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LV Motion and Strain Computation from tMRI Based on Meshless Deformable Models [chapter]

Xiaoxu Wang, Ting Chen, Shaoting Zhang, Dimitris Metaxas, Leon Axel
2008 Lecture Notes in Computer Science  
In particular, the model performs well even when the control points (tag intersections) are relatively sparse.  ...  We propose a novel meshless deformable model for in vivo Left Ventricle (LV) 3D motion estimation and analysis based on tagged MRI (tMRI).  ...  Conclusion We have proposed a meshless deformable model for in vivo LV 3D motion tracking and strain analysis based on tMRI.  ... 
doi:10.1007/978-3-540-85988-8_76 fatcat:3ickhkjncjdebhsiym3u3co4wq

Meshless deformable models for 3D cardiac motion and strain analysis from tagged MRI

Xiaoxu Wang, Ting Chen, Shaoting Zhang, Joël Schaerer, Zhen Qian, Suejung Huh, Dimitris Metaxas, Leon Axel
2015 Magnetic Resonance Imaging  
Due to the through-plane motion, the tracking of 3D trajectories of the material points and the computation of 3D strain field call for the necessity of building 3D cardiac deformable models.  ...  Meshless deformable models can track the trajectory of any material point in the myocardium and compute the 3D strain field of any particular area.  ...  Myocardium motion in each direction can be quantitatively measured by tracking the tagging lines.  ... 
doi:10.1016/j.mri.2014.08.007 pmid:25157446 pmcid:PMC4876045 fatcat:64qli6prtva67fwdohsrcj7wly

Fully Automated Myocardial Strain Estimation from Cardiovascular MRI–tagged Images Using a Deep Learning Framework in the UK Biobank

Edward Ferdian, Avan Suinesiaputra, Kenneth Fung, Nay Aung, Elena Lukaschuk, Ahmet Barutcu, Edd Maclean, Jose Paiva, Stefan K. Piechnik, Stefan Neubauer, Steffen E. Petersen, Alistair A. Young
2020 Radiology: Cardiothoracic Imaging  
To demonstrate the feasibility and performance of a fully automated deep learning framework to estimate myocardial strain from short-axis cardiac MRI-tagged images.  ...  The fully automated combined RNN and CNN framework for analysis of myocardial strain enabled unbiased strain evaluation in a high-throughput workflow, with similar ability to distinguish impairment due  ...  Figure 2 Overview of the machine learning framework for automatic myocardial strain estimation from CMR tagging.  ... 
doi:10.1148/ryct.2020190032 pmid:32715298 pmcid:PMC7051160 fatcat:vv5apgj63zhevnlwakmmcqjimm

A review of heart chamber segmentation for structural and functional analysis using cardiac magnetic resonance imaging

Peng Peng, Karim Lekadir, Ali Gooya, Ling Shao, Steffen E. Petersen, Alejandro F. Frangi
2016 Magnetic Resonance Materials in Physics, Biology and Medicine  
important role in clinical use of CMR. • Finally, we include newly emerged concepts in machine learning-based cardiac image analysis, such as direct estimation of cardiac function [14] [15] [16] [17]  ...  both long-axis and short-axis views [18] Fig. 4 4 Short-axis tagged MRI mid-cavity slices: a tagging produced at end-diastole; b-d tag lines deform with myocardial contraction in systole; e, f tag  ... 
doi:10.1007/s10334-015-0521-4 pmid:26811173 pmcid:PMC4830888 fatcat:cceypmtjrfbyjjtpkktjk7m7fi

Cardiac motion estimation by joint alignment of tagged MRI sequences

E. Oubel, M. De Craene, A.O. Hero, A. Pourmorteza, M. Huguet, G. Avegliano, B.H. Bijnens, A.F. Frangi
2012 Medical Image Analysis  
in tag intensity along the cardiac cycle.  ...  For as-48 sessing consistency with other modalities, we have studied two patients with 49 myocardial infarction and compared the strain maps with the information 50 provided by delayed-enhancement MRI  ...  On the other hand, finer grids add 256 unnecessary degrees of freedom to the transform (there are not material 257 points to track between two adjacent tags) and make the optimization process 258 more  ... 
doi:10.1016/ pmid:22000567 pmcid:PMC4401871 fatcat:2ydc4bwbozd5dkicu6wh3nuczu

Patient-Specific Inverse Modeling of In Vivo Cardiovascular Mechanics with Medical Image-Derived Kinematics as Input Data: Concepts, Methods, and Applications

Johane H. Bracamonte, Sarah K. Saunders, John S. Wilson, Uyen T. Truong, Joao S. Soares
2022 Applied Sciences  
patient-specific data acquired with medical imaging in inverse modeling approaches.  ...  Its incorporation into clinical practice has the potential to improve diagnosis and treatment planning with low associated risks and costs.  ...  MRI tissue tagging was used to estimate average strains in all 17 standard regions.  ... 
doi:10.3390/app12083954 fatcat:so7cwudvyfgprigztufgw7we2e

A review of deep learning in medical imaging: Image traits, technology trends, case studies with progress highlights, and future promises [article]

S. Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S. Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L. Prince, Daniel Rueckert, Ronald M. Summers
2020 arXiv   pre-print
It is known that the success of AI is mostly attributed to the availability of big data with annotations for a single task and the advances in high performance computing.  ...  In this survey paper, we first highlight both clinical needs and technical challenges in medical imaging and describe how emerging trends in deep learning are addressing these issues.  ...  One special type of dataset useful for tracking are MRI tagging acquisitions, and deep learning has recently played a role in tracking these tags and quantifying the displacement information for motion  ... 
arXiv:2008.09104v1 fatcat:z2gic7or4vgnnfcf4joimjha7i

A Pipeline for the Generation of Realistic 3D Synthetic Echocardiographic Sequences: Methodology and Open-Access Database

M. Alessandrini, M. De Craene, O. Bernard, S. Giffard-Roisin, P. Allain, I. Waechter-Stehle, J. Weese, E. Saloux, H. Delingette, M. Sermesant, J. D'hooge
2015 IEEE Transactions on Medical Imaging  
Quantification of cardiac deformation and strain with 3D ultrasound takes considerable research efforts.  ...  The simulated images show typical artifacts that make motion tracking in ultrasound challenging.  ...  In this case no intermodal registration is needed (unlike tagged MRI) and the exact motion/deformation is known at each point (voxel) inside the myocardium (unlike sonomicrometry).  ... 
doi:10.1109/tmi.2015.2396632 pmid:25643402 fatcat:qobpqrkmljatdpy6khuyw3bbva

Systems Biology through Mouse Imaging Centers: Experience and New Directions

R. Mark Henkelman
2010 Annual Review of Biomedical Engineering  
Mouse imaging combined with powerful statistical methods has a unique and growing role to play in phenotyping genetically modified mice.  ...  Much of the mammalian genome-to-phenotype relationship will be worked out in the mouse, for which powerful genetic-manipulation tools are available.  ...  providing advice on PET, to John Parkinson for providing the genome informatics, and to Fred Epstein for providing cardiac MR data.  ... 
doi:10.1146/annurev-bioeng-070909-105343 pmid:20415591 fatcat:zvs4nepdebgoroovxatthhl7le

Efficient Intelligent Systems for Healthcare Data Management and Delivery

Dr Krishna Prasad K
2021 Zenodo  
To this end, features to be analyzed in the structural MRI image can be measured by a PCA scheme, and each of the measured features are learned through a regularized extreme learning machine (RELM) learning  ...  Prediction of risk in patients with diabetes using machine learning.  ...  The Oxford COVID-19 Government Response Tracker tracks the level of rigor with which a government implements COVID-19 prevention and suppression measures.  ... 
doi:10.5281/zenodo.5148783 fatcat:zabqdvbnkbacrgritgzu2sedsy

A semi-automatic system for segmentation of cardiac M-mode images

Luca Bertelli, Rita Cucchiara, Giovanni Paternostro, Andrea Prati
2006 Pattern Analysis and Applications  
A common approach to the performance evaluation of classifier systems is based on the measurement of the classification errors and, at the same time, on the computational time.  ...  This paper analyzes different classifiers and proposes an evaluation of the classifiers in the case of semi-automatic processes with human interaction.  ...  Tagging can simplify the identification of heart borders in MRI, but methods exist also in cine MR to obtain borders without tagging, such as that proposed by Staib and Duncan [21] .  ... 
doi:10.1007/s10044-006-0034-x fatcat:uy7gvwkt2naunge3lwu33af6tm

A Magnetic-Resonance-Imaging-Compatible Remote Catheter Navigation System

M. A. Tavallaei, Y. Thakur, S. Haider, M. Drangova
2013 IEEE Transactions on Biomedical Engineering  
Mount To enable arbitrary positioning of the CM, with respect to the patient, and to allow for access to various entry points, a Mount was developed.  ...  However, as was shown in Fig. 3 the profiles tracked with the optical tracking system demonstrated a small but measurable variability in successive profiles.  ...  /publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.  ... 
doi:10.1109/tbme.2012.2229709 pmid:23192485 fatcat:lymzyj63knbgdewdy6tcmjku5a

Sparse Bayesian registration of medical images for self-tuning of parameters and spatially adaptive parametrization of displacements

Loïc Le Folgoc, Hervé Delingette, Antonio Criminisi, Nicholas Ayache
2017 Medical Image Analysis  
The performance of the proposed approach is demonstrated on time series of (cine and tagged) magnetic resonance and echocardiographic cardiac images.  ...  Adaptive parametrizations have been used with success to promote both the regularity and accuracy of registration schemes, but so far on non-probabilistic grounds -either as part of multiscale heuristics  ...  Finer bases were used more often in experiments with tagged MRI; in addition to the higher resolution of these volumes compared to cine MRI data, tags may have been regarded as reliable, informative structures  ... 
doi:10.1016/ pmid:27870999 fatcat:hf2ysjiqwncpla2iltrsj5627a

Robust spatio-temporal registration of 4D cardiac ultrasound sequences

Jørn Bersvendsen, Matthew Toews, Adriyana Danudibroto, William M. Wells, Stig Urheim, Raúl San José Estépar, Eigil Samset, Neb Duric, Brecht Heyde
2016 Medical Imaging 2016: Ultrasonic Imaging and Tomography  
The method is fully automatic and consists of three main steps, all solved with machine learning techniques.  ...  First, the segmentation is performed using a learned prediction step and a restricted model allowing no local deformations, which results in a rough segmentation and tracking of the RV during the cardiac  ...  Several methods have been proposed using elastic registration to study the motion of the heart walls, which can be used to estimate the strain in the myocardium [1]- [4] .  ... 
doi:10.1117/12.2217005 pmid:27516706 pmcid:PMC4976768 fatcat:bg656wug4fan3oact6wtgydqee

Non-invasive Model-Based Assessment of Passive Left-Ventricular Myocardial Stiffness in Healthy Subjects and in Patients with Non-ischemic Dilated Cardiomyopathy

Myrianthi Hadjicharalambous, Liya Asner, Radomir Chabiniok, Eva Sammut, James Wong, Devis Peressutti, Eric Kerfoot, Andrew King, Jack Lee, Reza Razavi, Nicolas Smith, Gerald Carr-White (+1 others)
2016 Annals of Biomedical Engineering  
In this paper we present personalised models of cardiac mechanics, focusing on improving model accuracy, while ensuring unique parametrisation.  ...  The influence of principal model uncertainties on accuracy and parameter identifiability was systematically assessed in a group of patients with dilated cardiomyopathy (n ¼ 3) and healthy volunteers (n  ...  in Medical Engineering (WT 088641/Z/09/Z).  ... 
doi:10.1007/s10439-016-1721-4 pmid:27605213 pmcid:PMC5479360 fatcat:5cgmxg4ixffzpm2iycos5d3tha
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