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Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function.

Bishop MJ, Plank G, Burton RA, Schneider JE, Gavaghan DJ, Grau V, Kohl P - Am. J. Physiol. Heart Circ. Physiol. (2009)

Bottom Line: Simulation results were compared with those from a simplified model built from the same images but excluding finer anatomical features (vessels/endocardial structures).Postshock, these differences resulted in the genesis of new excitation wavefronts that were not observed in more simplified models.In conclusion, structurally simplified models are well suited for a large range of cardiac modeling applications.

View Article: PubMed Central - PubMed

Affiliation: University of Oxford Computing Laboratory, Parks Road, Oxford OX1 3QD, UK. martin.bishop@comlab.ox.ac.uk

ABSTRACT
Recent advances in magnetic resonance (MR) imaging technology have unveiled a wealth of information regarding cardiac histoanatomical complexity. However, methods to faithfully translate this level of fine-scale structural detail into computational whole ventricular models are still in their infancy, and, thus, the relevance of this additional complexity for simulations of cardiac function has yet to be elucidated. Here, we describe the development of a highly detailed finite-element computational model (resolution: approximately 125 microm) of rabbit ventricles constructed from high-resolution MR data (raw data resolution: 43 x 43 x 36 microm), including the processes of segmentation (using a combination of level-set approaches), identification of relevant anatomical features, mesh generation, and myocyte orientation representation (using a rule-based approach). Full access is provided to the completed model and MR data. Simulation results were compared with those from a simplified model built from the same images but excluding finer anatomical features (vessels/endocardial structures). Initial simulations showed that the presence of trabeculations can provide shortcut paths for excitation, causing regional differences in activation after pacing between models. Endocardial structures gave rise to small-scale virtual electrodes upon the application of external field stimulation, which appeared to protect parts of the endocardium in the complex model from strong polarizations, whereas intramural virtual electrodes caused by blood vessels and extracellular cleft spaces appeared to reduce polarization of the epicardium. Postshock, these differences resulted in the genesis of new excitation wavefronts that were not observed in more simplified models. Furthermore, global differences in the stimulus recovery rates of apex/base regions were observed, causing differences in the ensuing arrhythmogenic episodes. In conclusion, structurally simplified models are well suited for a large range of cardiac modeling applications. However, important differences are seen when behavior at microscales is relevant, particularly when examining the effects of external electrical stimulation on tissue electrophysiology and arrhythmia induction. This highlights the utility of histoanatomically detailed models for investigations of cardiac function, in particular for future patient-specific modeling.

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Related in: MedlinePlus

Results of the automated sequential segmentation pipeline shown in the transverse (top) and frontal (bottom) slices. A: unsegmented MR dataset. B: output from the first stage in the segmentation pipeline, the threshold level-set filter, which acts as a good approximate initial segmentation. C: output from the geodesic active contour filter. D: final result of the segmentation pipeline following the Laplacian level-set filter.
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Figure 2: Results of the automated sequential segmentation pipeline shown in the transverse (top) and frontal (bottom) slices. A: unsegmented MR dataset. B: output from the first stage in the segmentation pipeline, the threshold level-set filter, which acts as a good approximate initial segmentation. C: output from the geodesic active contour filter. D: final result of the segmentation pipeline following the Laplacian level-set filter.

Mentions: The full segmentation pipeline consisted of the following steps. First, an initial approximate segmentation was performed using a level-set threshold filter, following prior automated selection of seed points throughout the volume of the dataset (Fig. 2B). Seeds were automatically chosen based on explicit local intensity values using a very conservative threshold to avoid placing seeds within tissue voxels. The propagation term of the evolving contour (P in Eq. 1) in the threshold level-set method is dependent on user-defined lower and upper threshold limits. By selecting relatively high values for these limits, an approximate segmentation could be achieved without the fronts “leaking” into regions of tissue. However, as shown by the highlighted regions in the top left and bottom right corners of Fig. 2B, this also has the effect of leaving many areas of the background outside the volume of the heart incorrectly defined (due to the above-mentioned effects of the spatial variations in coil sensitivity) and of missing smaller structures within the myocardium.


Development of an anatomically detailed MRI-derived rabbit ventricular model and assessment of its impact on simulations of electrophysiological function.

Bishop MJ, Plank G, Burton RA, Schneider JE, Gavaghan DJ, Grau V, Kohl P - Am. J. Physiol. Heart Circ. Physiol. (2009)

Results of the automated sequential segmentation pipeline shown in the transverse (top) and frontal (bottom) slices. A: unsegmented MR dataset. B: output from the first stage in the segmentation pipeline, the threshold level-set filter, which acts as a good approximate initial segmentation. C: output from the geodesic active contour filter. D: final result of the segmentation pipeline following the Laplacian level-set filter.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2822578&req=5

Figure 2: Results of the automated sequential segmentation pipeline shown in the transverse (top) and frontal (bottom) slices. A: unsegmented MR dataset. B: output from the first stage in the segmentation pipeline, the threshold level-set filter, which acts as a good approximate initial segmentation. C: output from the geodesic active contour filter. D: final result of the segmentation pipeline following the Laplacian level-set filter.
Mentions: The full segmentation pipeline consisted of the following steps. First, an initial approximate segmentation was performed using a level-set threshold filter, following prior automated selection of seed points throughout the volume of the dataset (Fig. 2B). Seeds were automatically chosen based on explicit local intensity values using a very conservative threshold to avoid placing seeds within tissue voxels. The propagation term of the evolving contour (P in Eq. 1) in the threshold level-set method is dependent on user-defined lower and upper threshold limits. By selecting relatively high values for these limits, an approximate segmentation could be achieved without the fronts “leaking” into regions of tissue. However, as shown by the highlighted regions in the top left and bottom right corners of Fig. 2B, this also has the effect of leaving many areas of the background outside the volume of the heart incorrectly defined (due to the above-mentioned effects of the spatial variations in coil sensitivity) and of missing smaller structures within the myocardium.

Bottom Line: Simulation results were compared with those from a simplified model built from the same images but excluding finer anatomical features (vessels/endocardial structures).Postshock, these differences resulted in the genesis of new excitation wavefronts that were not observed in more simplified models.In conclusion, structurally simplified models are well suited for a large range of cardiac modeling applications.

View Article: PubMed Central - PubMed

Affiliation: University of Oxford Computing Laboratory, Parks Road, Oxford OX1 3QD, UK. martin.bishop@comlab.ox.ac.uk

ABSTRACT
Recent advances in magnetic resonance (MR) imaging technology have unveiled a wealth of information regarding cardiac histoanatomical complexity. However, methods to faithfully translate this level of fine-scale structural detail into computational whole ventricular models are still in their infancy, and, thus, the relevance of this additional complexity for simulations of cardiac function has yet to be elucidated. Here, we describe the development of a highly detailed finite-element computational model (resolution: approximately 125 microm) of rabbit ventricles constructed from high-resolution MR data (raw data resolution: 43 x 43 x 36 microm), including the processes of segmentation (using a combination of level-set approaches), identification of relevant anatomical features, mesh generation, and myocyte orientation representation (using a rule-based approach). Full access is provided to the completed model and MR data. Simulation results were compared with those from a simplified model built from the same images but excluding finer anatomical features (vessels/endocardial structures). Initial simulations showed that the presence of trabeculations can provide shortcut paths for excitation, causing regional differences in activation after pacing between models. Endocardial structures gave rise to small-scale virtual electrodes upon the application of external field stimulation, which appeared to protect parts of the endocardium in the complex model from strong polarizations, whereas intramural virtual electrodes caused by blood vessels and extracellular cleft spaces appeared to reduce polarization of the epicardium. Postshock, these differences resulted in the genesis of new excitation wavefronts that were not observed in more simplified models. Furthermore, global differences in the stimulus recovery rates of apex/base regions were observed, causing differences in the ensuing arrhythmogenic episodes. In conclusion, structurally simplified models are well suited for a large range of cardiac modeling applications. However, important differences are seen when behavior at microscales is relevant, particularly when examining the effects of external electrical stimulation on tissue electrophysiology and arrhythmia induction. This highlights the utility of histoanatomically detailed models for investigations of cardiac function, in particular for future patient-specific modeling.

Show MeSH
Related in: MedlinePlus