ESTRO 2022 - Abstract Book

S1380

Abstract book

ESTRO 2022

Conclusion Using the synthetic CT images generated from T2-weighted MR images results in clinically insignificant dose differences compared to dose calculated on the deformed CT. Therefore, sCT images generated with this algorithm would allow for a MR-only workflow in radiotherapy planning.

PO-1598 Deep learning based 4D synthetic CTs for daily proton dose calculations in lung cancer patients

A. Thummerer 1 , C. Seller Oria 1 , P. Zaffino 2 , K. Veldman 1 , A. Meijers 1 , J. Seco 3,4 , R. Wijsman 1 , J.A. Langendijk 1 , A.C. Knopf 1,5 , M.F. Spadea 2 , S. Both 1 1 University Medical Center Groningen, Department of Radiation Oncology, Groningen, The Netherlands; 2 Magna Graecia University, Department of Experimental and Clinical Medicine, Catanzaro, Italy; 3 Deutsches Krebsforschungszentrum (DKFZ), Department of Biomedical Physics in Radiation Oncology, Heidelberg, Germany; 4 Heidelberg University, Department of Physics and Astronomy, Heidelberg, Germany; 5 University Hospital of Cologne, Center for Integrated Oncology Cologne, Cologne, Germany Purpose or Objective Recently deep neural networks have shown promising results in correcting image quality deficiencies of CBCT images in multiple anatomical locations. Corrected CBCT images, also referred to as synthetic CTs (sCT), can enable daily adaptive proton therapy workflows, which rely on a recalculation of treatment plans on an up-to-date patient anatomy. Compared to 3D-CBCTs, 4D-CBCTs allow to assess the patients breathing and tumour motion on a daily basis but suffer from additional artifacts due to the reconstruction with a very limited projection number. In this study we focused on extending previous deep learning approaches to generate 4D-synthetic CTs using 4D-CBCTs reconstructed from projections acquired with a 3D- protocol. The suitability of 4D-sCTs for proton dose calculations in lung cancer patients was evaluated in terms of image quality and dosimetric accuracy. Materials and Methods CBCT projections and 4D-CTs of 50 lung cancer patients, treated with proton therapy, were used to reconstruct 4D-CBCTs and train a U-net like convolutional neural network to generate 4D-sCTs. Projection phase binning was performed using the Amsterdam Shroud method, and the binned projections were reconstructed into six breathing phases using the MA-ROOSTER