Abstract #3476

Super-resolution imaging on an MRI-linac to improve real-time MRI used in MRI guided radiation therapy.

James Grover^1,2, Paul Liu^1,2, Bin Dong², Shanshan Shan^1,2, Brendan Whelan^1,2, Paul Keall^1,2, and David Waddington^1,2

¹Image X Institute, University of Sydney, Sydney, Australia, ²Ingham Institute for Applied Medical Research, Sydney, Australia

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, MRI guided radiation therapy

Real-time MRI is limited in its spatiotemporal resolution due to imaging time being proportional to the spatial resolution. Super-resolution imaging was integrated into an MRI-linac to improve the spatiotemporal resolution of images used in real-time adaptive MRI guided radiation therapy. Real-time up-sampling techniques included conventional bicubic interpolation and deep learning-based super-resolution. Up-sampling increased the spatial resolution as characterised by healthy volunteer brain and thorax MRIs with negligible impact on the temporal resolution as measured in a motion phantom tracking experiment.

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