scholarly journals Mjolnir: Extending HAMMER Using a Diffusion Transformation Model and Histogram Equalization for Deformable Image Registration

2009 ◽  
Vol 2009 ◽  
pp. 1-18 ◽  
Author(s):  
Lotta M. Ellingsen ◽  
Jerry L. Prince
Keyword(s):  
Image Registration ◽  
Medical Image Analysis ◽  
Computation Time ◽  
Deformable Image Registration ◽  
Histogram Equalization ◽  
Image Features ◽  
Transformation Model ◽  
Brain Images ◽  
Novel Approach ◽  
Feature Correspondences

Image registration is a crucial step in many medical image analysis procedures such as image fusion, surgical planning, segmentation and labeling, and shape comparison in population or longitudinal studies. A new approach to volumetric intersubject deformable image registration is presented. The method, called Mjolnir, is an extension of the highly successful method HAMMER. New image features in order to better localize points of correspondence between the two images are introduced as well as a novel approach to generate a dense displacement field based upon the weighted diffusion of automatically derived feature correspondences. An extensive validation of the algorithm was performed on T1-weighted SPGR MR brain images from the NIREP evaluation database. The results were compared with results generated by HAMMER and are shown to yield significant improvements in cortical alignment as well as reduced computation time.

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 The distance discordance metric—a novel approach to quantifying spatial uncertainties in intra- and inter-patient deformable image registration

2014 ◽  
Vol 59 (3) ◽  
pp. 733-746 ◽  
Author(s):  
Ziad H Saleh ◽  
Aditya P Apte ◽  
Gregory C Sharp ◽  
Nadezhda P Shusharina ◽  
Ya Wang ◽  
...  
Keyword(s):  
Image Registration ◽  
Deformable Image Registration ◽  
Novel Approach

scholarly journals Incorporating Metric Flows and Sparse Jacobian Transformations in ITK

2006 ◽  
Author(s):  
Mathieu De craene ◽  
Aloys Du bois d'aische ◽  
Benoit Macq ◽  
Simon Warfield
Keyword(s):  
Image Registration ◽  
Mutual Information ◽  
Computation Time ◽  
Deformable Registration ◽  
Deformation Field ◽  
Transformation Model ◽  
Rigid Registration ◽  
Information Metric ◽  
Demons Algorithm

Various metrics have been proposed in the literature for performing intrinsic automatic image to image registration. Among these measures, mutual information is a very popular one because of its robustness and accuracy for a wide variety of applications. In this paper, we propose a filter for performing non-rigid registration by estimating a dense deformation field derived from the mutual information metric. This filter takes place in the ITK PDE deformable registration design like the Demons algorithm of Thirion. We also show how the concept of metric flow is conceptually linked to the concept of metric derivative for a prior transformation model by the transformation jacobian. We also suggest a sparse implementation of the GetJacobian() method for reducing the computation time of a metric derivative for local transformations models.

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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