MO-FF-A4-04: A Method to Construct Synthetic Maps of Spatially Correlated Deformable Image Registration Errors by Random Sampling of De-Correlated Error Modes

2010 ◽  
Vol 37 (6Part6) ◽  
pp. 3366-3366
Author(s):  
D Staub ◽  
C Vaman ◽  
M Murphy
Keyword(s):  
Image Registration ◽  
Random Sampling ◽  
Deformable Image Registration ◽  
Spatially Correlated ◽  
Registration Errors ◽  
Correlated Error

TU-C-141-11: Dosimetric Consequences of Deformable Image Registration Errors for Lung SBRT Patients

2013 ◽  
Vol 40 (6Part26) ◽  
pp. 436-436
Author(s):  
H Zhong ◽  
J Kim ◽  
C Glide-Hurst ◽  
I Chetty
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Registration Errors ◽  
Lung Sbrt

Representing the dosimetric impact of deformable image registration errors

2017 ◽  
Vol 62 (17) ◽  
pp. N391-N403 ◽  
Author(s):  
Jason Vickress ◽  
Jerry Battista ◽  
Rob Barnett ◽  
Slav Yartsev
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Registration Errors

scholarly journals A finite element method to correct deformable image registration errors in low-contrast regions

2012 ◽  
Vol 57 (11) ◽  
pp. 3499-3515 ◽  
Author(s):  
Hualiang Zhong ◽  
Jinkoo Kim ◽  
Haisen Li ◽  
Teamour Nurushev ◽  
Benjamin Movsas ◽  
...  
Keyword(s):  
Finite Element Method ◽  
Finite Element ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Low Contrast ◽  
Registration Errors ◽  
Element Method

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

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.

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