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

Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions

2019 ◽  
Vol 66 (2) ◽  
pp. 302-310 ◽  
Author(s):  
In Young Ha ◽  
Matthias Wilms ◽  
Heinz Handels ◽  
Mattias P. Heinrich
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Respiratory Motion ◽  
Image Guided ◽  
Model Based ◽  
Dense Image

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 Implications of respiratory motion for small animal image-guided radiotherapy

2017 ◽  
Vol 90 (1069) ◽  
pp. 20160482 ◽  
Author(s):  
Mark A Hill ◽  
Borivoj Vojnovic
Keyword(s):  
Respiratory Motion ◽  
Small Animal ◽  
Image Guided Radiotherapy ◽  
Image Guided ◽  
Animal Image

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

scholarly journals Fast and accurate vision‐based stereo reconstruction and motion estimation for image‐guided liver surgery

2018 ◽  
Vol 5 (5) ◽  
pp. 208-214
Author(s):  
Andrew D. Speers ◽  
Burton Ma ◽  
William R. Jarnagin ◽  
Sharifa Himidan ◽  
Amber L. Simpson ◽  
...  
Keyword(s):  
Motion Estimation ◽  
Liver Surgery ◽  
Image Guided ◽  
Stereo Reconstruction

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
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