Use of Deformable Registration Tool for Verifying Tumor Position on Kilo-voltage Cone-beam CT Scans for Stage III Lung Cancer

2009 ◽  
Vol 75 (3) ◽  
pp. S97
Author(s):  
J.R. van Sornsen de Koste ◽  
B.J. Slotman ◽  
S. Senan
Keyword(s):  
Lung Cancer ◽  
Cone Beam Ct ◽  
Deformable Registration ◽  
Stage Iii ◽  
Ct Scans ◽  
Cone Beam

scholarly journals Quantitative evaluation of 4D Cone beam CT scans with reduced scan time in lung cancer patients

2019 ◽  
Vol 136 ◽  
pp. 64-70 ◽  
Author(s):  
Abigail Bryce-Atkinson ◽  
Thomas Marchant ◽  
John Rodgers ◽  
Geoff Budgell ◽  
Alan McWilliam ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cancer Patients ◽  
Quantitative Evaluation ◽  
Cone Beam Ct ◽  
Ct Scans ◽  
Cone Beam ◽  
Lung Cancer Patients ◽  
Scan Time

scholarly journals Effect of deformable registration on the dose calculated in radiation therapy planning CT scans of lung cancer patients a)

2014 ◽  
Vol 42 (1) ◽  
pp. 391-399 ◽  
Author(s):  
Alexandra R. Cunliffe ◽  
Clay Contee ◽  
Samuel G. Armato ◽  
Bradley White ◽  
Julia Justusson ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Radiation Therapy ◽  
Cancer Patients ◽  
Deformable Registration ◽  
Ct Scans ◽  
Therapy Planning ◽  
Lung Cancer Patients ◽  
Radiation Therapy Planning ◽  
Planning Ct

O-151 Four dimensional cone beam CT for verification of radiotherapy for lung cancer

Lung Cancer ◽  
2005 ◽  
Vol 49 ◽  
pp. S52 ◽  
Author(s):  
J. Sonke ◽  
J. Wolthaus ◽  
M. Rossi ◽  
J. Belderbos ◽  
E. Damen ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Cone Beam Ct ◽  
Cone Beam ◽  
Dimensional Cone

Improved image quality of cone beam CT scans for radiotherapy image guidance using fiber-interspaced antiscatter grid

2014 ◽  
Vol 41 (6Part1) ◽  
pp. 061910 ◽  
Author(s):  
Uros Stankovic ◽  
Marcel van Herk ◽  
Lennert S. Ploeger ◽  
Jan-Jakob Sonke
Keyword(s):  
Image Quality ◽  
Image Guidance ◽  
Cone Beam Ct ◽  
Ct Scans ◽  
Cone Beam ◽  
Antiscatter Grid

scholarly journals Fully Convolutional Network for Consistent Voxel-Wise Correspondence

2020 ◽  
Vol 34 (07) ◽  
pp. 12935-12942 ◽  
Author(s):  
Yungeng Zhang ◽  
Yuru Pei ◽  
Yuke Guo ◽  
Gengyu Ma ◽  
Tianmin Xu ◽  
...  
Keyword(s):  
Vector Fields ◽  
State Of The Art ◽  
Displacement Vector ◽  
Cone Beam Ct ◽  
Deformable Registration ◽  
Cone Beam ◽  
Nonlinear Mapping ◽  
Convolutional Network ◽  
Mapping Functions ◽  
Fully Convolutional Network

In this paper, we propose a fully convolutional network-based dense map from voxels to invertible pair of displacement vector fields regarding a template grid for the consistent voxel-wise correspondence. We parameterize the volumetric mapping using a convolutional network and train it in an unsupervised way by leveraging the spatial transformer to minimize the gap between the warped volumetric image and the template grid. Instead of learning the unidirectional map, we learn the nonlinear mapping functions for both forward and backward transformations. We introduce the combinational inverse constraints for the volumetric one-to-one maps, where the pairwise and triple constraints are utilized to learn the cycle-consistent correspondence maps between volumes. Experiments on both synthetic and clinically captured volumetric cone-beam CT (CBCT) images show that the proposed framework is effective and competitive against state-of-the-art deformable registration techniques.

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