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Abstract #2325

Rapid 4D-MRI reconstruction using a Deep RAdial ConvoLutionAl neural network: Dracula

Joshua N. Freedman1,2, Oliver J. Gurney-Champion1, Hannah E. Bainbridge3, Jennifer P. Kieselmann1, Michael Dubec4, Henry C. Mandeville3, Simeon Nill1, Marc Kachelrie├č5, Uwe Oelfke1, Martin O. Leach2, and Andreas Wetscherek1

1Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom, 2CR UK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom, 3Department of Radiotherapy, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, United Kingdom, 4Department of Radiotherapy Related Research, The Christie NHS Foundation Trust, Manchester, United Kingdom, 5Medical Physics in Radiology, The German Cancer Research Center (DKFZ), Heidelberg, Germany

4D-MRI could inform online treatment plan adaptation on MRI guided radiotherapy systems, but long iterative reconstruction times (> 10 minutes) limit its use. A deep convolutional neural network was trained to learn the joint MoCo-HDTV algorithm and high-quality 4D-MRI (1.25x1.25x3.3 mm3, 16 respiratory phases) were reconstructed from gridded raw data in 27 seconds. Calculated 4D-MRI exhibited a high structural similarity index (0.97 ┬▒ 0.013) with the iteratively reconstructed test images and only a minor loss of fine details. Despite exclusively training the network on data from a diagnostic scanner, 4D-MRI were successfully reconstructed from raw data acquired on an MR-linac.

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