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Dynamic MRI may capture temporal anatomical changes in soft tissue organswith high contrast but the obtained sequences usually suffer from limitedvolume coverage which makes the high resolution reconstruction of organ shapetrajectories a major challenge in temporal studies. Because of the variabilityof abdominal organ shapes across time and subjects, the objective of this studyis to go towards 3D dense velocity measurements to fully cover the entiresurface and to extract meaningful features characterizing the observed organdeformations and enabling clinical action or decision. We present a pipelinefor characterization of bladder surface dynamics during deep respiratorymovements. For a compact shape representation, the reconstructed temporalvolumes were first used to establish subject-specific dynamical 4D meshsequences using the LDDMM framework. Then, we performed a statisticalcharacterization of organ dynamics from mechanical parameters such as meshelongations and distortions. Since we refer to organs as non flat surfaces, wehave also used the mean curvature changes as metric to quantify surfaceevolution. However, the numerical computation of curvature is stronglydependant on the surface parameterization. To cope with this dependency, weemployed a new method for surface deformation analysis. Independent ofparameterization and minimizing the length of the geodesic curves, it stretchessmoothly the surface curves towards a sphere by minimizing a Dirichlet energy.An Eulerian PDE approach is used to derive a shape descriptor from thecurve-shortening flow. Intercorrelations between individual motion patterns arecomputed using the Laplace Beltrami operator eigenfunctions for sphericalmapping. Application to extracting characterization correlation curves forlocally controlled simulated shape trajectories demonstrates the stability ofthe proposed shape descriptor.

Characterization of surface motion patterns in highly deformable soft tissue organs from dynamic MRI: An application to assess 4D bladder motion