Abstract #5012

Learned 3D radial trajectories using Image Quality Metrics from Prior Data

Chenwei Tang¹, Laura Burns Eisenmenger², Steven Kecskemeti³, and Kevin Johnson^1,2

¹Medical Physics, University of Wisconsin-Madison, Madison, WI, United States, ²Radiology, University of Wisconsin-Madison, Madison, WI, United States, ³University of Wisconsin-Madison, Madison, WI, United States

Synopsis

3D radial sampling provides high levels of acceleration with insensitivity to motion. Methods of designing the radial projection angles and orders have been focused on improving the sampling uniformity, which is indirectly related to image quality. We propose a flexible framework to optimize the sampling directly based on image reconstruction metrics. We demonstrated the optimized trajectories were able to produce higher quality images in both simulations and scans.

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