RANGR: Deep Learning Autonavigation of Free-Breathing Golden-Angle Radial Abdominal MRI

Motivation: Current autonavigation methodology for free-breathing MRI methods lacks reliability. Goal(s): Develop deep learning methodology to estimate a motion signal directly from the acquired data without manually tuned filtering or PCA transformation. Approach: RANGR uses an encoder network based on the popular VGG architecture to estimate a 1-D respiratory navigator signal from 1-D projections extracted directly from the data. Results: RANGR improved motion estimation and results on motion-resolved images with reduced artifacts, and was even able to detect motion even in cases where filtering+PCA completely failed. Impact: The improved robustness and automation presented by RANGR can promote the use of free-breathing motion-resolved imaging for both diagnostic and treatment guidance purposes.