TY - GEN
T1 - Dose distribution prediction for optimal treamtment of modern external beam radiation therapy for nasopharyngeal carcinoma
AU - Daoud, Bilel
AU - Morooka, Ken’ichi
AU - Miyauchi, Shoko
AU - Kurazume, Ryo
AU - Mnejja, Wafa
AU - Farhat, Leila
AU - Daoud, Jamel
N1 - Funding Information:
Acknowledgement. This work was supported by JST CREST Grant Number JPMJCR1786, JSPS KAKENHI Grant Number 19H04139, and JIUN Corporation.
PY - 2019/1/1
Y1 - 2019/1/1
N2 - In Intensity-modulated radiation therapy, the planning of the optimal dose distribution for a patient is a complex and time-consuming process. This paper proposes a new automatic method for predicting of dose distribution of Nasopharyngeal carcinoma (NPC) from contoured computer tomography (CT) images. The proposed method consists of two phases: (1) predicting the 2D optimal dose images of each beam from contoured CT images of a patient by convolutional deep neural network model, called OTNet, and (2) integrating the optimal dose images of all the beams to predict the dose distribution for the patient. From the experiments using CT images of 80 NPC patients, our proposed method achieves a good performance for predicting dose distribution compared with conventional predicted dose distribution methods.
AB - In Intensity-modulated radiation therapy, the planning of the optimal dose distribution for a patient is a complex and time-consuming process. This paper proposes a new automatic method for predicting of dose distribution of Nasopharyngeal carcinoma (NPC) from contoured computer tomography (CT) images. The proposed method consists of two phases: (1) predicting the 2D optimal dose images of each beam from contoured CT images of a patient by convolutional deep neural network model, called OTNet, and (2) integrating the optimal dose images of all the beams to predict the dose distribution for the patient. From the experiments using CT images of 80 NPC patients, our proposed method achieves a good performance for predicting dose distribution compared with conventional predicted dose distribution methods.
UR - http://www.scopus.com/inward/record.url?scp=85075688049&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85075688049&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-32486-5_16
DO - 10.1007/978-3-030-32486-5_16
M3 - Conference contribution
AN - SCOPUS:85075688049
SN - 9783030324858
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 128
EP - 136
BT - Artificial Intelligence in Radiation Therapy - 1st International Workshop, AIRT 2019, Held in Conjunction with MICCAI 2019, Proceedings
A2 - Nguyen, Dan
A2 - Jiang, Steve
A2 - Xing, Lei
PB - Springer
T2 - 1st International Workshop on Connectomics in Artificial Intelligence in Radiation Therapy, AIRT 2019 held in conjunction with the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2019
Y2 - 17 October 2019 through 17 October 2019
ER -