Abstract #2325

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

Joshua N. Freedman^1,2, Oliver J. Gurney-Champion¹, Hannah E. Bainbridge³, Jennifer P. Kieselmann¹, Michael Dubec⁴, Henry C. Mandeville³, Simeon Nill¹, Marc Kachelrieß⁵, 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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