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

• Published in: Computer methods and programs in biomedicine Vol. 218; p. 106708
• Main Authors: Makki, Karim, Bohi, Amine, Ogier, Augustin C., Bellemare, Marc Emmanuel
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.05.2022; Elsevier
• Subjects: Computer Science; differential geometry; Discrete Mathematics; dynamic MRI; geodesic distances; Humans; Imaging, Three-Dimensional - methods; Magnetic Resonance Imaging - methods; Medical Imaging; Motion; motion estimation; Pelvic floor; Respiration; statistical shape analysis; Urinary Bladder - diagnostic imaging; motion estimation; differential geometry; Pelvic floor; geodesic distances; statistical shape analysis; dynamic MRI
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2022.106708

•Non-invasive characterization of bladder dynamics during deep respiratory movements from dynamic Magnetic Resonance Imaging.•Compact representation of organ shape dynamics from reconstructed or- gan trajectories with a full volume coverage.•Detecting salient and non salient organ motion patterns through the de- velopment of geometric shape descriptors. Background and objectives: Dynamic Magnetic Resonance Imaging (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 the present 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. Methods: 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 large deformation diffeomorphic metric mapping (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 change as metric to quantify surface evolution. However, the numerical computation of curvature is strongly dependant on the surface parameterization (i.e. the mesh resolution). To cope with this dependency, we employed a non-parametric 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 individuals’ motion patterns are computed using the Laplace-Beltrami Operator (LBO) eigenfunctions for spherical mapping. Results: Application to extracting characterization correlation curves for locally-controlled simulated shape trajectories demonstrates the stability of the proposed shape descriptor. Its usability was shown on MRI acquired for seven healthy participants for which the bladder was highly deformed by maximum of inspiration. As expected, the study showed that deformations occured essentially on the top lateral regions. Conclusion:Promising results were obtained, showing the organ in its 3D complexity during deformation due to strain conditions. Smooth genus-0 manifold reconstruction from sparse dynamic MRI data is employed to perform a statistical shape analysis for the determination of bladder deformation.