Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples

MacLeod, Robert S.; Stinstra, J.G.; Lew, Seok; Whitaker, Ross T.; Swenson, Darrell; Cole, M.J.; Krüger, Jens; Brooks, Dana H.; Johnson, Chris R.

doi:10.1098/rsta.2008.0314

Cited by 27 publications

(24 citation statements)

References 48 publications

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“…In addition we mention tools like Brainstorm (Tadel et al 2011), MNE (Gramfort et al 2014), OpenMEEG (Gramfort et al 2010(Gramfort et al , 2011, and SimBio-NeuroFEM (Wolters et al 2002). There also exists a complete open-source software pipeline for all these steps provided by ImageVis3D, Seg3D, BioMesh3D, and SCIRun (MacLeod et al 2009). …”

Section: Head Geometry Brain Connectivity and Signal Expressionmentioning

confidence: 98%

Neuroimaging, Neural Population Models for

Bojak¹,

Breakspear²

2014

Encyclopedia of Computational Neuroscience

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SynonymsEEG, neural population models of; fMRI BOLD, neural population models of; Multimodal neural population models DefinitionNeuroimaging denotes measurements of the brain with sufficient spatial resolution to construct maps of anatomy (structural neuroimaging) and activity (functional neuroimaging), respectively. Where a functional neuroimaging modality can capture spatial snapshots at sufficient speed, its data can be used to infer spatiotemporal brain dynamics. The spatial resolution of noninvasive functional neuroimaging in humans is limited to about one millimeter and requires the coherent activity of a large number of neurons to generate a signal that exceeds the noise floor. This is an ideal match for neural population models (NPMs), which are designed to capture the mass activity of neural groupings at mesoscopic scales and above. However, in order to compare NPM predictions to neuroimaging data, one also has to include signal expression -a measurement function -that maps the NPM-generated activity onto the sensor recordings. For most popular neuroimaging modalities, including multimodal approaches, such forward modelling "pipelines" are now available and have been used to predict and analyze data.

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Section: Head Geometry Brain Connectivity and Signal Expressionmentioning

confidence: 98%

Neuroimaging, Neural Population Models for

Bojak¹,

Breakspear²

2014

Encyclopedia of Computational Neuroscience

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“…9) Image-Based Modeling Frameworks and Standards: As the paradigm behind the Physiome and the VPH gains widespread acceptance in the scientific domain, there is an urgent requirement to demonstrate its translational value into the clinics and its adoption by industry. Focusing in the specific area of image-based modeling, a number of toolkits 1 have been developed to facilitate the task of building both patient-specific image-based models [15], [94]- [96] and for clinical prototyping of image-based modeling tools [77]. Such image-based modeling frameworks are complementary to the various simulation tools available within the VPH Toolkit [97]- [99] and, in some cases, are part of it.…”

Section: ) Integration and Fusion Of Multimodal Imaging And Multidimmentioning

confidence: 99%

Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

Frangi

Hose

Hunter

et al. 2013

IEEE Trans. Med. Imaging

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International audienceThe January 2013 Special Issue of IEEE transactions on medical imaging discusses papers on medical imaging and image computing in computational physiology. Aslanid and co-researchers present an experimental technique based on stained micro computed tomography (CT) images to construct very detailed atrial models of the canine heart. The paper by Sebastian proposes a model of the cardiac conduction system (CCS) based on structural information derived from stained calf tissue. Ho, Mithraratne and Hunter present a numerical simulation of detailed cerebral venous flow. The third category of papers deals with computational methods for simulating medical imagery and incorporate knowledge of imaging physics and physiology/biophysics. The work by Morales showed how the combination of device modeling and virtual deployment, in addition to patient-specific image-based anatomical modeling, can help to carry out patient-specific treatment plans and assess alternative therapeutic strategies

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“…Computational models of the heart have evolved to become an important tool for understanding several types of arrhythmias like AF [4]- [7], atrial flutter or ventricular fibrillation [8]- [11]. Also various genetic diseases leading to arrhythmias like Long-QT-or Short-QT-syndrome can be simulated successfully [12].…”

Section: Introductionmentioning

confidence: 99%

A framework for personalization of computational models of the human atria

Dössel

Krueger

Weber

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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A framework for step-by-step personalization of a computational model of human atria is presented. Beginning with anatomical modeling based on CT or MRI data, next fiber structure is superimposed using a rule-based method. If available, late-enhancement-MRI images can be considered in order to mark fibrotic tissue. A first estimate of individual electrophysiology is gained from BSPM data solving the inverse problem of ECG. A final adjustment of electrophysiology is realized using intracardiac measurements. The framework is applied using several patient data. First clinical application will be computer assisted planning of RF-ablation for treatment of atrial flutter and atrial fibrillation.

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Subject-specific, multiscale simulation of electrophysiology: a software pipeline for image-based models and application examples

Cited by 27 publications

References 48 publications

Neuroimaging, Neural Population Models for

Neuroimaging, Neural Population Models for

Guest Editorial Special Issue on Medical Imaging and Image Computing in Computational Physiology

A framework for personalization of computational models of the human atria

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