scholarly journals Quadratic penalty method for intensity‐based deformable image registration and 4DCT lung motion recovery

2019 ◽  
Vol 46 (5) ◽  
pp. 2194-2203 ◽  
Author(s):  
Edward Castillo
Keyword(s):  
Image Registration ◽  
Penalty Method ◽  
Deformable Image Registration ◽  
Motion Recovery ◽  
Lung Motion ◽  
Quadratic Penalty Method

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

SU-F-BRB-01: How Effective Is Abdominal Compression at Reducing Lung Motion? An Analysis Using Deformable Image Registration Within Different Sub-Regions of the Lung

2015 ◽  
Vol 42 (6Part25) ◽  
pp. 3529-3529
Author(s):  
D Paradiso ◽  
A Pearce ◽  
K Leszczynski ◽  
M Oliver
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Abdominal Compression ◽  
Lung Motion

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.

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

scholarly journals Detection of vessel bifurcations in CT scans for automatic objective assessment of deformable image registration accuracy

2021 ◽  
Author(s):  
Guillaume Cazoulat ◽  
Brian M Anderson ◽  
Molly M McCulloch ◽  
Bastien Rigaud ◽  
Eugene J Koay ◽  
...  
Keyword(s):  
Image Registration ◽  
Objective Assessment ◽  
Deformable Image Registration ◽  
Ct Scans ◽  
Registration Accuracy ◽  
Vessel Bifurcations

PO-1508: Exploration of deformable image registration to augment training data for deep learning contouring

2020 ◽  
Vol 152 ◽  
pp. S814
Author(s):  
R. Baggs ◽  
P. Aljabar ◽  
M. Gooding ◽  
P. Poortmans ◽  
Y. Kirova
Keyword(s):  
Deep Learning ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Training Data

scholarly journals Tissue-specific deformable image registration using a spatial-contextual filter

2021 ◽  
Vol 88 ◽  
pp. 101849
Author(s):  
Yongbin Zhang ◽  
Lifei Zhang ◽  
Laurence E. Court ◽  
Peter Balter ◽  
Lei Dong ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Tissue Specific

scholarly journals Evolutionary Machine Learning for Multi-Objective Class Solutions in Medical Deformable Image Registration

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 99 ◽  
Author(s):  
Kleopatra Pirpinia ◽  
Peter A. N. Bosman ◽  
Jan-Jakob Sonke ◽  
Marcel van Herk ◽  
Tanja Alderliesten
Keyword(s):  
Machine Learning ◽  
Image Registration ◽  
State Of The Art ◽  
Deformable Image Registration ◽  
Optimization Approach ◽  
High Quality ◽  
Trade Off ◽  
Multi Objective ◽  
Current State ◽  
Image Artefacts

Current state-of-the-art medical deformable image registration (DIR) methods optimize a weighted sum of key objectives of interest. Having a pre-determined weight combination that leads to high-quality results for any instance of a specific DIR problem (i.e., a class solution) would facilitate clinical application of DIR. However, such a combination can vary widely for each instance and is currently often manually determined. A multi-objective optimization approach for DIR removes the need for manual tuning, providing a set of high-quality trade-off solutions. Here, we investigate machine learning for a multi-objective class solution, i.e., not a single weight combination, but a set thereof, that, when used on any instance of a specific DIR problem, approximates such a set of trade-off solutions. To this end, we employed a multi-objective evolutionary algorithm to learn sets of weight combinations for three breast DIR problems of increasing difficulty: 10 prone-prone cases, 4 prone-supine cases with limited deformations and 6 prone-supine cases with larger deformations and image artefacts. Clinically-acceptable results were obtained for the first two problems. Therefore, for DIR problems with limited deformations, a multi-objective class solution can be machine learned and used to compute straightforwardly multiple high-quality DIR outcomes, potentially leading to more efficient use of DIR in clinical practice.

Sign in / Sign up

Export Citation Format

Share Document