International Journal of Medical Physics, Clinical Engineering and Radiation Oncology

International Journal of Medical Physics, Clinical Engineering and Radiation Oncology

ISSN Print: 2168-5436
ISSN Online: 2168-5444
www.scirp.org/journal/ijmpcero
E-mail: ijmpcero@scirp.org
"Method for Converting Cone-Beam CT Values into Hounsfield Units for Radiation Treatment Planning"
written by Tadanori Abe, Kunihiko Tateoka, Yuichi Saito, Takuya Nakazawa, Masaki Yano, Kensei Nakata, Masanori Someya, Masakazu Hori, Koichi Sakata,
published by International Journal of Medical Physics, Clinical Engineering and Radiation Oncology, Vol.6 No.4, 2017
has been cited by the following article(s):
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[4] Reliability and reproducibility of a conversion factor for grayscale values obtained from CBCTS assessed at various anatomical regions-A retrospective study
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[8] Daily dose evaluation based on corrected CBCTs for breast cancer patients: accuracy of dose and complication risk assessment
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[10] A deep unsupervised learning model for artifact correction of pelvis cone-beam CT
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[12] Cone-beam CT image quality improvement using Cycle-Deblur consistent adversarial networks (Cycle-Deblur GAN) for chest CT imaging in breast cancer …
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[13] Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT
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[14] Effect of computed tomography value error on dose calculation in adaptive radiotherapy with Elekta X‐ray volume imaging cone beam computed tomography
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[15] Imaging Study of Pseudo-CT Synthesized From Cone-Beam CT Based on 3D CycleGAN in Radiotherapy
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[16] Comparison of Supervised and Unsupervised Approaches for the Generation of Synthetic CT from Cone-Beam CT. Diagnostics 2021, 11, 1435
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[17] Levantamento da curva CT-TO-ED para CBCT
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[18] CBCT‐based synthetic CT generation using deep‐attention cycleGAN for pancreatic adaptive radiotherapy
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[19] Cone-Beam CT 画像の画質改善を目的とした 3 次元敵対的生成ネットワークの提案
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[20] Levantamento da curva CT-to-ED para CBCT e seu uso na estimativa de dose em tratamento radioterápico de próstata
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[21] Automatic three‐dimensional analysis of bone volume and quality change after maxillary sinus augmentation
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[22] Survey of the CT-TO-ED curve for CBCT
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[23] Generating Synthesized Computed Tomography (CT) from Cone-Beam Computed Tomography (CBCT) using CycleGAN for Adaptive Radiation Therapy
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[24] Image modality conversion from helical CT to cone-beam CT with deep learning model for adaptive radiation therapy
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