A hybrid approach to full-scale reconstruction of renal arterial network

Research output: Contribution to journal › Journal article › Research › peer-review

Standard

A hybrid approach to full-scale reconstruction of renal arterial network. / Xu, Peidi; Holstein-Rathlou, Niels-Henrik; Søgaard, Stinne Byrholdt; Gundlach, Carsten; Sørensen, Charlotte Mehlin; Erleben, Kenny; Sosnovtseva, Olga; Darkner, Sune.

In: Scientific Reports, Vol. 13, No. 1, 7569, 09.05.2023.

Research output: Contribution to journal › Journal article › Research › peer-review

Harvard

Xu, P, Holstein-Rathlou, N-H, Søgaard, SB, Gundlach, C, Sørensen, CM, Erleben, K, Sosnovtseva, O & Darkner, S 2023, 'A hybrid approach to full-scale reconstruction of renal arterial network', Scientific Reports, vol. 13, no. 1, 7569. https://doi.org/10.1038/s41598-023-34739-y

APA

Xu, P., Holstein-Rathlou, N-H., Søgaard, S. B., Gundlach, C., Sørensen, C. M., Erleben, K., Sosnovtseva, O., & Darkner, S. (2023). A hybrid approach to full-scale reconstruction of renal arterial network. Scientific Reports, 13(1), [7569]. https://doi.org/10.1038/s41598-023-34739-y

Vancouver

Xu P, Holstein-Rathlou N-H, Søgaard SB, Gundlach C, Sørensen CM, Erleben K et al. A hybrid approach to full-scale reconstruction of renal arterial network. Scientific Reports. 2023 May 9;13(1). 7569. https://doi.org/10.1038/s41598-023-34739-y

Author

Xu, Peidi ; Holstein-Rathlou, Niels-Henrik ; Søgaard, Stinne Byrholdt ; Gundlach, Carsten ; Sørensen, Charlotte Mehlin ; Erleben, Kenny ; Sosnovtseva, Olga ; Darkner, Sune. / A hybrid approach to full-scale reconstruction of renal arterial network. In: Scientific Reports. 2023 ; Vol. 13, No. 1.

Bibtex

@article{8e1b99333d504795a40b1fbd4409521e,
title = "A hybrid approach to full-scale reconstruction of renal arterial network",
abstract = "The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a close agreement between the reconstructed vasculature and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.",
keywords = "Animals, Rats, Artificial Intelligence, Arteries, Kidney/diagnostic imaging, Acceptance and Commitment Therapy, X-Ray Microtomography",
author = "Peidi Xu and Niels-Henrik Holstein-Rathlou and S{\o}gaard, {Stinne Byrholdt} and Carsten Gundlach and S{\o}rensen, {Charlotte Mehlin} and Kenny Erleben and Olga Sosnovtseva and Sune Darkner",
note = "{\textcopyright} 2023. The Author(s).",
year = "2023",
month = may,
day = "9",
doi = "10.1038/s41598-023-34739-y",
language = "English",
volume = "13",
journal = "Scientific Reports",
issn = "2045-2322",
publisher = "nature publishing group",
number = "1",

}

RIS

TY - JOUR

T1 - A hybrid approach to full-scale reconstruction of renal arterial network

AU - Xu, Peidi

AU - Holstein-Rathlou, Niels-Henrik

AU - Søgaard, Stinne Byrholdt

AU - Gundlach, Carsten

AU - Sørensen, Charlotte Mehlin

AU - Erleben, Kenny

AU - Sosnovtseva, Olga

AU - Darkner, Sune

N1 - © 2023. The Author(s).

PY - 2023/5/9

Y1 - 2023/5/9

N2 - The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a close agreement between the reconstructed vasculature and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.

AB - The renal vasculature, acting as a resource distribution network, plays an important role in both the physiology and pathophysiology of the kidney. However, no imaging techniques allow an assessment of the structure and function of the renal vasculature due to limited spatial and temporal resolution. To develop realistic computer simulations of renal function, and to develop new image-based diagnostic methods based on artificial intelligence, it is necessary to have a realistic full-scale model of the renal vasculature. We propose a hybrid framework to build subject-specific models of the renal vascular network by using semi-automated segmentation of large arteries and estimation of cortex area from a micro-CT scan as a starting point, and by adopting the Global Constructive Optimization algorithm for generating smaller vessels. Our results show a close agreement between the reconstructed vasculature and existing anatomical data obtained from a rat kidney with respect to morphometric and hemodynamic parameters.

KW - Animals

KW - Rats

KW - Artificial Intelligence

KW - Arteries

KW - Kidney/diagnostic imaging

KW - Acceptance and Commitment Therapy

KW - X-Ray Microtomography

U2 - 10.1038/s41598-023-34739-y

DO - 10.1038/s41598-023-34739-y

M3 - Journal article

C2 - 37160979

VL - 13

JO - Scientific Reports

JF - Scientific Reports

SN - 2045-2322

IS - 1

M1 - 7569

ER -

ID: 346412570