ESTRO 2022 - Abstract Book

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Abstract book

ESTRO 2022

Conclusion The proposed cycleGAN-based workflow resulted in improved synthetic results. Using global residuals with geometrical loss and weakly paired data via smart data selection was agnostic to the generator architecture and improved the performance of CBCT-to-CT synthesis task.

OC-0774 Generalizability of deep-learning-based CBCT image enhancement with respect to anti-scatter grids.

X. Staal 1 , J. Sonke 1

1 Netherlands Cancer Institute, Radiation Oncology, Amsterdam, The Netherlands

Purpose or Objective In recent years, the neural network (NN) has emerged as a promising tool to enhance Cone Beam Computed Tomography (CBCT) images. Among them, NNs based on the cycle consistent generative adversarial network (CycleGAN) architecture have been shown to suppress imaging artifacts in CBCT images. Such a NN is applied to a CBCT image, resulting in a synthetic CT (sCT), with processing times of a few seconds. These sCTs should allow accurate dose calculations and (re-)contouring, which is an important step toward online adaptive radiotherapy with conventional linacs. A common concern with NNs is generalizability. When a trained NN is deployed, it is hard to predict how it will perform on out-of-distribution data, e.g. acquired in a different institute or with a different machine. This study quantifies the performance of a NN, trained on CBCTs acquired on machines without anti-scatter grid (ASG), when applied to CBCTs acquired on machines with ASG. Materials and Methods The NN was a pretrained, CycleGAN-based model, deployed in the Advanced Medical Image Registration Engine (ADMIRE, Elekta) v3.34. It was trained on CBCTs of the male pelvis region, acquired without ASG, downsampled to 1.64x1.64mm in- plane voxel size. The testing dataset contained 895 CBCTs (Elekta Versa HD) from 47 prostate cancer patients, each with a planning CT (Siemens Somatom go.Open Pro). Of these CBCTs, 336 and 559 were acquired with and without ASG respectively. For each CBCT, a sCT was generated by the NN and two reference CTs were generated by deformably registering the planning CT to the CBCT and sCT respectively. The CBCT and sCT were compared to their reference CT with the structural similarity (SSIM) index, mutual information (MI), mean error (ME), mean absolute error (MAE) and the absolute percentage difference between their histograms (histogram error). The sharpness of the images was evaluated as the full width at half maximum (FWHM) value of the compact bone of the pelvis. Statistical significance of the results was evaluated using the Wilcoxon rank-sum test for clustered data.

Results