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A Finite Element Model Generation and Simulation for Functional Analysis of 4D Cardiac Tagged-MR Images. Kyoungju Park. Goals. Functional analysis of cardiac images: Realistic Generic Real-time Clinically useful. Why functional analysis?. Important to: Normal physiology study
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A Finite Element Model Generation and Simulation for Functional Analysis of 4D Cardiac Tagged-MR Images Kyoungju Park
Goals Functional analysis of cardiac images: • Realistic • Generic • Real-time • Clinically useful
Why functional analysis? Important to: • Normal physiology study • Heart diseases and their causes • Alteration in heart shape and motion is a reasonable indicator of heart diseases
Difficult due to • Realistic • Non-homogeneous motion of the complete heart • Generic • Different hearts • Real-time • Pre-processing such as manual boundary segmentation ( about 200 images ) • Clinically useful • Large number of parameters • Correspondence between subjects
About cardiac imaging • Angiography • Echocardiography(2DE or 3DE) • CT • SPECT • PET • MRI
Cardiac MR Cardiac MR allows more accurate measurements and also provides an insight into myocardial functions • MR tagging techniques • Velocity encoding techniques
MR tagging The most promising non-invasive technology to evaluate regional myocardial contraction • SPAMM • CSPAMM • DANTE
Displacement measures • Modified Snakes Algorithm • interactive scheme for tag tracking • B-Spline Surfaces • HARP MRI • fast, fully automatic and dense material points • so far has been applied to 2-D images
Tag localization Phase 1 Phase 3 Phase 5 Phase 7 The top four figures are short-axis images, whereas the bottom four figures are long-axis images
Work to date on heart modeling • Surface models • Geometric models • Generalized ellipsoids, cylinders etc. • Spherical coordinates, planispheric coordinates • Statistical models • Volumetric models • Deformation models
Fundamental questions? • Imaging techniques? • SPAMM • Modeling techniques? • The FE model with underlying geometry and physical constraints • Estimation techniques? • Hierarchical estimation • Functional analysis? =>Shape/motion parameters and strain analysis =>Global/Regional/Local
System overview Three parts • Shape model generation • Motion estimation • Functional analysis
System overview Three parts • Shape model generation • Motion estimation • Functional analysis
Shape model generation “We use a generic heart to automatically build finite element meshes from tagged MR images” • A generic model • Represent the anatomical structure mathematically • Incorporate the prior knowledge • Include the LV and the RV up to its basal area
Anatomic orientation right left LV RV
Model coordinates We define a heart shape model with three surfaces
u Material coordinates Spherical coordinates latitude u, longitude v v
Model geometry The RV is composed of one tube and two ellipsoidal primitives.
Mapping parameters The rs and rt parameters represent the relative location of septum area w.r.t. the LV center
Axial scaling parameters Axial scaling parameters, r1, r2, r3 are defined along the x,y,z-axes respectively
Model dynamics Simplified Lagrangian dynamics equation of motion External forces, parameter forces, are computed from image-derived forces Regularizing term rigidity elasticity
xc p xa xb Edge forces Forces from edge data points • For each edge point, find the closest triangular face from a point z • Let p be the projected point of z onto the plane defined by the triangle • The force that z exerts is computed and linearly distributed to the nodes of triangle xaxbxc z
xa Image plane p xb xc z Edge forces Forces from model points • For each intersection point of image planes and triangle elements, find the closest edge from a point p • Let z be the closest edge point of p on the image plane • The force is computed and linearly distributed to the nodes of triangle xaxbxc
Hierarchical estimation • Hierarchical representation is introduced to describe deformation • The algorithm incrementally adapts the shape parameters • global axial scaling parameters • piecewise shape parameters along u • local shape parameters at each u,v
Overview Three • Model generation • Motion estimation • Functional analysis
FEM generation Automatic volumetric element generation endocardium RV LV
FEM generation Automatic volumetric element generation endocardium RV LV epicardium
FEM generation Automatic volumetric element generation endocardium RV LV epicardium
A single element v w v u u w Material Coordinates Physical Location
Deformation descriptors • Deformation • Twisting • Longitudinal function • Radial function l r t
FEM dynamics • External forces • Tagging forces • Edge forces • Internal strain energy • Transversely isotropic material (Stiffness) • Parametric constraints (Regularizing) Lagrangian dynamics equation of motion
External forces • Tag data are embedded in the FE model
Model tags Tag plane Model Tag: intersection of tag planes and elements
Model Tag Stripe: intersection of model tag and image plane Image plane Force from tags xb Image plane xa xi xp xp nt xc
Local position (e,n,s) Global position (x,y,z) Element coordinate systems n z e x s y
Internal strain energy • The linear elastic theory • Regularization in material property domain • Isotropic and homogeneous material property • Parametric constraints • Regularization in geometric domain • Bending and stretching constraints The element stiffness matrix
Reconstructed motion T=1 T=6
System overview Three parts • Shape model generation • Motion estimation • Functional analysis
Motion parameters:radial contraction RV LV Outer wall
Motion parameters:longitudinal displacements RV LV Outer wall
Motion parameters:twisting motion anterior LV free wall RV free wall posterior LV septum RV septum
Quantification of deformation • Strain / strain ratio analysis • Volume • Cardiac output • Ejection fraction • Stroke • LVM, RVM
Segmental analysis Divide into segments and perform analysis
Proposing work • Test and modify • Realistic outflow tract • Deformation analysis • Combine the framework • Boundary delineation at t=1 • FEM generation • Tag tracking and motion analysis • Experiment and evaluate
Contributions • Generic and comprehensive model • Less post-processing • Clinically useful information