TH-CD-303-01: On Characterizing 4D Lung Motion Using Coupled Multi-Physics Framework of Computational Fluid Dynamics, Linear Elasticity Estimation and 4D Lung Deformable Image Registration

2015 ◽  
Vol 42 (6Part43) ◽  
pp. 3728-3728 ◽  
Author(s):  
A Santhanam ◽  
B Seyfi ◽  
D Thomas ◽  
O Ilegbusi
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Image Registration ◽  
Linear Elasticity ◽  
Deformable Image Registration ◽  
Lung Motion

MO-G-18C-03: Evaluation of Deformable Image Registration for Lung Motion Estimation Using Hyperpolarized Gas Tagging MRI

2014 ◽  
Vol 41 (6Part25) ◽  
pp. 439-440
Author(s):  
Q Huang ◽  
Y Zhang ◽  
Y Liu ◽  
L Hu ◽  
W Miller ◽  
...  
Keyword(s):  
Image Registration ◽  
Motion Estimation ◽  
Deformable Image Registration ◽  
Hyperpolarized Gas ◽  
Lung Motion ◽  
Tagging Mri

Estimation of Lung Motion Using Deformable Image Registration Based on Compressible Flow

2017 ◽  
Vol 7 (7) ◽  
pp. 1667-1670 ◽  
Author(s):  
Min Li ◽  
Limei Zhang ◽  
Zhichao Lian ◽  
Zhikang Xiang ◽  
Liang Xiao
Keyword(s):  
Image Registration ◽  
Compressible Flow ◽  
Deformable Image Registration ◽  
Lung Motion

scholarly journals Modeling Respiratory Signals by Deformable Image Registration on 4DCT Lung Images

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Pham The Bao ◽  
Hoang Thi Kieu Trang ◽  
Tran Anh Tuan ◽  
Tran Thien Thanh ◽  
Vo Hong Hai
Keyword(s):  
Image Registration ◽  
Hybrid Approach ◽  
Three Dimensional ◽  
Deformable Image Registration ◽  
Minimum Variance ◽  
Human Anatomy ◽  
Breath Hold ◽  
Breathing Motion ◽  
Lung Motion ◽  
Overall Evaluation

The lung organ of human anatomy captured by a medical device reveals inhalation and exhalation information for treatment and monitoring. Given a large number of slices covering an area of the lung, we have a set of three-dimensional lung data. And then, by combining additionally with breath-hold measurements, we have a dataset of multigroup CT images (called 4DCT image set) that could show the lung motion and deformation over time. Up to now, it has still been a challenging problem to model a respiratory signal representing patients’ breathing motion as well as simulating inhalation and exhalation process from 4DCT lung images because of its complexity. In this paper, we propose a promising hybrid approach incorporating the local binary pattern (LBP) histogram with entropy comparison to register the lung images. The segmentation process of the left and right lung is completely overcome by the minimum variance quantization and within class variance techniques which help the registration stage. The experiments are conducted on the 4DCT deformable image registration (DIR) public database giving us the overall evaluation on each stage: segmentation, registration, and modeling, to validate the effectiveness of the approach.

Efficient computational fluid dynamics mesh generation by image registration

2007 ◽  
Vol 11 (6) ◽  
pp. 648-662 ◽  
Author(s):  
D.C. Barber ◽  
E. Oubel ◽  
A.F. Frangi ◽  
D.R. Hose
Keyword(s):  
Fluid Dynamics ◽  
Computational Fluid Dynamics ◽  
Image Registration ◽  
Mesh Generation
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