Respiratory Motion Estimation with Hybrid Implementation

2013 ◽  
pp. 67-89
Author(s):  
Suk Jin Lee ◽  
Yuichi Motai
Keyword(s):  
Motion Estimation ◽  
Respiratory Motion

Diffeomorphic Respiratory Motion Estimation of Thoracoabdominal Organs for Image‐Guided Interventions

2021 ◽  
Author(s):  
Long Lei ◽  
Li Huang ◽  
Baoliang Zhao ◽  
Ying Hu ◽  
Zhongliang Jiang ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Respiratory Motion ◽  
Image Guided

A Regularized Approach For Respiratory Motion Estimation From Short-Time Projection Data Frames In Emission Tomography

2021 ◽  
Author(s):  
Andoni I. Garmendia ◽  
Yongyi Yang ◽  
Chao Song ◽  
Miles N. Wernick ◽  
P. Hendrik Pretorius ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Respiratory Motion ◽  
Emission Tomography ◽  
Projection Data ◽  
Time Projection ◽  
Short Time

scholarly journals Autoadaptive motion modelling for MR-based respiratory motion estimation

2017 ◽  
Vol 35 ◽  
pp. 83-100 ◽  
Author(s):  
Christian F. Baumgartner ◽  
Christoph Kolbitsch ◽  
Jamie R. McClelland ◽  
Daniel Rueckert ◽  
Andrew P. King
Keyword(s):  
Motion Estimation ◽  
Respiratory Motion

Simulation of Range Imaging-based Estimation of Respiratory Lung Motion

2014 ◽  
Vol 53 (04) ◽  
pp. 257-263 ◽  
Author(s):  
R. Werner ◽  
M. Blendowski ◽  
J. Ortmüller ◽  
H. Handels ◽  
M. Wilms
Keyword(s):  
Motion Estimation ◽  
Simulation Study ◽  
Respiratory Motion ◽  
Internal Motion ◽  
Estimation Accuracy ◽  
Patient Specific ◽  
Range Images ◽  
Motion Patterns ◽  
Multidimensional Signals ◽  
Lung Motion

SummaryObjectives: A major problem associated with the irradiation of thoracic and abdominal tumors is respiratory motion. In clinical practice, motion compensation approaches are frequently steered by low-dimensional breathing signals (e.g., spirometry) and patient-specific correspondence models, which are used to estimate the sought internal motion given a signal measurement. Recently, the use of multidimensional signals derived from range images of the moving skin surface has been proposed to better account for complex motion patterns. In this work, a simulation study is carried out to investigate the motion estimation accuracy of such multidimensional signals and the influence of noise, the signal dimensionality, and different sampling patterns (points, lines, regions).Methods: A diffeomorphic correspondence modeling framework is employed to relate multidimensional breathing signals derived from simulated range images to internal motion patterns represented by diffeomorphic non-linear transformations. Furthermore, an automatic approach for the selection of optimal signal combinations/patterns within this framework is presented.Results: This simulation study focuses on lung motion estimation and is based on 28 4D CT data sets. The results show that the use of multidimensional signals instead of one-dimensional signals significantly improves the motion estimation accuracy, which is, however, highly affected by noise. Only small differences exist between different multidimensional sampling patterns (lines and regions). Automatically determined optimal combinations of points and lines do not lead to accuracy improvements compared to results obtained by using all points or lines.Conclusions: Our results show the potential of multidimensional breathing signals derived from range images for the model-based estimation of respiratory motion in radiation therapy.

scholarly journals Respiratory motion estimation of the liver with abdominal motion as a surrogate

2018 ◽  
Vol 14 (6) ◽  
pp. e1940
Author(s):  
Shamel Fahmi ◽  
Frank F.J. Simonis ◽  
Momen Abayazid
Keyword(s):  
Motion Estimation ◽  
Respiratory Motion

Subpopulation-based correspondence modelling for improved respiratory motion estimation in the presence of inter-fraction motion variations

2017 ◽  
Vol 62 (14) ◽  
pp. 5823-5839 ◽  
Author(s):  
Matthias Wilms ◽  
René Werner ◽  
Tokihiro Yamamoto ◽  
Heinz Handels ◽  
Jan Ehrhardt
Keyword(s):  
Motion Estimation ◽  
Respiratory Motion

Technical Note: Fast respiratory motion estimation using sorted singles without unlist processing: A feasibility study

2017 ◽  
Vol 44 (5) ◽  
pp. 1632-1637 ◽  
Author(s):  
Jaewon Yang ◽  
Mehdi Khalighi ◽  
Thomas A. Hope ◽  
Karen Ordovas ◽  
Youngho Seo
Keyword(s):  
Motion Estimation ◽  
Feasibility Study ◽  
Respiratory Motion ◽  
Technical Note
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