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

TL;DR

This study develops a pipeline using dynamic MRI and advanced shape analysis techniques to characterize surface motion patterns of the bladder, enabling detailed 4D deformation analysis for clinical insights.

Contribution

It introduces a novel surface deformation analysis method independent of parameterization, utilizing a Dirichlet energy minimization and Laplace Beltrami eigenfunctions for robust 4D organ motion characterization.

Findings

Effective 4D bladder surface motion characterization.

Robust shape descriptor stable under different parameterizations.

Potential for improved clinical assessment of organ dynamics.

Abstract

Dynamic MRI may capture temporal anatomical changes in soft tissue organs with high contrast but the obtained sequences usually suffer from limited volume coverage which makes the high resolution reconstruction of organ shape trajectories a major challenge in temporal studies. Because of the variability of abdominal organ shapes across time and subjects, the objective of this study is to go towards 3D dense velocity measurements to fully cover the entire surface and to extract meaningful features characterizing the observed organ deformations and enabling clinical action or decision. We present a pipeline for characterization of bladder surface dynamics during deep respiratory movements. For a compact shape representation, the reconstructed temporal volumes were first used to establish subject-specific dynamical 4D mesh sequences using the LDDMM framework. Then, we performed a statistical characterization of organ dynamics from mechanical parameters such as mesh elongations and distortions. Since we refer to organs as non flat surfaces, we have also used the mean curvature changes as metric to quantify surface evolution. However, the numerical computation of curvature is strongly dependant on the surface parameterization. To cope with this dependency, we employed a new method for surface deformation analysis. Independent of parameterization and minimizing the length of the geodesic curves, it stretches smoothly the surface curves towards a sphere by minimizing a Dirichlet energy. An Eulerian PDE approach is used to derive a shape descriptor from the curve-shortening flow. Intercorrelations between individual motion patterns are computed using the Laplace Beltrami operator eigenfunctions for spherical mapping. Application to extracting characterization correlation curves for locally controlled simulated shape trajectories demonstrates the stability of the proposed shape descriptor.