Robust automated calcification meshing for personalized cardiovascular biomechanics

Abstract

Calcification has significant influence over cardiovascular diseases and interventions. Detailed characterization of calcification is thus desired for predictive modeling, but calcium deposits on cardiovascular structures are still often manually reconstructed for physics-driven simulations. This poses a major bottleneck for large-scale adoption of computational simulations for research or clinical use. To address this, we propose an end-to-end automated image-to-mesh algorithm that enables robust incorporation of patient-specific calcification onto a given cardiovascular tissue mesh. The algorithm provides a substantial speed-up from several hours of manual meshing to ~1 min of automated computation, and it solves an important problem that cannot be addressed with recent template-based meshing techniques. We validated our final calcified tissue meshes with extensive simulations, demonstrating our ability to accurately model patient-specific aortic stenosis and Transcatheter Aortic Valve Replacement. Our method may serve as an important tool for accelerating the development and usage of personalized cardiovascular biomechanics.

Fig. 1. An overview schematic of the C-MAC algorithm and its representative results.

a Starting from a 3D CTA, a patient-specific volumetric mesh of cardiovascular tissues is first generated using DeepCarve. The tissue mesh is used to aid the voxelgrid segmentation of calcification, as well as to generate the background mesh for DMTet. The SDF values are first initialized by sampling the voxelgrid segmentation at each node of the background grid, and subsequently the node coordinates and nodal SDF values are optimized for better element quality. The resulting DMTet output mesh is further processed via node-based remeshing to generate the final input for tetrahedralization. b C-MAC robustly and automatically incorporates a voxelgrid segmentation onto an existing mesh without changing the original mesh topology. Here, we demonstrate its performance using two vastly different calcification segmentations (orange) and complex aortic valve meshes (gray).

Fig. 2. Qualitative evaluations of the initial segmentation and post-processing algorithms.

a Assessing the segmentation accuracy in two image slice views from two test-set patients. GT ground-truth segmentation, DL deep learning segmentation using “GDL (ours)”, post: result of PostProcessCa2Seg using either GT or DL as the initial segmentation, final: result of the full C-MAC. Yellow: calcification, red: partial LV myocardium, blue: aorta, (green, orange, purple): aortic valve leaflets. b Before and after PostProcessCa2Seg on one test-set patient, where the white dashed circles highlight the regions with clear gap-closing effects on the voxelgrid segmentation. c Visualizing the effects of PostProcessCa2Seg on the final C-MAC mesh. Purple dots indicate the merged nodes between the calcification and the leaflet mesh, and the black dashed circles indicate the regions with large changes in the merged nodes. This illustrates both the benefit and drawback of our post-processing algorithm. Benefit: improved anatomical consistency with the surrounding tissue. Drawback: some overestimation of calcified regions.

Fig. 3. Qualitative evaluations of the meshing steps and baseline comparisons.

a Illustration of the background mesh generation process. From left to right: patient-specific mesh of the aorta + aortic valve leaflets, TetGen input generated by combining the exterior surface and an offset surface from the original geometry, preliminary background mesh generated by TetGen and hollowing, and final background mesh after adding a “fake” vertex to the exterior surface elements of the preliminary mesh. All meshes are clipped at a viewing plane for visualization purposes. b The three main sequential steps for anatomically consistent surface meshing. From left to right: initial inputs of patient-specific aorta + aortic valve leaflets (gray) and voxelgrid segmentation of calcification (red), initial DMTet mesh with raw sampled SDF, optimized DMTet mesh, and final remeshed surface. Green box indicates the viewing region, and colored circles indicate noticeable regions of improvement after each step. c Baseline comparisons for the final mesh quality. Yellow: calcification, green: aortic valve leaflet. Top: front view, bottom: back view. Colored circles indicate the noticeable regions of improvement from each baseline to C-MAC.

Fig. 4. Simulation outputs using the final reconstructed meshes.

a Valve opening simulations demonstrate the effects of calcification on the final leaflet positions. Yellow is the ground-truth calcification, left is the input valve geometry predicted by DeepCarve, and right is the deformed geometry after finite element analysis. Movement is clearly restricted near calcified regions. b Stress (top) and strain (bottom) analyses from valve opening simulations. Left is the Gaussian KDE plot of stress/strain vs. distance to calcification from the aggregate of 35 test-set patient simulations. Right is one test-set patient with stress/strain overlaid with the valve leaflets, plus the ground-truth calcification (gray) for reference. c TAVR stent deployment simulation results. Left: image and simulated geometry overlay. Right: maximum principal stress magnitudes plotted on the aortic valve leaflets for 10 different test-set patients.