ABSTRACT Purpose To develop a deep learning‐based auto‐navigation technique for free‐breathing golden‐angle radial MRI named RANGR ( R espiratory A uto‐ N avigator for G olden‐angle R adial 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.
Nario et al. (2026) studied this question.