance of the BCS scheme to recover dynamic datasets from retrospectively and prospectively undersampled measurements. We also compared its performance against view-sharing, nuclear norm minimization, and l1 Fourier sparsity regularization schemes. Quantitative experiments were performed on a healthy subject using a fully sampled 2D dataset with uniform radial sampling, which was retrospectively undersampled with 16 radial spokes per frame to correspond to an undersampling factor of 8. The images obtained from the four reconstruction schemes were compared to the fully sampled data using mean square error (MSE) and normalized high frequency (HFEN) error metrics. The schemes