Automatic Laplacian-based shape optimization for patient-specific vascular grafts

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). Subsequent..., , , # 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.

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