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BM/IA 3D shape variability of the healthy and infarcted mouse heart Korbeeck, J.M. Eindhoven, July 1st 2004 BM/IA Contents • • • • Goals; Anatomy heart; Modes of LV deformation; Method: – Tagging MRI; – Statistical Shape Models. • Results; • Future research. BM/IA Introduction • Infarction major cause of death; • Temporal shape changes mechanical pumping efficiency warning system of heart failure? All Other Causes 41% Diseases of the Heart 29% Cancer 23% Stroke 7% [From “Stroke Facts 2004: All Americans”, American Heart Association, 2004] BM/IA 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. BM/IA Heart anatomy • Left ventricle (LV) is studied: – Volume corresponds with stroke volume pumping-efficiency; – Blood through entire circulation thickest wall. [From Marieb1997, page 661] BM/IA Layers of the heart wall [From Marieb1997, page 658] BM/IA Modes of LV deformation • Deformation: – Radial displacement; – Axial torsion; – Circumferential contraction with long axis extension. • Rotation; • Translation. [From Arts1992] BM/IA 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] BM/IA Mouse heart • Left ventricle: posterior – Large; – Thick wall. • Right ventricle: myocardium RV wall RV LV anterior – Smaller; – Thinner wall tagging MRI not yet possible. BM/IA Statistical Shape Models • Modelling shape and shape variation: – Without shape assumptions. • Shape represented by set of points in time; • Model the variation using PCA. BM/IA Principal Component Analysis 1 2 • Parameterised model: x f shape (b) x x Φb • Reduce dimensionality: 1 2 x x b1 1 – Eigenvectors of covariance; – Eigenvectors main directions; – Eigenvalue variance along eigenvector. BM/IA Algorithm • Represent points of 2D image as vector x: x ( x 1,x n , y 1,y n )T • Compute the mean and covariance: 1 1 x x S ( x x)( x x) s s 1 s s T i 1 i i 1 i i • Compute and of S, approximation of x: x x Φb b ΦT ( x x) • Choose t largest eigenvalues such that f V where fv defines the proportion of total variation t i 1 i v T BM/IA Example [From Cootes2004] BM/IA Cardiac Motion Model [From Suinesiaputra2002] BM/IA Results • “Normal” (i.e. healthy) heart; • Heart with infarction (LDA occluded by ligation): – Slice through infarction; – Slice above infarction. BM/IA 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. Percentage of total Healthy Infarction Above infarction 0.4 0.3 0.2 0.1 0 2 4 6 8 10 Eigenmode BM/IA Eigenmodes healthy Radial compression or compression with long axis extension Rotation or torsion Translation Unknown BM/IA Eigenmodes infarction Translation Deviated radial displacement Unknown Unknown BM/IA Eigenmodes above infarction Strong compression to compensate for infarction Normal translation Unknown Unknown BM/IA Use as filter method Eigenmodes 1-4 • Good approximation with only four eigenmodes ( 95%). BM/IA Use as filter method Eigenmode 1 • The end of ventricular systole is almost completely described by the first eigenmode. BM/IA Use as filter method Eigenmodes 2-4 • Filtering out of the compression (described by the first eigenmode) works fine. BM/IA 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). BM/IA Future • Better indication of heart failure during a hospital consult after heart dysfunction.