scholarly journals A Quantitative Assessment of Lung Tumor Motion under Abdominal Compression using 4D-CT Imaging

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Mohatt Dennis ◽  
Gomez Jorge ◽  
Malhotra Harish K
Keyword(s):  
Quantitative Assessment ◽  
Lung Tumor ◽  
Ct Imaging ◽  
Tumor Motion ◽  
Abdominal Compression ◽  
4D Ct

Variability of Tumor Motion in 4DCT: The Effects of Abdominal Compression on Lung Tumor Displacement

2012 ◽  
Vol 84 (3) ◽  
pp. S562
Author(s):  
S. Rosenberg ◽  
S.K. Jabbour ◽  
N.J. Yue ◽  
A. Rasheed ◽  
B.G. Haffty ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Motion ◽  
Abdominal Compression

Lung tumor motion measured using retrospective 4D CT and correlated with tumor location and attachment

2004 ◽  
Vol 60 (1) ◽  
pp. S288-S289 ◽  
Author(s):  
E.D. Brandner ◽  
A. Wu ◽  
H. Chen ◽  
D. Heron ◽  
S. Kalnicki ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Location ◽  
Tumor Motion ◽  
4D Ct

WE-C-AUD C-09: Quantitative Assessment of Respiratory-Induced Lung Tumor Motion

2008 ◽  
Vol 35 (6Part23) ◽  
pp. 2937-2937
Author(s):  
S Jabbari ◽  
E Kurien ◽  
W Smith ◽  
P Dunscombe
Keyword(s):  
Quantitative Assessment ◽  
Lung Tumor ◽  
Tumor Motion

scholarly journals Dynamic MLC Tracking Using 4D Lung Tumor Motion Modelling and EPID Feedback

2018 ◽  
Author(s):  
N Rostampour ◽  
K Jabbari ◽  
Sh Nabavi ◽  
M Mohammadi ◽  
M Esmaeili
Keyword(s):  
Respiratory Motion ◽  
Lung Tumor ◽  
Statistical Criterion ◽  
Mean Square ◽  
Tumor Motion ◽  
4D Ct ◽  
Improve Treatment ◽  
Neuro Fuzzy ◽  
Targeting Accuracy ◽  
Good Agreement

Background: Respiratory motion causes thoracic movement and reduces targeting accuracy in radiotherapy.Objective: This study proposes an approach to generate a model to track lung tumor motion by controlling dynamic multi-leaf collimators.Material and Methods: All slices which contained tumor were contoured in the 4D-CT images for 10 patients. For modelling of respiratory motion, the end-exhale phase of these images has been considered as the reference and they were analyzed using neuro-fuzzy method to predict the magnitude of displacement of the lung tumor. Then, the predicted data were used to determine the leaf motion in MLC. Finally, the trained algorithm was figured out using Shaper software to show how MLCs could track the moving tumor and then imported on the Varian Linac equipped with EPID.Results: The root mean square error (RMSE) was used as a statistical criterion in order to investigate the accuracy of neuro-fuzzy performance in lung tumor prediction. The results showed that RMSE did not have a considerable variation. Also, there was a good agreement between the images obtained by EPID and Shaper for a respiratory cycle.Conclusion: The approach used in this study can track the moving tumor with MLC based on the 4D modelling, so it can improve treatment accuracy, dose conformity and sparing of healthy tissues because of low error in margins that can be ignored. Therefore, this method can work more accurately as compared with the gating and invasive approaches using markers.

Lung tumor motion measured using retrospective 4D CT and correlated with tumor location and attachment

2004 ◽  
Vol 60 ◽  
pp. S288-S289 ◽  
Author(s):  
E BRANDNER ◽  
A WU ◽  
H CHEN ◽  
D HERON ◽  
S KALNICKI ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Location ◽  
Tumor Motion ◽  
4D Ct

SU-FF-J-109: Comparison of Lung Tumor Motion Profiles From 4D CT and a Treatment Tracking System

2009 ◽  
Vol 36 (6Part7) ◽  
pp. 2501-2501
Author(s):  
N Yu ◽  
C Chuang ◽  
A Gottschalk ◽  
S Yom ◽  
P Xia
Keyword(s):  
Lung Tumor ◽  
Tracking System ◽  
Tumor Motion ◽  
4D Ct

Quantification of the geometric uncertainty when using implanted markers as a surrogate for lung tumor motion

2021 ◽  
Author(s):  
Nicholas Hardcastle ◽  
Adam Briggs ◽  
Vincent Caillet ◽  
Giorgios Angelis ◽  
Danielle Chrystall ◽  
...  
Keyword(s):  
Lung Tumor ◽  
Tumor Motion ◽  
Geometric Uncertainty

On using an adaptive neural network to predict lung tumor motion during respiration for radiotherapy applications

2005 ◽  
Vol 32 (12) ◽  
pp. 3801-3809 ◽  
Author(s):  
Marcus Isaksson ◽  
Joakim Jalden ◽  
Martin J. Murphy
Keyword(s):  
Neural Network ◽  
Lung Tumor ◽  
Tumor Motion ◽  
Adaptive Neural Network
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