DOI: 10.5061/dryad.x0k6djhtg
title: Data from: Automatic Laplacian-based shape optimization for patient-specific vascular grafts

description: Cognitional heart disease is one of the leading case of mortality among
 newborns. Tissue-engineered vascular grafts presents the potential to help
 treat newborn with cognitional heart disease. This paper presents a
 computational framework for the automatic shape optimization of
 patient-specific tissue-engineered vascular grafts for repairing the
 aortic arch, aiming to improve the current treatment outcomes. The core
 innovation lies in an automatic shape optimization pipeline that combines
 Bayesian optimization techniques with the open-source finite volume
 solver, OpenFOAM, and a novel graft deformation algorithm. In this study
 we used imaging and flow data obtained from six patients diagnosed with
 cognitional heart disease. Our framework begins with Laplacian mode
 computation and the approximation of a computationally low-cost Gaussian
 process surrogate model to capture the minimum of weighted combination of
 inlet-outlet pressure drop (PD) and maximum wall shear stress (WSS).
 Subsequently, with a limited number of OpenFOAM simulations, the optimal
 shape is identified. The result showcases the potential of online training
 and hemodynamic surrogate model optimization for providing optimal graft
 shapes. The resulting graft designs for six patient-specific cases
 minimize PD and WSS, offering improved hemodynamic performance compared to
 initial models. These results show our framework successfully reduce
 inlet-outlet PD and maximum WSS as key factors influencing hemodynamic
 performance in comparison to baseline models. Furthermore, we compare how
 the performance of each design optimized under steady-state simulation
 compares to its performance when simulated under transient simulations.
 Our findings underscore that the automated designs achieve at least a 17%
 reduction in blood flow pressure drop in comparison to the baseline model,
 establishing their superiority over geometries optimized by human experts.
 Also, we evaluate to what degree optimal design shapes computed based on
 steady-state simulations remain performant under transient
 simulations.
description: # Data from: Automatic Laplacian-based shape optimization for
 patient-specific vascular grafts
 [https://doi.org/10.5061/dryad.x0k6djhtg](https://doi.org/10.5061/dryad.x0k6djhtg) This dataset comprises three-dimensional anatomical models of the aorta from six pediatric patients aged 9 months to 15 years, diagnosed with both Coarctation of the Aorta (CoA) and Transverse Aortic Hypoplasia (TAH). These models were generated using contrast-enhanced magnetic resonance angiography (MRA). The segmentation tool in Mimics software (Materialise, Leuven, Belgium) was employed to create the 3D anatomical models of the aortas. ## Description of the data and file structure * **Patient0_native_geometry.STL** * **Patient1_native_geometry.STL** * **Patient2_native_geometry.STL** * **Patient3_native_geometry.STL** * **Patient4_native_geometry.STL** * **Patient5_native_geometry.STL** Each file contains the three-dimensional model of the aorta for a specific patient in STL format. ## Sharing/Access information The data has been fully anonymized to protect patient privacy. Any personal identifiers have been removed, and the dataset only includes non-identifiable information related to the anatomical models.