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Abstract #2918

Four-Dimensional Respiratory Motion-Resolved Sparse Lung MRI

Li Feng1, Jean Delacoste2, Hersh Chandarana1, Davide Piccini2,3, Francis Girvin1, Matthias Stuber2,4, Daniel K Sodickson1, and Ricardo Otazo1

1Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, NY, United States, 2University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 3Advanced Clinical Imaging Technology, Siemens Healthcare, Lausanne, Switzerland, 4Center for Biomedical Imaging (CIBM), Lausanne, Switzerland

A four-dimensional (4D) respiratory motion-resolved UTE MRI method is presented for free-breathing lung MRI with isotropic spatial resolution. Center-out radial half-projection k-space data are continuously acquired using a 3D golden-angle UTE sequence. The radial k-space data are retrospectively sorted into distinct respiratory states, resulting in an undersampled 4D dataset (kx-ky-kz-respiration) using a respiratory motion signal extracted from the acquired data. The undersampled 4D data are reconstructed by exploiting sparsity along the new respiratory dimension. The proposed approach enables free-breathing lung MRI with 100% scan efficiency, allowing for assessment of lung tissue in arbitrary orientations at different respiratory states.

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