Direct medical image to simulation using auto-segmentation and point cloud-based CFD

Ashton M. Corpuz; Monu Jaiswal; Pan Du; Abhay B. Ramachandra; Jian-Xun Wang; Ming-Chen Hsu

doi:10.3934/acse.2025006

Article Contents

March 2025, Volume 3, 95-124. Doi: 10.3934/acse.2025006

This volume Previous Article Extended B-spline-based mixed material point method stabilized by the variational multiscale method for compressible and nearly incompressible hyperelastic materials Next Article Solving implicit inverse problems with homotopy-based regularization path

Direct medical image to simulation using auto-segmentation and point cloud-based CFD

1.
Department of Mechanical Engineering, Iowa State University, Ames, IA 50011, USA
2.
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA

^*Corresponding author: Ming-Chen Hsu
^*Corresponding author: Ming-Chen Hsu

Received: January 2025

Revised: March 2025

Early access: March 27, 2025

Published online: March 27, 2025

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Abstract

Cardiovascular disease (CVD) remains one of the leading causes of mortality worldwide. Computational medicine and digital twins hold promise in mitigating the impact and prevalence of CVD. Recent advances in image-based computational methods have enabled the quantification of functional and biologically important metrics that would otherwise be difficult to obtain from the standard of care. However, significant challenges remain due to the manual/semi-automated nature of the processes and the domain expertise required to perform them. This paper addresses these challenges by proposing a novel framework that builds on our recently developed direct point cloud-to-CFD approach using immersogeometric analysis. The proposed method leverages advanced auto-segmentation techniques to extract medically relevant geometries as point clouds, which are then directly used for CFD simulations. The framework is validated using benchmark flow problems with analytical and computational solutions and is subsequently applied to patient-specific images to demonstrate its capabilities. The results highlight the method's ability to facilitate rapid CFD simulations directly on point clouds derived from patient scans, underscoring its potential to accelerate the image-to-simulation pipeline and enable the tractability of cardiovascular digital twins.

Keywords:

Mathematics Subject Classification: Primary: 76M10, 68T07, 92C50; Secondary: 76D05, 65N30, 68U10.

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Figure 1. The pipeline of the proposed framework from initial CT scan to simulation results

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Figure 2. Diagram of the schematic representation of the proposed pipeline for aorta segmentation and 3D mesh reconstruction. The image segmentation architecture utilizes 3D U-Net for segmenting the aorta and its branches. The encoder component of 3D U-Net is responsible for extracting high-dimensional features from the small field of view. These features are then combined with the image gradient obtained from the MRI image within the shape stream

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Figure 3. Illustration of (A) parameters used to define the anisotropic mesh, and a comparison showcasing the difference between (B) the isotropic mesh and (C) the anisotropic mesh

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Figure 4. (A) An example of flow within an object, illustrated using a 2D cross-section of a 3D channel represented by a point cloud. The object with boundary $ \Gamma $ is immersed into the domain $ \Omega $. The immersed boundary $ \Gamma $ divides the domain $ \Omega $ into a physical part $ \Omega_{\text{phys}} $, where the flow occurs, and a fictitious part $ \Omega_{\text{fict}} $. (B) Background mesh discretizing the domain $ \Omega $. (C) The faces ($ \mathcal{F}^f $) where the ghost penalty is applied are highlighted in red. These faces are shared by two neighboring elements that lie fully or partially within the physical domain, with at least one of them intersected by the boundary

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Figure 5. Background mesh domain containing an aorta model represented by a point cloud. (A) A domain whose boundaries do not coincide with the flow inlet and outlet locations. (B) A clipped domain in which both the domain and the point cloud are trimmed to align with the desired inlet and outlet faces

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Figure 6. Dimensions of the simulated pipe using 500,000 points. The overall domain is a cuboid of size $ 0.02 \times 0.02 \times 0.25 $ m, while the diameter and length of the pipe are 0.01 m and 0.25 m, respectively

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Figure 7. Visualization of the pipe flow IM3 mesh with zoomed-in figures of the side and front of the mesh

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Figure 8. (A) Velocity magnitude and (B) pressure at the central cross-section along the streamwise direction of the pipe for $ Re = 300 $ obtained using IM3

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Figure 9. (A) Pressure drop along the centerline of the pipe in the streamwise direction and (B) streamwise velocity profile across the outlet of the pipe for all three background meshes for $ Re = 300 $

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Figure 10. Pressure drop along the centerline of the pipe in the streamwise direction with $ Re = 300 $ for (A) IM1 at four different levels of adaptive quadrature (AQ0 to AQ3) and (B) IM2 using boundary conditions pressure, resistance, and traction-free

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Figure 11. The left shows the point cloud, dimensions, flow properties, and boundary conditions for the bifurcation flow example at $ Re = 505 $. The right shows a mesh cut along the center plane of the bifurcation and a zoomed-in view of the bifurcation apex

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Figure 12. (A) Velocity magnitude and pressure contours along the center plane. (B) Pressure drop versus distance along the centerline of the pipe before the branches split, with the maximum distance being the bifurcation apex at 13.95 mm. The present work is compared with those from previous studies by Chung [21] and Weddell et al. [96]

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Figure 13. Resampled point clouds colored by normalized minimum and maximum depth values for enhanced visualization. From left to right: Case 1 – Adult patient with single ventricle disease who had undergone the Fontan procedure. Case 2 – Pediatric patient with a diseased ventricle and healthy aorta. Case 3 – Healthy lamb aorta

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Figure 14. From left to right: Voxel representation, NIMBUS point cloud representation, and both representations overlaid with semi-translucent voxels for Case 2

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Figure 15. (A) The domain of the CT scan accompanied by a slice across the midsection to reveal the aorta. (B) Point output from performing auto-segmentation on the CT scan

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Figure 16. Dimensions of the simulation domain boundary with the point cloud aorta immersed inside. The smaller boxes visualized are Boolean subtractions of the original rectangular domain using an estimated centerline normal to align the background mesh surface with the desired inlet/outlet surfaces. No-slip condition on the arterial wall is enforced weekly using the point cloud

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Figure 17. From left to right: Volume rendering of the blood flow speed. Velocity streamlines representation with point cloud overlaid and a zoomed-in portion of disturbed flow in the aorta

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Figure 18. (A) Volume rendering of pressure for the full aorta and zoomed-in sections of branching arteries. (B) Velocity magnitude visualization at user-selected slices throughout the simulation domain

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Figure 19. Illustration of feedback loops in a cardiovascular digital twin and the role of high-fidelity simulation

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Table 1. Anisotropic mesh refinement metrics for IM1, IM2, and IM3

Mesh	$h_{\mathrm{NOR}}$	$h_{\mathrm{TAN}}$	Growth rate	Boundary layer height ($H$)	Internal refinement	Total number of elements
IM1	0.02	0.4	1.1	0.5	0.2	378,867
IM2	0.01	0.2	1.1	0.5	0.1	1,074,581
IM3	0.005	0.1	1.1	0.5	0.05	4,180,767

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Table 2. Resampling, CT resolution, and percent deviation statistic

Aorta	Original points	Mean resolution (cm/voxel)	Resampled points	Percent deviation
Case 1	6,736	0.125	177,129	0.003952
Case 2	19,150	0.076	405,688	0.002712
Case 3	25,237	0.025	376,365	0.000797

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