scholarly journals Validation of Finite Element Image Registration‐based Cardiac Strain Estimation from Magnetic Resonance Images

PAMM ◽  
2019 ◽  
Vol 19 (1) ◽  
Author(s):  
Ezgi Berberoğlu ◽  
Christian Stoeck ◽  
Philippe Moireau ◽  
Sebastian Kozerke ◽  
Martin Genet
Keyword(s):  
Finite Element ◽  
Magnetic Resonance ◽  
Image Registration ◽  
Magnetic Resonance Images ◽  
Strain Estimation ◽  
Cardiac Strain

Analysis of serial magnetic resonance images of mouse brains using image registration

NeuroImage ◽  
2009 ◽  
Vol 44 (3) ◽  
pp. 692-700 ◽  
Author(s):  
Satheesh Maheswaran ◽  
Hervé Barjat ◽  
Simon T. Bate ◽  
Paul Aljabar ◽  
Derek L.G. Hill ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Magnetic Resonance Images

Biomechanical Mechanisms and Hemodynamics in the Case of Stenosis

2019 ◽  
Vol 9 (10) ◽  
pp. 1334-1338
Author(s):  
Yu-Chou Huang ◽  
Han-Yi Cheng
Keyword(s):  
Mechanical Properties ◽  
Finite Element Method ◽  
Finite Element ◽  
Blood Flow ◽  
Magnetic Resonance ◽  
Blood Flow Velocity ◽  
Three Dimensional ◽  
Vascular Wall ◽  
Magnetic Resonance Images ◽  
Artery Segment

The objective of the present study is to investigate the blood flow of the artery with stenosis using finite element method. Three-dimensional 3-D artery models were reconstructed to simulate blood hemodynamic behaviors from magnetic resonance images. Many papers have studied 3-D finite element artery models, but few have examined the effects of different stenosis thicknesses in arteries. It is imperative to incorporate the mechanical properties of a diseased artery segment into treatment planning because stress is a strong biological trigger that directs atherosclerosis protection. Stress may also have predictive value to pinpoint regions at risk for restenosis. The results showed that stenosis of a 1 mm thickness decreased the blood flow velocity about 48%. This confirmed that stenosis also induces abnormal stress in the narrowest position of a vascular wall. This research provides information for arteries with stenosis in clinical treatment.

scholarly journals Evaluation of Interpolation Effects on Upsampling and Accuracy of Cost Functions-Based Optimized Automatic Image Registration

2013 ◽  
Vol 2013 ◽  
pp. 1-19 ◽  
Author(s):  
Amir Pasha Mahmoudzadeh ◽  
Nasser H. Kashou
Keyword(s):  
Magnetic Resonance ◽  
High Resolution ◽  
Image Registration ◽  
Magnetic Resonance Images ◽  
Qualitative Assessment ◽  
Cost Functions ◽  
Low Resolution ◽  
Registration Method ◽  
B Spline ◽  
Joint Histogram

Interpolation has become a default operation in image processing and medical imaging and is one of the important factors in the success of an intensity-based registration method. Interpolation is needed if the fractional unit of motion is not matched and located on the high resolution (HR) grid. The purpose of this work is to present a systematic evaluation of eight standard interpolation techniques (trilinear, nearest neighbor, cubic Lagrangian, quintic Lagrangian, hepatic Lagrangian, windowed Sinc, B-spline 3rd order, and B-spline 4th order) and to compare the effect of cost functions (least squares (LS), normalized mutual information (NMI), normalized cross correlation (NCC), and correlation ratio (CR)) for optimized automatic image registration (OAIR) on 3D spoiled gradient recalled (SPGR) magnetic resonance images (MRI) of the brain acquired using a 3T GE MR scanner. Subsampling was performed in the axial, sagittal, and coronal directions to emulate three low resolution datasets. Afterwards, the low resolution datasets were upsampled using different interpolation methods, and they were then compared to the high resolution data. The mean squared error, peak signal to noise, joint entropy, and cost functions were computed for quantitative assessment of the method. Magnetic resonance image scans and joint histogram were used for qualitative assessment of the method.

Subvoxel Image Registration of Multislice (2D) Magnetic Resonance Images in Patients with High-grade Gliomas of the Brain

2002 ◽  
Vol 57 (12) ◽  
pp. 1098-1108 ◽  
Author(s):  
A Oatridge ◽  
J.V Hajnal ◽  
N Saeed ◽  
E.S Newlands ◽  
W.L Curati ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Magnetic Resonance Images ◽  
High Grade ◽  
High Grade Gliomas ◽  
The Brain

Non-rigid image registration of brain magnetic resonance images using graph-cuts

2011 ◽  
Vol 44 (10-11) ◽  
pp. 2450-2467 ◽  
Author(s):  
Ronald W.K. So ◽  
Tommy W.H. Tang ◽  
Albert C.S. Chung
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Graph Cuts ◽  
Magnetic Resonance Images

scholarly journals User Guide to The Computational Morphometry Toolkit

2011 ◽  
Author(s):  
Torsten Rohlfing
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Hypothesis Testing ◽  
Source Code ◽  
Magnetic Resonance Images ◽  
Target Audience ◽  
Statistical Hypothesis ◽  
Jacobian Determinant ◽  
Statistical Hypothesis Testing ◽  
Map Generation

This guide is intended as a very brief introduction of the main tools in the Computational Morphometry Toolkit (CMTK), which is available in source code and as precompiled binaries from http://www.nitrc.org/projects/cmtk/. The target audience of this document are CMTK users, who might use this document as a reference to the most common processing tasks, and prospective users, who may find this information useful to determine whether CMTK provides functionality that they can use. We focus in particular on a simplified workflow for deformation morphometry studies based on magnetic resonance images: DICOM conversion, artifact correction, affine and nonlinear image registration, reformatting, Jacobian determinant map generation, and statistical hypothesis testing.

scholarly journals Accuracy of deformable image registration on magnetic resonance images in digital and physical phantoms

2017 ◽  
Vol 44 (10) ◽  
pp. 5153-5161 ◽  
Author(s):  
Rachel B. Ger ◽  
Jinzhong Yang ◽  
Yao Ding ◽  
Megan C. Jacobsen ◽  
Clifton D. Fuller ◽  
...  
Keyword(s):  
Magnetic Resonance ◽  
Image Registration ◽  
Deformable Image Registration ◽  
Magnetic Resonance Images
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