Feasibility of image quality improvement for high-speed CBCT imaging using deep convolutional neural network for image-guided radiotherapy in prostate cancer.
Cone-beam computed tomography
Convolutional neural network
Image-guided radiotherapy
Radiotherapy
Journal
Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
ISSN: 1724-191X
Titre abrégé: Phys Med
Pays: Italy
ID NLM: 9302888
Informations de publication
Date de publication:
Dec 2020
Dec 2020
Historique:
received:
16
05
2020
revised:
14
09
2020
accepted:
15
10
2020
pubmed:
3
11
2020
medline:
25
6
2021
entrez:
2
11
2020
Statut:
ppublish
Résumé
High-speed cone-beam computed tomography (CBCT) scan for image-guided radiotherapy (IGRT) can reduce both the scan time and the exposure dose. However, it causes noise and artifacts in the reconstructed images due to the lower number of acquired projection data. The purpose of this study is to improve the image quality of high-speed CBCT using a deep convolutional neural network (DCNN). CBCT images of 36 prostate cancer patients were selected. The CBCT images acquired at normal scan speed were defined as CBCT The DCNN model can process CBCT We developed a DCNN model to remove noise and artifacts from high-speed CBCT. We emphasize that it is possible to reduce exposure to one quarter and to increase the CBCT scan speed by a factor of four.
Identifiants
pubmed: 33137623
pii: S1120-1797(20)30255-6
doi: 10.1016/j.ejmp.2020.10.012
pii:
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
84-91Informations de copyright
Copyright © 2020 Associazione Italiana di Fisica Medica. Published by Elsevier Ltd. All rights reserved.