ESTRO 2023 - Abstract Book

S1409

Digital Posters

ESTRO 2023

system), was also included. The usefulness in the virtual CBCT system was demonstrated by applying the developed DL based U-net EMD model in both virtual phantom and experimental Rando phantom. The evaluation of the EMD results were performed by using the root mean squared error (RMSE) and the structural similarity index measure (SSIM) in each predicted 300 slices in 6 test phantoms. Results Examples of scattered, direct x-ray and CBCT images with noise produced by the CBCT simulator are shown in Fig.1, where the projection image agreed well with that generated in Monte Carlo (MC) simulation (Fig.1(a), (b)). The noise determined in the water phantom was somewhat large in the CBCT image domain for human phantoms (Fig.1(c)). The elemental map predicted in the DL-based EMD model developed with the virtual CBCT database is shown in Fig.2, where RMSE is less than 0.08 in all elements within the database as shown in Fig.2(a). Although the CBCT image degradation due to the scattered x-ray and the statistical noise slightly affects the prediction accuracy (p<.001 for all metrics except SSIM of N), the amount of these effects was small. The predicted elemental map with the real CBCT image was also generated reasonably well as shown in Fig.2(c).

Conclusion We demonstrated that the developed virtual simulator can generate various CBCT images based on the human phantom library. Comprehensively, the prediction of the EMD can be successfully performed by preparing the CBCT database from the proposed virtual system, even for the real images using Rando phantom. This study suggests that our approach, to use the computer vision for the data preparation, has a potential in further development of radiation therapy, such as the ART with the EMD from CBCT images.

PO-1698 Towards MR contrast independent synthetic CT generation.