SU-GG-T-231: A Software Tool To Adapt The QUASAR Penta-Guide Phantom To Make An Additional Measurement Of Cone Beam CT Image Sharpness

2008 ◽  
Vol 35 (6Part12) ◽  
pp. 2778-2778
Author(s):  
J Sykes ◽  
D Brettle ◽  
D Magee ◽  
A Morgan ◽  
D Thwaites
Keyword(s):  
Cone Beam Ct ◽  
Software Tool ◽  
Cone Beam ◽  
Ct Image ◽  
Image Sharpness ◽  
Additional Measurement

Association of cortical shape of the mandible on panoramic radiographs with mandibular trabecular bone structure in Japanese adults: a cone-beam CT-image analysis

2013 ◽  
Vol 30 (2) ◽  
pp. 160-167 ◽  
Author(s):  
Noriyasu Mochizuki ◽  
Noriyuki Sugino ◽  
Tadashi Ninomiya ◽  
Nobuo Yoshinari ◽  
Nobuyuki Udagawa ◽  
...  
Keyword(s):  
Image Analysis ◽  
Trabecular Bone ◽  
Bone Structure ◽  
Cone Beam Ct ◽  
Trabecular Bone Structure ◽  
Cone Beam ◽  
Ct Image ◽  
Panoramic Radiographs ◽  
Japanese Adults ◽  
Ct Image Analysis

Denoising of cone beam CT image using wavelet transform

2002 ◽  
Author(s):  
Yi-Qiang Yang ◽  
Nobuyuki Nakamori ◽  
Yasuo Yoshida ◽  
Takanori Tsunoo ◽  
Masahiro Endo ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Image

scholarly journals GPU-Based 3D Cone-Beam CT Image Reconstruction for Large Data Volume

2009 ◽  
Vol 2009 ◽  
pp. 1-8 ◽  
Author(s):  
Xing Zhao ◽  
Jing-jing Hu ◽  
Peng Zhang
Keyword(s):  
Image Reconstruction ◽  
Cone Beam Ct ◽  
Large Data ◽  
Circular Cone ◽  
Cone Beam ◽  
Projection Data ◽  
Ct Image ◽  
Reconstruction Process ◽  
Data Volume ◽  
Ct Image Reconstruction

Currently, 3D cone-beam CT image reconstruction speed is still a severe limitation for clinical application. The computational power of modern graphics processing units (GPUs) has been harnessed to provide impressive acceleration of 3D volume image reconstruction. For extra large data volume exceeding the physical graphic memory of GPU, a straightforward compromise is to divide data volume into blocks. Different from the conventional Octree partition method, a new partition scheme is proposed in this paper. This method divides both projection data and reconstructed image volume into subsets according to geometric symmetries in circular cone-beam projection layout, and a fast reconstruction for large data volume can be implemented by packing the subsets of projection data into the RGBA channels of GPU, performing the reconstruction chunk by chunk and combining the individual results in the end. The method is evaluated by reconstructing 3D images from computer-simulation data and real micro-CT data. Our results indicate that the GPU implementation can maintain original precision and speed up the reconstruction process by 110–120 times for circular cone-beam scan, as compared to traditional CPU implementation.

SU-GG-J-74: Congruence of Cone-Beam CT Image Center with the Radiation Isocenter of a Linear Accelerator

2010 ◽  
Vol 37 (6Part10) ◽  
pp. 3162-3162 ◽  
Author(s):  
W Du ◽  
J Yang ◽  
E Chang ◽  
D Luo ◽  
M McAleer ◽  
...  
Keyword(s):  
Linear Accelerator ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Image ◽  
Image Center

SU-E-T-613: A Method to Improve Cone Beam CT Image Quality and Hounsfield Units Accuracy for Prostate Proton Treatment Planning

2011 ◽  
Vol 38 (6Part20) ◽  
pp. 3630-3630 ◽  
Author(s):  
R Betancourt ◽  
H Lu ◽  
J McDonough ◽  
Z Tochner ◽  
S Both
Keyword(s):  
Image Quality ◽  
Treatment Planning ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Ct Image ◽  
Hounsfield Units ◽  
Proton Treatment ◽  
Ct Image Quality
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