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

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

scholarly journals A hybrid patient-specific biomechanical model based image registration method for the motion estimation of lungs

2017 ◽  
Vol 39 ◽  
pp. 87-100 ◽  
Author(s):  
Lianghao Han ◽  
Hua Dong ◽  
Jamie R. McClelland ◽  
Liangxiu Han ◽  
David J. Hawkes ◽  
...  
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Biomechanical Model ◽  
Patient Specific ◽  
Registration Method ◽  
Model Based

scholarly journals Evaluation of synergistic image registration for motion-corrected coronary NaF-PET-MR

2021 ◽  
Vol 379 (2200) ◽  
Author(s):  
Johannes Mayer ◽  
Yining Jin ◽  
Thomas-Heinrich Wurster ◽  
Marcus R. Makowski ◽  
Christoph Kolbitsch
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Numerical Simulations ◽  
Respiratory Motion ◽  
Plaque Composition ◽  
Theme Issue ◽  
Tomographic Image Reconstruction ◽  
Combined Motion ◽  
Artery Disease

Coronary artery disease (CAD) is caused by the formation of plaques in the coronary arteries and is one of the most common cardiovascular diseases. NaF-PET can be used to assess plaque composition, which could be important for therapy planning. One of the main challenges of NaF-PET is cardiac and respiratory motion which can strongly impair diagnostic accuracy. In this study, we investigated the use of a synergistic image registration approach which combined motion-resolved MR and PET data to estimate cardiac and respiratory motion. This motion estimation could then be used to improve the NaF-PET image quality. The approach was evaluated with numerical simulations and in vivo scans of patients suffering from CAD. In numerical simulations, it was shown, that combining MR and PET information can improve the accuracy of motion estimation by more than 15%. For the in vivo scans, the synergistic image registration led to an improvement in uptake visualization. This is the first study to assess the benefit of combining MR and NaF-PET for cardiac and respiratory motion estimation. Further patient evaluation is required to fully evaluate the potential of this approach. This article is part of the theme issue ‘Synergistic tomographic image reconstruction: part 1’.

scholarly journals 4D-CT motion estimation using deformable image registration and 5D respiratory motion modeling

2008 ◽  
Vol 35 (10) ◽  
pp. 4577-4590 ◽  
Author(s):  
Deshan Yang ◽  
Wei Lu ◽  
Daniel A. Low ◽  
Joseph O. Deasy ◽  
Andrew J. Hope ◽  
...  
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Respiratory Motion ◽  
Deformable Image Registration ◽  
Motion Modeling ◽  
4D Ct

scholarly journals Non-rigid image registration using a modified fuzzy feature-based inference system for 3D cardiac motion estimation

2021 ◽  
Vol 205 ◽  
pp. 106085
Author(s):  
Monire Sheikh Hosseini ◽  
Mahammad Hassan Moradi ◽  
Mahdi Tabassian ◽  
Jan D'hooge
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Cardiac Motion ◽  
Inference System ◽  
Feature Based
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