SU-FF-J-127: Three-Dimensional Deformable Image Registration Using ALE Moving Mesh

2007 ◽  
Vol 34 (6Part6) ◽  
pp. 2397-2397
Author(s):  
J Li ◽  
T Zhu
Keyword(s):  
Image Registration ◽  
Three Dimensional ◽  
Deformable Image Registration ◽  
Moving Mesh

scholarly journals Estimation of three-dimensional intrinsic dosimetric uncertainties resulting from using deformable image registration for dose mapping

2010 ◽  
Vol 38 (1) ◽  
pp. 343-353 ◽  
Author(s):  
Francisco J. Salguero ◽  
Nahla K. Saleh-Sayah ◽  
Chenyu Yan ◽  
Jeffrey V. Siebers
Keyword(s):  
Image Registration ◽  
Three Dimensional ◽  
Deformable Image Registration ◽  
Dose Mapping

TU-C-141-10: A Three-Dimensional Thermoplastic Prostate Phantom for Evaluation of Deformable Image Registration

2013 ◽  
Vol 40 (6Part26) ◽  
pp. 435-436
Author(s):  
C Reber ◽  
A Neo ◽  
E Schoenhoff ◽  
N Kirby ◽  
K Singhrao ◽  
...  
Keyword(s):  
Image Registration ◽  
Three Dimensional ◽  
Deformable Image Registration

scholarly journals Evaluation of Deformable Image Registration for Three-Dimensional Temporal Subtraction of Chest Computed Tomography Images

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Yan ◽  
Yoshie Kodera ◽  
Kazuhiro Shimamoto
Keyword(s):  
Computed Tomography ◽  
Image Registration ◽  
Three Dimensional ◽  
Deformable Image Registration ◽  
Ct Images ◽  
Temporal Changes ◽  
Transformation Model ◽  
Chest Computed Tomography ◽  
Temporal Subtraction ◽  
Computed Tomography Images

Purpose. To perform lung image registration for reducing misregistration artifacts on three-dimensional (3D) temporal subtraction of chest computed tomography (CT) images, in order to enhance temporal changes in lung lesions and evaluate these changes after deformable image registration (DIR). Methods. In 10 cases, mutual information (MI) lung mask affine mapping combined with cross-correlation (CC) lung diffeomorphic mapping was used to implement lung volume registration. With advanced normalization tools (ANTs), we used greedy symmetric normalization (greedy SyN) as a transformation model, which involved MI-CC-SyN implementation. The resulting displacement fields were applied to warp the previous (moving) image, which was subsequently subtracted from the current (fixed) image to obtain the lung subtraction image. Results. The average minimum and maximum log-Jacobians were 0.31 and 3.74, respectively. When considering 3D landmark distance, the root-mean-square error changed from an average of 20.82 mm for Pfixed to Pmoving to 0.5 mm for Pwarped to Pfixed. Clear shadows were observed as enhanced lung nodules and lesions in subtraction images. The lesion shadows showed lesion shrinkage changes over time. Lesion tissue morphology was maintained after DIR. Conclusions. DIR (greedy SyN) effectively and accurately enhanced temporal changes in chest CT images and decreased misregistration artifacts in temporal subtraction images.

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.

OC-0444: Impact of deformable image registration on inter-fractional variations in lung cancer proton therapy

2020 ◽  
Vol 152 ◽  
pp. S245
Author(s):  
L. Nenoff ◽  
C.O. Ribeiro ◽  
M. Matter ◽  
L. Hafner ◽  
A.C. Knopf ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Image Registration ◽  
Proton Therapy ◽  
Deformable Image Registration
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