Deep Learning-Based Auto-Navigation for Free-Breathing Golden-Angle Radial MRI

Purpose: To develop a deep learning-based auto-navigation technique for free-breathing golden-angle radial MRI named RANGR (Respiratory Auto-Navigator for Golden-angle Radial free-breathing abdominal MRI). Methods: RANGR computes a one-dimensional (1D) respiratory motion signal from 1D projections along the superior–inferior dimension (z) extracted directly from the acquired golden-angle stack-of-stars k-space data. The motion signal is used to retrospectively sort k-space data into different undersampled motion states, which are reconstructed using Movienet, a neural network developed for dynamic reconstruction. RANGR was trained using PCA (Principal Component Analysis) as a reference in cases where PCA was successful. The performance of RANGR is evaluated against PCA using a dynamic phantom with programmable motion waveforms on a 1.5 T MR-Linac system and free-breathing imaging of patients with abdominal tumors. In vivo image quality was qualitatively scored by body radiologists. Results: For phantom studies, RANGR can track motion with less than 1 mm error with respect to the ground-truth. For in vivo studies, RANGR generalizes to cases where PCA failed due to limited liver coverage and/or the presence of high intensity regions outside the liver dome. RANGR outscores PCA on all qualitative image criteria as evaluated on a 5-point Likert scale by two expert body radiologists (p < 0.05). RANGR estimates motion faster than PCA on GPU (1.7 ± 0.3 vs. 168.7 ± 172.4 ms, p < 0.005). The total time from motion estimation to Movienet reconstruction is 2.91 ± 1.07 s. Conclusion: RANGR presents a robust auto-navigation solution based on deep learning for free-breathing MRI using golden-angle radial MRI acquisition.