Download Evidence of Structural Remodeling in the Dyssynchronous Failing

Remodeling in the Dyssynchronous Failing Heart
CIRCRESAHA/2005/111013/R1
Evidence of Structural Remodeling in the Dyssynchronous Failing Heart
Patrick A. Helm†, Laurent Younes‡, Mirza F. Beg**, Daniel B. Ennis††, Christophe Leclercq*,
Owen P. Faris††, Elliot McVeigh††, David Kass*, Michael I. Miller‡, Raimond L. Winslow†
Centers for Cardiovascular Bioinformatics & Modeling† and Imaging Science‡ and Division of
Cardiology*, Johns Hopkins University, Baltimore, MD 21205
**Engineering Science, Simon Fraiser University, Burnaby, BC V5A1S6, Canada
††
Laboratory of Cardiac Energetics, National Heart Lung Blood Institute, National Institute of
Health, Bethesda, MD 20892
Abbreviated Running Title: Remodeling in the Dyssynchronous Heart
Subject Codes: 104,110
Corresponding Author: Patrick Helm, 202 Clark Hall, Johns Hopkins University, 3400N. Charles
St., Baltimore MD 21218. Email:[email protected] Phone:410-516-5466
Total Word Count: 5995
Pages: 33
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Abstract
Ventricular remodeling of both geometry and fiber structure is a prominent feature of
several cardiac pathologies. Advances in magnetic resonance imaging and analytical methods
now make it possible to measure changes of cardiac geometry, fiber and sheet orientation at high
spatial resolution. In this report, we use diffusion tensor imaging to measure the geometry, fiber
and sheet architecture of eight normal and five dyssynchronous failing canine hearts, which were
explanted and fixed in an unloaded state. We apply novel computational methods to identify
statistically significant changes of cardiac anatomic structure in the failing and control heart
populations. The results demonstrate significant regional differences in geometric remodeling in
the dyssynchronous failing heart versus control. Ventricular chamber dilatation and reduction in
wall thickness in septal and some posterior and anterior regions are observed. Primary fiber
orientation showed no significant change. However, this result coupled with the local wall
thinning in the septum implies an altered transmural fiber gradient. Further, we observe that
orientation of laminar sheets become more vertical in the early-activated septum, with no
significant change of sheet orientation in the late-activated lateral wall.
Measured changes in
both fiber gradient and sheet structure will affect both the heterogeneity of passive myocardial
properties as well as electrical activation of the ventricles.
Keywords: Cardiac Remodeling, Computational Anatomy, Fiber Architecture, Dyssynchrony
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Introduction
Dilated cardiomyopathy (DCM) is one of the most common forms of heart muscle
disease. In this disease, chronic hemodynamic overload leads to increased wall stress.
Subsequent adaptive responses of the heart include both ventricular chamber dilation and
myocyte hypertrophy to equalize wall stress1-3. While such adaptations help to maintain
pump function over the short term, chronically, inadequate compensation leads to heart
failure. Patients with DCM may also exhibit intraventricular conduction delays,
particularly of the left bundle branch (LBB) type4-6. The resulting asynchronous electrical
activation produces dyssynchronous mechanical contraction and regional heterogeneity in
wall stress7-9. In particular, strain magnitude shows a marked phase delay between early
septal and late lateral shortening, with early systolic stretch of the lateral wall occurring
during septal shortening10. In patients with congenital heart block and chronic RV
pacing, dyssynchronous contraction has been shown to result in significant structural
remodeling of left ventricular (LV) morphology as the early activated septum thins and
the later-activated lateral wall hypertrophies11. Understanding LV remodeling in the
dyssynchronous failing heart, including alteration of ventricular geometry as well as fiber
architecture, is therefore of critical importance to understanding DCM and such an
understanding may lead to improved pacing therapies that will reverse remodeling12-14.
Ventricular myocardium has a complex structural organization. Fiber orientation was first
studied by means of fiber dissections15-17 and histologic measurements in transmural
plugs of ventricular tissue18-20. The principle conclusions of this work were that cardiac
fibers are arranged as counter-wound helices encircling the ventricles and that fiber
orientation is a function of transmural location, with fiber direction being oriented
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predominantly in the base-apex direction on the epicardial and endocardial surfaces and
rotating to a circumferential direction in the midwall. Subsequently, LeGrice et al.
showed that collagen binds adjacent myocytes together, forming layers known as lamina
or sheets21. Reorientation of sheets are responsible for substantial wall thickening during
contraction22.
Nielsen et al. developed histological methods for the systematic
reconstruction of cardiac ventricular geometry and primary fiber organization and
efficient computational methods for representing these anatomical data through the use of
finite-element (FE) models23.
Development of these approaches has contributed
significantly to our understanding of ventricular anatomy and its influence on both
electrical and mechanical properties. However, histological reconstruction has
limitations; taking weeks to months to reconstruct a single heart and yielding a low
spatial resolution.
Diffusion tensor imaging24 (DTI) has emerged as a means to overcome both these
limitations by allowing non-invasive characterization of cardiac tissue structure25,26. In
this technique, a tensor that represents the 3D diffusion of water in each image voxel is
estimated. The primary eigenvector of this tensor points in the direction in which the rate
of diffusion is largest. Comparing DTI with histological measurements, it has been
shown that the primary eigenvector of the diffusion tensor is aligned with the cardiac
fiber long axis25,26. In addition, in myocardial regions where diffusion is anisotropic, we
have recently shown that the tertiary eigenvector of the diffusion tensor is aligned with
the surface normal to the cardiac sheets27. Finally, we have demonstrated that DTI may
be used to reconstruct the fiber and sheet architecture of the heart at a spatial resolution of
350x350x800µm, yielding fiber structure estimates at two orders of magnitude more
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points in a fraction of the time (~40 hours versus weeks to months per heart) than can be
achieved histologically28. We have used these methods to image and reconstruct the
geometry and fiber structure of populations of normal and failing canine hearts.
Using these imaging data, we have recently developed computational methods to quantify
variation of anatomic structure both within and between populations of DTI reconstructed
hearts28,29. The method is adapted from the field of computational anatomy30,31, whereby
different anatomies are registered to an anatomical template using the large deformation
diffeomorphic metric mapping (LDDMM) method32. Prior, computational methods have
used low-dimensional transformations requiring landmarks33 or high-dimensional
transformations but suffer from small deformation assumptions34,35. LDDMM uses highdimensional transformation working under the assumption of large deformations thereby
enabling registration of cardiac pathology which may have large geometric variation.
Similar methods have been used previously to analyze brain structures and their variation
in disease, in particular, to identify deformations in the shape of the hippocampus that
discriminate patients with schizophrenia from matched controls and to define
hippocampal shape and volume abnormalities in patients with mild symptoms of
Alzheimer's disease36. Here, we combine, for the first time, high resolution DTMRI with
rigorous computational methods to quantify differences in the anatomic structure of a
population of normal and dyssynchronous failing canine hearts.
Materials and Methods
Model of Dyssynchronous Heart Failure
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The experimental model of cardiac dyssynchrony and heart failure was generated
in canines via radio-frequency ablation of the LBB followed by three weeks of
tachypacing (210min-1)37,38. This model has been well described in the literature as one
which reproduces many of the molecular, cellular, and structural features of human
dilated cardiomyopathy39-41.
Once ventricular dyssynchrony and heart failure were
established, animals were euthanized and hearts explanted. Hearts were retrogradely
perfused with a formalin solution and fixed in an unloaded state before imaging with
DTMRI42.. All studies were conducted in accordance with the guidelines of the Animal
Care and Use Committee of the National Heart, Lung, and Blood Institute.
Diffusion Tensor Magnetic Resonance Imaging
Details of the imaging sequence have been reported previously27,43. Hearts were placed
in an acrylic container filled with Fomblin (Ausimon, Thorofare, NJ) and a 3D Fast SpinEcho (3D-FSE) sequence was used to acquire diffusion images. MR parameters varied
slightly depending on heart size. The field of view ranged from 8-10cm yielding image
in-plane resolutions of 312.2x312.5 - 390x390µm. The volume was imaged with 110-120
slices at 800-1000µm thickness. Diffusion gradients were applied in a minimum of
sixteen non-collinear directions with a maximum diffusion weighting of 1500s/mm2.
DTI Processing and Ventricular Fiber / Sheet Analysis
The three principal components were computed from the diffusion tensors at each image
voxel24,26. Voxels in the 3D DTMRI images representing compact myocardium were
identified using a semi-automated contouring method43. LV geometry was modeled
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using 3D finite elements (FE), as described by Nielsen et al.23. In the contouring process
endocardial trabeculation was removed from the images, allowing a smoother description
of the geometry and consequently smaller endocardial fitting errors than those reported
by Nielsen et al.23.
Diffusion eigenvectors originating from voxels within the FE
boundaries were used to study the fiber structure of the ventricles. These eigenvectors
were transformed into the local geometric coordinates of the model as described by
LeGrice et al.44.
Fiber angle, θ, was defined as the angle formed by the local
circumferential tangent vector and the projection of the primary eigenvector, 1', onto the
epicardial tangent plane, as shown in Fig 2. Using methods developed previously for the
analysis of DTI27, regions of each heart exhibiting anisotropic diffusion were identified.
The sheet angle, φ, (see Fig. 2) was computed in these regions, where π/2+φ was defined
as the angle formed by the radial vector and the projection of the tertiary eigenvector 3'
into the plane defined by radial and circumferential vectors.
Anatomical Registration ( Large Deformation Diffeomorphic Metric Mapping)
Anatomical variability is studied by placement of imaged anatomic structures into
standard coordinates using the LDDMM algorithm29,45. A flow chart and accompanying
illustrations shown in Fig. 1 summarize the method. MR images of each heart were
contoured and re-sampled to a common uniform (900 µm) lattice. A normal canine heart
from the set of imaged hearts was chosen as an initial template anatomy (#1 in Fig. 1B).
Using the LDDMM algorithm, high order transformations were computed to register
(green arrows) the template anatomy to the remaining hearts (target anatomies) such that
every voxel in the template heart maps to a voxel in each target heart. The quality of
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registration is measured by the ratio of overlapping image-intensity volumes subsequent
to transformation to that prior to transformation.
The mean trajectory (red arrow)
computed from each mapping (green arrows) is used to deform the template, defining yet
another new template anatomy. The process is iterated until the mean trajectory length is
zero. The resulting template anatomy is called the Procrustes mean and resides within
the center of possible geometric shapes (Fig. 1C).
Geometric variation about this Procrustes mean is studied using Principle
Component Analysis (PCA) of the final mapping trajectories (green arrows, Fig 1C). In
PCA, a set of orthogonal basis vectors oriented along the directions of maximal variance
of the data are computed45.
Under high-order transformations, such as those computed using LDDMM, the
principle eigenvectors, which define fiber architecture, are not all preserved46,47. As a
result reorientation of the eigenvector must be performed if they are transformed in the
mapping.
Alternatively, the eigenvectors may be referenced within a geometric
coordinate system, which deforms with the anatomy, thereby eliminating the need to
reorient the eigenvectors. In this report, instead of performing structure analysis on the
eigenvectors in the space of the template, we use the mapping method to identify
corresponding regions within each un-deformed heart and study fiber /sheet angles
(derived from eigenvectors and referenced in local geometric coordinates) within these
regions. For example, within the template anatomy, an ROI is defined and using the
target specific mappings a corresponding ROI is defined on each target. Structural
differences within a population are observed by comparing fiber angles, within each
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corresponding target ROI. Mean values of this pooled information from target ROIs are
naturally visualized within the template ROI.
Geometric Analysis
Referencing geometric images of each target heart into the common coordinate
system of the template, as described above, enables quantitative comparison of geometric
features within corresponding regions of different imaged hearts. Evaluation of relative
wall thickness (RWT) was performed by comparing corresponding regions in each target
heart. The Laplace relation defines wall stress as being inversely proportional to RWT,
defined as the ratio between wall thickness and chamber radius.
RWT provides an
indication of myocyte hypertophy relative to ventricular dilation and is well preserved in
normal hearts, independent of body weight and ventricular volume48,49. Epicardial points
in the template were identified and corresponding points in each target were computed
using the LDDMM method. The wall thickness at these points was defined as the length
of the transmural segment between each epicardial point and the nearest endocardial
point.
The radius of the ventricle was determined from a best fitting circle that
encompassed all endocardial points on corresponding slices in each target.
Statistical Analysis
Using the LDDMM method, RWT, fiber and sheet angles within corresponding regions
of each target heart were determined. Statistical significance (p) for geometry was
determined using a non-paired Student's t-test. Circular statistics50 were used to compute
the circular mean and circular variance of fiber and sheet angles. Statistical significance
for angles was determined using Watson-Williams F-test50. P-values <0.01 were
considered statistically significant.
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Results
LDDMM Matching
Eight normal and five failing explanted canine hearts were imaged using 3D-FSEDTMRI. The average registration error for matching the template heart to the normal
targets was 5.80±1.47% and for matching the template heart to the failing hearts
4.42±0.47% These values are not statistically different from each other and are
comparable to those reported in the literature for matching of brain imagery51,
52
and
demonstrates that the LDDMM method successfully overlays cardiac target geometries
with high accuracy to a common coordinate system29.
Geometric Variability
The mean geometry for the normal population was computed using a Procrustes
alignment (Fig. 1) and used as the normal template. Regions in the anterior, lateral,
posterior, and septum of this template were selected and corresponding regions in each
target anatomy were identified. RWT within corresponding regions at the base and apex
are shown in Table 1. The normal population showed an average RWT of 0.52±0.04
with no statistical difference over the entire ventricle except for the apical septum.
Within the apical septum, the RWT was larger at 0.61±0.04. This difference may be
attributed to the errors associated with identifying the RV endocardial surface given the
extent of trabeculation within the RV apex. The failing population showed a significant
reduction of this ratio, indicating a greater ventricular dilation relative to myocyte
hypertophy. There was also significant regional disparity of this ratio. RWT within the
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lateral region was significantly different (p<0.01) than that found in the septal wall,
(0.42±0.07 and 0.29±0.06, respectively). Regional disparity was not as evident in the
apical regions. Differences in RWT between the normal and failing populations are
depicted in Fig. 3. The top row (Fig. 3A) shows the template heart (in anterior/posterior
view and two cut-away perspectives) colored according to mean RWT for the normal
population. The middle row (Fig. 3B) shows the template heart colored according to
mean RWT for the failing population. Regional disparity is clearly evident.
The
statistical differences between normal and failing populations are shown graphically in
the bottom row (Fig. 3C). The template heart is colored such that regional differences that
are statistically significant (p<0.01) are blue and regional differences that are not
significant are cyan.
We next examined geometric variability of the normal population about the normal
template using PCA applied to the mapping trajectories defined from the template
anatomy. Analyses were limited to assessment of the principal eigenvector αn, (i.e.
direction of highest geometric variability). Figure 4 shows different time steps in the
evolution of the normal template (middle top image) in the direction of αn (top right) and
-αn (top left).
The insertion of the RV shows the greatest variability in the normal
population. There is some septal wall deviation, which may be due in part to variability
in the RV insertion.
Figure 4 also shows evolution of the normal template (middle
bottom image) in the principal direction of highest variation for the failing population, αf.
As expected, the most notable variation was dilation of the left ventricular chamber and
reduction of wall thickness.
Movies showing the deformation of the template heart in
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the direction of highest geometric variance for both normal and failing populations are
available in the online supplement.
Fiber structure variability
Before studying diffusion eigenvectors, a ventricular coordinate system from which fiber
and sheet angles are referenced was defined. On average, each MR study resulted in
50,000 epicardial and 27,000 LV endocardial data points to which the radial component
of a 24-element prolate spheroidal FE mesh was fit. The average root mean squared
(RMS) errors of the FE fit on epicardial and left ventricular endocardial surface were
0.81±1.08 mm and 0.68±0.83 mm, respectively. Fiber and sheet angles were determined
from the primary and tertiary eigenvectors using the ventricular coordinate system as
shown in Fig 2. On average, each study resulted in 550,000 measurements of fiber and
sheet angle within the myocardium.
Plots of mean fiber angle in the normal and failing hearts are shown in Fig. 5.
The top row (Fig. 5A) shows the template heart colored according to mean fiber angle for
the normal population while the middle row (Fig. 5B) shows the template heart colored
according to average fiber angle for the failing population. The bottom row (Fig. 5C)
displays the statistical significance of regional differences in fiber orientation between the
normal and failing hearts. Despite large regional differences in wall thickness in the
septal and lateral walls, the majority of the LV showed no significant change in fiber
structure. Some regions on the epicardial and endocardial surfaces showed an increase in
fiber angle for the failing population as noted in yellowish color but these differences
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were not significant at p<0.01. These data therefore demonstrate that fiber orientation
assessed at corresponding points in failing and control hearts are largely similar.
Laminar structure variability
Variability of laminar structure was also studied. The top row (Fig. 6A) shows the
template heart colored according to dominant sheet angles for the normal population.
Two dominant populations of sheet angles were identified: one at approximately -45° and
the other at +45°. The majority of the lateral wall (both apex and base) showed a single
dominant angle at approximately -45°, while the majority of the septum showed a
dominant angle of +45° at the base and -45° at the apex.
Transmural variation in the
dominant angles is also seen in some LV regions as the angles toggled from -45 to +45
and sometimes back (lateral wall). A statistical assessment of differences in laminar
structure between normal and failing hearts is shown in the bottom (Fig. 6C) set of
images.
The majority of the LV showed no significant difference between the laminar
structure in the normal and failing populations; however in the septal region of the failing
heart both population of sheet angles (±45) became steeper (less radial) by about 15°.
These differences were statistically significant (p<0.01).
Discussion
To our knowledge this is the first report which combines high resolution diffusion
tensor imaging techniques with methods of computational anatomy to demonstrate
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quantitatively, global and local remodeling in a population of normal and failing hearts
with a LBBB. There are several important findings.
First, our results show regional differences in anatomical remodeling in the
dyssynchronous failing heart. Significant ventricular chamber dilatation and reduction of
RWT in septal, posterior and anterior regions was observed, while the lateral wall
showed little change in RWT.
It has been shown previously that the tachycardia-
induced heart failure model leads to greater ventricular dilation relative to ventricular
mass41; hence, RWT should reduce as compared with normal hearts. We observed a
similar global reduction in RWT as a result of tachycardia pacing. However, our model
additionally includes a LBB block. Such dyssynchronous electrical activation results in
early and late mechanical activated regions of myocardium and hence subjects these
regions to different loading conditions.
In early systole, the late-activated lateral wall
dilates (stretches) due to contraction of the early-activated septum.
A reversal of this
effect occurs during late systole/early diastole when the lateral wall contracts against a
relaxing septum. These regional differences in wall stress ultimately lead to different
patterns of hypertrophy/remodeling as we observed.
Prinzen et al. showed similar
differences in hypertrophy response in early-activated versus late-activated myocardium
using ventricular pacing studies53.
Second, despite regional differences in gross remodeling, fiber orientation at
corresponding points remains similar in control versus failing hearts. These results are
consistent with other canine models of failure induced with arteriovenous fistule. Such
studies indicate global dilation with little change in fiber orientation1, 54. These results,
coupled with gross remodeling of wall thickness (particularly in the septum) imply a
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statistically significant change in transmural fiber gradient. Given that the gradient of
fiber orientation is a major determinant of electrical propagation55, this has important
implications for electrical activation of the ventricle.
Finally, we demonstrate significant (p<0.01) regional difference in sheet structure
remodeling which may explain regional difference in wall thickness. Specifically, we
find an increase of sheet angle in the early-activated septum but no change in that of the
late-activated lateral wall. These data imply a possible slowing of transmural electrical
conduction, since conductivity is smaller in the cross-sheet direction.
A possible
mechanism of remodeling occurring in the septum is shown graphically for a small
transmural tissue slab in Fig. 7.
The normal sheet structure is shown in panel A. As
sheet angle φ increases, the laminar sheets assume a more vertical orientation, thus
reducing overall wall thickness (panel B). This mechanism has been proposed and
substantiated by others54. Such reorientation also implies an elongation of the septal
wall, which is observable in figure 4. Unlike the lateral wall, the septal wall showed
large variation in its apex to base arc-length. We observed no significant changes in the
laminar architecture of the lateral wall.
These data have important clinical implications. Mechanical dyssynchrony is
common in patients with dilated cardiomyopathy, ventricular pacemakers and/or postmyocardial infarction.
Our data shows regional differences in remodeling in the
dyssynchronous failing heart particularly within the laminar sheet structure.
Sheet
structure influences the apparent passive properties of myocardium; thus regional
differences in sheet structure may impart heterogeneity of myocardial function within the
ventricle.
Although it is yet unclear whether these structural changes coupled with
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altered electrical conduction due to increased fiber gradient are beneficial or detrimental
to function, a better appreciation of the remodeling process is important in the
understanding of, improvement in and development of therapeutics.
In conclusion, unlike previous studies of myofiber architecture, we have
employed a high resolution, 3D imaging modality coupled with rigorous mathematical
assessment of variability to demonstrate significant regional changes in ventricular
structure in the dyssynchronous, failing heart, thereby enabling quantitative
understanding of the nature of ventricular remodeling and heart failure progression
Acknowledgements
Work supported by NIH HL70894, HL52307, Natural Sciences and Engineering Research
Council of Canada 31-611387, Falk Medical Trust and IBM Corporation. DB Ennis was
supported by a pre-IRTA fellowship from NHLBI.
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List of Tables:
Table 1: Tabulated are RWT (x 100) measurements for various regions within the LV of
normal and failing canine hearts. † denotes statistical significance (p<0.01) relative to
basal lateral wall of normal population. Within in the failing population * denotes
statistical significance (p<0.01) relative to basal lateral wall.
List of Figures:
Figure 1: A simplified flow chart (A) and accompanying illustrations (B,C) summarizes
the computational anatomy methods. A normal heart was chosen as an initial template
anatomy from a set of imaged heart structures. Using the LDDMM, different high order
transformations were computed to map (green arrows) the template anatomy to the
remaining hearts (target anatomies) (B). The mean trajectory (red arrow) computed from
each mapping (green arrows) was used to deform the template, defining yet another new
template anatomy. The process was iterated until the mean trajectory from the template
anatomy to each target heart was zero. At this stage the template anatomy resided within
the center of possible geometric shapes (C).
Figure 2: The coordinate system used to define the fiber angle θ and the sheet angle φ.
The fiber angle, θ, is the angle formed by the circumferential tangent vector and the
projection of the primary eigenvector, 1', into the epicardial tangent plane. The sheet
angle, φ, is defined relative to the tertiary eigenvector of diffusion where π/2+φ is the
angle formed by the radial vector and the projection of the tertiary eigenvector, 3', into
the plane defined by radial and circumferential vectors.
Figure 3: The top row (A) shows the template heart (three different views) colored
according to the mean RWT for the normal population. The middle row (B) shows the
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template heart color according to the mean RWT for the failing population. Note the
reduced thickness in the septal wall. The RV (gray) was not analyzed but included in the
figure for a reference. The bottom row (C) shows the statistical difference (p) as
determined by the Student's t-test between the normal and failing population. The color
cyan represents no difference and the color blue represents a statistical difference at
(p<0.01).
Figure 4: With αn defined as a vector pointing in the direction of highest geometric
variance of a population on normals (see methods), we illustrate the evolution of normal
template (middle top image) in the direction of αn (top right) and -αn (top left).
The
insertion of RV (gray arrows) shows highest variability in the normal population. There is
some septal wall deviation (gray arrows) mostly due to the variability in RV insertion.
In the bottom row, we illustrate the evolution of the normal template in the principal
direction of highest geometric variance, αf for the failing population. Changes occurred
in the LV diameter (gray arrows) , wall thickness and expanse of the septal wall.
Figure 5: The top row (A) shows the template heart (three different views) colored
according to average fiber angle [degrees] for the normal population. The middle row
(B) shows the template heart color according to average fiber angle for the failing
population. There is a very slight increase in fiber angles on the epicardial and
endocardial surface for the failing heart as noted by the more yellow/red color on these
surfaces. The bottom row (C) shows the statistical difference between the normal and
failing population. The color cyan represents no difference and the color blue represents
a statistical difference at (p<0.01).
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Figure 6: The top row (A) shows the template heart (three different views) colored
according to sheet angle [degrees] for the normal population. The middle row (B) shows
the template heart color according to sheet angle [degrees] for the failing population. The
bottom row (C) shows the statistical difference between the normal and failing
population. The color cyan represents no difference and the color blue represents a
statistical difference at (p<0.01).
Figure 7: Shown is an illustration of sheet remodeling that accounts for reduction in wall
thickness. Shown in (a) are normal sheet orientation and (b) altered sheet orientation.
The sheets become more vertical (less radial) and hence the wall becomes thinner.
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Figure 1
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Figure 2
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Figure 3
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Figure 4
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Figure 5
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Figure 6
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Figure 7
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Table 1
RWT x
100
Normal
(n=8)
Failing
(n=5)
Bas. Ant.
Bas. Lat.
Bas. Pos.
Bas. Sep.
Api. Ant.
Api. Lat.
Api. Pos.
Api. Sep.
50±3
51±3
50±3
49±4
57±10
51±11
49±9
64±14†
40±6†
42±7†
37±7†
29±6*†
41±8†
32±5*†
29±6*†
41±20
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