Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence
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.
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