Abstract #2918

Four-Dimensional Respiratory Motion-Resolved Sparse Lung MRI

Li Feng¹, Jean Delacoste², Hersh Chandarana¹, Davide Piccini^2,3, Francis Girvin¹, Matthias Stuber^2,4, 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 (k_x-k_y-k_z-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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