Optimization of Case-Specific Vascular Tree Models Based on Vessel Size Imaging

Lloyd, Bryn; Hirsch, Sven; Székely, Gábor

doi:10.1007/978-3-642-11615-5_5

“…Following the experimental setup of Schneider et al [12,, two artificial arterial tree models are generated for different random seeds. For each of these models we derive a vessel size imaging (VSI) map that is in turn used to synthesize a functionally equivalent but structurally different arterial tree following [5]. The vessel radii range from (2-17) µm in all four cases.…”

Section: Experiments and Resultsmentioning

confidence: 99%

“…The slightly different and more fundamental problem of synthetic formation of functionally adequate vascular networks has attracted considerable attention in the field of computational physiology [5,12,13]. Here, physiological principles are applied to generate synthetic vascular models that satisfy prescribed physiological conditions.…”

Section: Introductionmentioning

confidence: 99%

TGIF: Topological Gap In-Fill for Vascular Networks

Schneider

¹

,

Hirsch

²

,

Weber

³

et al. 2014

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014

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This paper describes a new approach for the reconstruction of complete 3-D arterial trees from partially incomplete image data. We utilize a physiologically motivated simulation framework to iteratively generate artificial, yet physiologically meaningful, vasculatures for the correction of vascular connectivity. The generative approach is guided by a simplified angiogenesis model, while at the same time topological and morphological evidence extracted from the image data is considered to form functionally adequate tree models. We evaluate the effectiveness of our method on four synthetic datasets using different metrics to assess topological and functional differences. Our experiments show that the proposed generative approach is superior to state-of-the-art approaches that only consider topology for vessel reconstruction and performs consistently well across different problem sizes and topologies.

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“…A challenge in this context is to estimate the permeability tensors and porosities for each compartment and to derive suitable coupling conditions between the different compartments. Other types of models directly simulate vascular growth taking optimality principles like the minimisaton of building material or the minimisation of energy dissipation into account 17‐19 . Instead of simulating the growth of the vessels, a direct optimisation of the microvascular system can be considered.…”

Section: Introductionmentioning

confidence: 99%

A 3D‐1D coupled blood flow and oxygen transport model to generate microvascular networks

Köppl¹,

Vidotto²,

Wohlmuth

³

2020

Numer Methods Biomed Eng

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In this work, we introduce an algorithmic approach to generate microvascular networks starting from larger vessels that can be reconstructed without noticeable segmentation errors. Contrary to larger vessels, the reconstruction of fine-scale components of microvascular networks shows significant segmentation errors, and an accurate mapping is time and cost intense. Thus there is a need for fast and reliable reconstruction algorithms yielding surrogate networks having similar stochastic properties as the original ones. The microvascular networks are constructed in a marching way by adding vessels to the outlets of the vascular tree from the previous step. To optimise the structure of the vascular trees, we use Murray's law to determine the radii of the vessels and bifurcation angles. In each step, we compute the local gradient of the partial pressure of oxygen and adapt the orientation of the new vessels to this gradient. At the same time, we use the partial pressure of oxygen to check whether the considered tissue block is supplied sufficiently with oxygen. Computing the partial pressure of oxygen, we use a 3D-1D coupled model for blood flow and oxygen transport. To decrease the complexity of a fully coupled 3D model, we reduce the blood vessel network to a 1D graph structure and use a bi-directional coupling with the tissue which is described by a 3D homogeneous porous medium. The resulting surrogate networks are analysed with respect to morphological and physiological aspects. K E Y W O R D S 3D-1D coupled flow models, blood flow simulations, dimensionally reduced models, flows in porous media, oxygen transport, vascular growth

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“…Motivated by such problems, different approaches for the generation of surrogate microvascular networks have been developed [6]. Typically, they are based on optimality principles like the minimisaton of building material or the minimisation of energy dissipation [23,27,35]. In this work, we adapt methods developed in [42,43] to generate artificial arterial trees.…”

Section: Introductionmentioning

confidence: 99%

A 3D-1D coupled blood flow and oxygen transport model to generate microvascular networks

Köppl¹,

Vidotto²,

Wohlmuth³

2020

Preprint

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In this work, we introduce an algorithmic approach to generate microvascular networks starting from larger vessels that can be reconstructed without noticeable segmentation errors. Contrary to larger vessels, the reconstruction of fine-scale components of microvascular networks shows significant segmentation errors, and an accurate mapping is time and cost intense. Thus there is a need for fast and reliable reconstruction algorithms yielding surrogate networks having similar stochastic properties as the original ones. The microvascular networks are constructed in a marching way by adding vessels to the outlets of the vascular tree from the previous step. To optimise the structure of the vascular trees, we use Murray's law to determine the radii of the vessels and bifurcation angles. In each step, we compute the local gradient of the partial pressure of oxygen and adapt the orientation of the new vessels to this gradient. At the same time, we use the partial pressure of oxygen to check whether the considered tissue block is supplied sufficiently with oxygen. Computing the partial pressure of oxygen, we use a 3D-1D coupled model for blood flow and oxygen transport. To decrease the complexity of a fully coupled 3D model, we reduce the blood vessel network to a 1D graph structure and use a bi-directional coupling with the tissue which is described by a 3D homogeneous porous medium. The resulting surrogate networks are analysed with respect to morphological and physiological aspects.

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Optimization of Case-Specific Vascular Tree Models Based on Vessel Size Imaging

Cited by 3 publications

References 16 publications

TGIF: Topological Gap In-Fill for Vascular Networks

TGIF: Topological Gap In-Fill for Vascular Networks

A 3D‐1D coupled blood flow and oxygen transport model to generate microvascular networks

A 3D-1D coupled blood flow and oxygen transport model to generate microvascular networks

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