4D Respiratory Motion-Compensated Image Reconstruction of Free-Breathing Radial MR Data With Very High Undersampling

Purpose: To develop four-dimensional (4D) respiratory timeresolved MRI based on free-breathing acquisition of radial MR data with very high undersampling. Methods: We propose the 4D joint motion-compensated highdimensional total variation (4D joint MoCo-HDTV) algorithm, which alternates between motion-compensated image reconstruction and artifact-robust motion estimation at multiple resolution levels. The algorithm is applied to radial MR data of the thorax and upper abdomen of 12 free-breathing subjects with acquisition times between 37 and 41 s and undersampling factors of 16.8. Resulting images are compared with compressed sensing-based 4D motion-adaptive spatio-temporal regularization (MASTeR) and 4D high-dimensional total variation (HDTV) reconstructions. Results: For all subjects, 4D joint MoCo-HDTV achieves higher similarity in terms of normalized mutual information and cross-correlation than 4D MASTeR and 4D HDTV when compared with reference 4D gated gridding reconstructions with 8.4 +/- 1.1 times longer acquisition times. In a qualitative assessment of artifact level and image sharpness by two radiologists, 4D joint MoCo-HDTV reveals higher scores (P < 0.05) than 4D HDTV and 4D MASTeR at the same undersampling factor and the reference 4D gated gridding reconstructions, respectively. Conclusions: 4D joint MoCo-HDTV enables time-resolved image reconstruction of free-breathing radial MR data with undersampling factors of 16.8 while achieving low-streak arti-fact levels and high image sharpness. (C) 2016 International Society for Magnetic Resonance in Medicine