SU-FF-J-03: Quantitative Assessment of Three Dead Detector Interpolative Correction Methods for Cone Beam CT Images

Medical Physics ◽

10.1118/1.3181295 ◽

2009 ◽

Vol 36 (6Part5) ◽

pp. 2475-2475

Author(s):

D Nelms ◽

H Shukla ◽

E Nixon ◽

J Bayouth ◽

R Flynn

Keyword(s):

Quantitative Assessment ◽

Cone Beam Ct ◽

Ct Images ◽

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SU-GG-I-24: A Quantitative Assessment of the Effects of a Simple Dead Detector Pixel Correction Method for Megavoltage Cone Beam CT Images

Medical Physics ◽

10.1118/1.2961423 ◽

2008 ◽

Vol 35 (6Part3) ◽

pp. 2648-2648

Author(s):

R Flynn ◽

H Shukla ◽

E Nixon ◽

J Bayouth

Keyword(s):

Quantitative Assessment ◽

Cone Beam Ct ◽

Correction Method ◽

Ct Images ◽

Cone Beam ◽

Megavoltage Cone Beam Ct

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Cone Beam CT Images of Normal and Paralyzed Facial Nerves

Otolaryngology ◽

10.1016/j.otohns.2010.06.489 ◽

2010 ◽

Vol 143 (2_suppl) ◽

pp. P228-P229

Author(s):

Masahiro Komori ◽

Naoaki Yanagihara ◽

Yasuyuki Hinohira ◽

Hiroshi Aritomo

Keyword(s):

Cone Beam Ct ◽

Ct Images ◽

Cone Beam ◽

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Independent component analysis based ring artifact reduction in cone-beam CT images

2009 16th IEEE International Conference on Image Processing (ICIP) ◽

10.1109/icip.2009.5414528 ◽

2009 ◽

Author(s):

Yen-Wei Chen ◽

Guifen Duan

Keyword(s):

Independent Component Analysis ◽

Cone Beam Ct ◽

Component Analysis ◽

Ct Images ◽

Independent Component ◽

Cone Beam ◽

Artifact Reduction ◽

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The Ability of Different Observers in Measuring Dimensions of Objects and Cavities in Cone Beam-CT Images

Avicenna Journal of Dental Research ◽

10.34172/ajdr.2019.28 ◽

2019 ◽

Vol 11 (4) ◽

pp. 139-143

Author(s):

Fatemeh Eskandarloo ◽

Amir Eskandarloo ◽

Payam Amini

Keyword(s):

Cone Beam Ct ◽

Ct Images ◽

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Inter-fraction variations in motion modeling using patient 4D-cone beam CT images

2018 Advances in Science and Engineering Technology International Conferences (ASET) ◽

10.1109/icaset.2018.8376889 ◽

2018 ◽

Author(s):

Salam Dhou ◽

Dan Ionascu ◽

Christopher Williams ◽

John Lewis

Keyword(s):

Cone Beam Ct ◽

Ct Images ◽

Cone Beam ◽

Motion Modeling

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GPU-accelerated deformable registration of cone-beam CT images: design and implementation in the compute unified device architecture

10.17918/etd-2865 ◽

2021 ◽

Author(s):

Nakul Jain

Keyword(s):

Cone Beam Ct ◽

Ct Images ◽

Deformable Registration ◽

Cone Beam ◽

Compute Unified Device Architecture ◽

Design And Implementation ◽

Device Architecture

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Improvement of Image Quality of Cone-beam CT Images by Three-dimensional Generative Adversarial Network

10.1109/embc46164.2021.9629952 ◽

2021 ◽

Author(s):

Takumi Hase ◽

Megumi Nakao ◽

Keiho Imanishi ◽

Mitsuhiro Nakamura ◽

Tetsuya Matsuda

Keyword(s):

Image Quality ◽

Cone Beam Ct ◽

Three Dimensional ◽

Ct Images ◽

Cone Beam ◽

Generative Adversarial Network ◽

Adversarial Network

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A Virtual Cutting Based Method for Aero Engine Turbine Blade Reverse Modeling from Its Cone Beam CT Images

Materials Science Forum - Advances in Materials Manufacturing Science and Technology II ◽

10.4028/0-87849-421-9.877 ◽

2006 ◽

pp. 877-880

Author(s):

Yun Yong Cheng ◽

Kun Pu ◽

Yuan Peng Liu ◽

Xin Bo Zhao

Keyword(s):

Turbine Blade ◽

Cone Beam Ct ◽

Ct Images ◽

Cone Beam ◽

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Using Meglumine Diatrizoate to improve the accuracy of diagnosis of cracked teeth on Cone‐beam CT images

International Endodontic Journal ◽

10.1111/iej.13270 ◽

2020 ◽

Vol 53 (5) ◽

pp. 709-714

Author(s):

M. Yuan ◽

A. T. Gao ◽

T. M. Wang ◽

J. H. Liang ◽

G. B. Aihemati ◽

...

Keyword(s):

Cone Beam Ct ◽

Ct Images ◽

Cone Beam ◽

Meglumine Diatrizoate

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Classification of alveolar bone density using 3-D deep convolutional neural network in the cone-beam CT images: A 6-month clinical study

Measurement ◽

10.1016/j.measurement.2019.106945 ◽

2019 ◽

Vol 148 ◽

pp. 106945 ◽

Author(s):

Majid Memarian Sorkhabi ◽

Maryam Saadat Khajeh

Keyword(s):

Neural Network ◽

Clinical Study ◽

Bone Density ◽

Convolutional Neural Network ◽

Alveolar Bone ◽

Cone Beam Ct ◽

Ct Images ◽

Deep Convolutional Neural Network ◽

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