TY - GEN
T1 - Scatter correction in cone-beam computed tomography using convolutional neural networks
AU - Moncada, Fernando
AU - Zapien, Brian
AU - Cruz-Bastida, Juan Pablo
AU - Rodríguez-Villafuerte, Mercedes
AU - Martínez-Dávalos, Arnulfo
N1 - Publisher Copyright:
© 2023 Author(s).
PY - 2023/10/5
Y1 - 2023/10/5
N2 - A validated Monte Carlo model of the kV imaging system of the TrueBeam STx Linac based on the egs_cbct code of EGSnrc has been used to determine the scatter contribution in cone-beam computed tomography (CBCT) images. Geometrical and anatomical digital phantoms were used in this MC framework to acquire a data set of CBCT images and its respective scatter components. This data set was used to train a deep learning algorithm, based on Convolutional Neural Networks. Specifically, we analyzed the performance of UNets and Generative Adversarial Networks. UNets demonstrate more efficient and precise results, even when trained with a few hundred images. Scatter corrections in CBCT images can be achieved with a trained deep learning-based model in 3 to 4 orders of magnitude faster than MC-based methods.
AB - A validated Monte Carlo model of the kV imaging system of the TrueBeam STx Linac based on the egs_cbct code of EGSnrc has been used to determine the scatter contribution in cone-beam computed tomography (CBCT) images. Geometrical and anatomical digital phantoms were used in this MC framework to acquire a data set of CBCT images and its respective scatter components. This data set was used to train a deep learning algorithm, based on Convolutional Neural Networks. Specifically, we analyzed the performance of UNets and Generative Adversarial Networks. UNets demonstrate more efficient and precise results, even when trained with a few hundred images. Scatter corrections in CBCT images can be achieved with a trained deep learning-based model in 3 to 4 orders of magnitude faster than MC-based methods.
UR - http://www.scopus.com/inward/record.url?scp=85177648786&partnerID=8YFLogxK
U2 - 10.1063/5.0161214
DO - 10.1063/5.0161214
M3 - Conference contribution
AN - SCOPUS:85177648786
VL - 2947
T3 - AIP Conference Proceedings
BT - AIP Conference Proceedings
A2 - Morales-Barcenas, Jose Hector
A2 - Aguirre, Olga Leticia Avila
A2 - Ruiz-Trejo, Cesar
A2 - Massillon-J.L., Guerda
A2 - Torres-Garcia, Eugenio
A2 - Brandan, Maria-Ester
A2 - Garcia-Pelagio, Karla Paola
PB - American Institute of Physics Inc.
T2 - 17th Mexican Symposium on Medical Physics 2022, MSMP 2022
Y2 - 7 September 2022 through 9 September 2022
ER -