Abstract #4249

Neural network based denoising of high temporal resolution cine images for tumor tracking in MR-guided radiotherapy

Florian Friedrich^1,2, Juliane Hörner-Rieber³, Peter Bachert^1,2, Mark E. Ladd^1,2,4, and Benjamin R. Knowles¹

¹Medical Physics in Radiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, ²Department of Physics and Astronomy, Heidelberg University, Heidelberg, Germany, ³Department of Radiation Oncology, University Hospital of Heidelberg, Heidelberg, Germany, ⁴Faculty of Medicine, Heidelberg University, Heidelberg, Germany

MR-linac systems allow for real-time tumor position updates. Higher temporal resolution imaging through k-space undersampling allows for an increased number of position updates, however iterative reconstructions may negate the decrease in acquisition time and undersampling artifacts may impact tracking stability.

In this study a fast method to denoise and suppress image artifact using a U-net is presented. Undersampled Cartesian and radial cine images were acquired from a patient with a liver tumor on an MR-linac. Tumor tracking stability was assessed. Denoising was found to improve tracking stability and has potential in high temporal resolution cine imaging on MR-linac systems.

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