3D shape variability of the healthy and infarcted mouse heart

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3D shape variability of the healthy and infarcted
mouse heart

• Korbeeck, J.M.
• Eindhoven, July 1st 2004

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Contents

• Goals
• Anatomy heart
• Modes of LV deformation
• Method
• Tagging MRI
• Statistical Shape Models.
• Results
• Future research.

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Introduction

• Infarction ? major cause of death
• Temporal shape changes ? mechanical pumping
  efficiency ? warning system of heart failure?

From Stroke Facts 2004 All Americans,
American Heart Association, 2004
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Goals

• Study the left ventricle motion of the heart
• Design of Statistical Shape Model algorithm
• Interpretation of shape variability results ?
  physiological changes described in literature.

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Heart anatomy

• Left ventricle (LV) is studied
• Volume corresponds with stroke volume ?
  pumping-efficiency
• Blood through entire circulation ? thickest wall.

From Marieb1997, page 661
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Layers of the heart wall
From Marieb1997, page 658
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Modes of LV deformation

• Deformation
• Radial displacement
• Axial torsion
• Circumferential contraction with long axis
  extension.
• Rotation
• Translation.

From Arts1992
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Tagging MRI (C-SPAMM)

• Cine gradient echo MR image of beating heart
• C-SPAMM
• Tag pattern applied by applying magnetic field
  gradient
• Deformation of the myocardium can be calculated
  using phase tracking.

From Heijman2004
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Mouse heart

• Left ventricle
• Large
• Thick wall.
• Right ventricle
• Smaller
• Thinner wall ? tagging MRI not yet possible.

posterior
myocardium
RV wall
LV
RV
anterior
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Statistical Shape Models

• Modelling shape and shape variation
• Without shape assumptions.
• Shape represented by set of points in time
• Model the variation using PCA.

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Principal Component Analysis

• Parameterised model
• Reduce dimensionality
• Eigenvectors of covariance
• Eigenvectors ? main directions
• Eigenvalue ? variance along eigenvector.

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Algorithm

• Represent points of 2D image as vector x
• Compute the mean and covariance
• Compute ? and ? of S, approximation of x
• Choose t largest eigenvalues such that

where fv defines the proportion of total variation
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Example
From Cootes2004
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Cardiac Motion Model
From Suinesiaputra2002
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Results

• Normal (i.e. healthy) heart
• Heart with infarction (LDA occluded by ligation)
• Slice through infarction
• Slice above infarction.

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Eigenvalues

• Healthy heart ? more eigenvalues ? mix of more
  different shape variabilities
• Slice through infarction ? less deformation modes
  (mainly translation) caused by infarction
• Great compression component to compensate for
  infarction.

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Eigenmodes healthy
Radial compression or compression with long axis
extension
Translation
Rotation or torsion
Unknown
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Eigenmodes infarction
Translation
Deviated radial displacement
Unknown
Unknown
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Eigenmodes above infarction
Normal translation
Strong compression to compensate for infarction
Unknown
Unknown
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Use as filter method
Eigenmodes 1-4

• Good approximation with only four eigenmodes (?
  95).

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Use as filter method
Eigenmode 1

• The end of ventricular systole is almost
  completely described by the first eigenmode.

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Use as filter method
Eigenmodes 2-4

• Filtering out of the compression (described by
  the first eigenmode) works fine.

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Future research

• Better statistics ? increment of mice
• Use PCA with foreknowledge
• Analysis of spatial derivatives ?
  (circumferential) strain
• 3D tagging MRI/long axis slices
• Link with DTI (fibre tracking).

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Future

• Better indication of heart failure during a
  hospital consult after heart dysfunction.