scholarly journals Strain Measurement Using Deformable Image Registration

2006 ◽  
pp. 489-501 ◽  
Author(s):  
J. A. Weiss ◽  
A. I. Veress ◽  
G. T. Gullberg ◽  
N. S. Phatak ◽  
Q. Sun ◽  
...  
Keyword(s):  
Image Registration ◽  
Strain Measurement ◽  
Deformable Image Registration

Strain Measurement in the Left Ventricle During Systole with Deformable Image Registration

2007 ◽  
pp. 32-40 ◽  
Author(s):  
Nikhil S. Phatak ◽  
Steve A. Maas ◽  
Alexander I. Veress ◽  
Nathan A. Pack ◽  
Edward V. R. Di Bella ◽  
...  
Keyword(s):  
Image Registration ◽  
Left Ventricle ◽  
Strain Measurement ◽  
Deformable Image Registration

scholarly journals Strain measurement in the left ventricle during systole with deformable image registration

2009 ◽  
Vol 13 (2) ◽  
pp. 354-361 ◽  
Author(s):  
Nikhil S. Phatak ◽  
Steve A. Maas ◽  
Alexander I. Veress ◽  
Nathan A. Pack ◽  
Edward V.R. Di Bella ◽  
...  
Keyword(s):  
Image Registration ◽  
Left Ventricle ◽  
Strain Measurement ◽  
Deformable Image Registration

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.

scholarly journals PO-0952: Validation of margin recipes in endometrial cancer IMRT using deformable image registration

2018 ◽  
Vol 127 ◽  
pp. S520-S521
Author(s):  
I. White ◽  
D. McQuaid ◽  
A. Dunlop ◽  
S. Court ◽  
N. Hopkins ◽  
...  
Keyword(s):  
Endometrial Cancer ◽  
Image Registration ◽  
Deformable Image Registration

scholarly journals Evaluation of deformable image registration (DIR) methods for dose accumulation in nasopharyngeal cancer patients during radiotherapy

2017 ◽  
Vol 51 (4) ◽  
pp. 438-446 ◽  
Author(s):  
Wannapha Nobnop ◽  
Imjai Chitapanarux ◽  
Hudsaleark Neamin ◽  
Somsak Wanwilairat ◽  
Vicharn Lorvidhaya ◽  
...  
Keyword(s):  
Spinal Cord ◽  
Image Registration ◽  
Treatment Plan ◽  
Nasopharyngeal Cancer ◽  
Treatment Strategies ◽  
Deformable Image Registration ◽  
Organ Dose ◽  
Target Dose ◽  
Dose Accumulation ◽  
The Impact

Abstract Introduction Deformable image registration (DIR) is used to modify structures according to anatomical changes for observing the dosimetric effect. In this study, megavoltage computed tomography (MVCT) images were used to generate cumulative doses for nasopharyngeal cancer (NPC) patients by various DIR methods. The performance of the multiple DIR methods was analysed, and the impact of dose accumulation was assessed. Patients and methods The study consisted of five NPC patients treated with a helical tomotherapy unit. The weekly MVCT images at the 1st, 6th, 11th, 16th, 21st, 26th, and 31st fractions were used to assess the dose accumulation by the four DIR methods. The cumulative dose deviations from the initial treatment plan were analysed, and correlations of these variations with the anatomic changes and DIR methods were explored. Results The target dose received a slightly different result from the initial plan at the end of the treatment. The organ dose differences increased as the treatment progressed to 6.8% (range: 2.2 to 10.9%), 15.2% (range: -1.7 to 36.3%), and 6.4% (range: -1.6 to 13.2%) for the right parotid, the left parotid, and the spinal cord, respectively. The mean uncertainty values to estimate the accumulated doses for all the DIR methods were 0.21 ± 0.11 Gy (target dose), 1.99 ± 0.76 Gy (right parotid), 1.19 ± 0.24 Gy (left parotid), and 0.41 ± 0.04 Gy (spinal cord). Conclusions Accuracy of the DIR methods affects the estimation of dose accumulation on both the target dose and the organ dose. The DIR methods provide an adequate dose estimation technique for observation as a result of inter-fractional anatomic changes and are beneficial for adaptive treatment strategies.

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