In silico simulation of hepatic arteries: An open-source algorithm for efficient synthetic data generation.
data synthesis
hepatic arterial trees
interventional imaging simulation
Journal
Medical physics
ISSN: 2473-4209
Titre abrégé: Med Phys
Pays: United States
ID NLM: 0425746
Informations de publication
Date de publication:
Sep 2023
Sep 2023
Historique:
revised:
28
02
2023
received:
22
11
2022
accepted:
13
03
2023
pmc-release:
01
09
2024
medline:
11
9
2023
pubmed:
24
3
2023
entrez:
23
3
2023
Statut:
ppublish
Résumé
In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high-resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random. The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation. The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization-based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non-intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented. The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high-resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law This method facilitates the generation of large datasets of high-resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.
Sections du résumé
BACKGROUND
BACKGROUND
In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high-resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random.
PURPOSE
OBJECTIVE
The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation.
METHODS
METHODS
The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization-based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non-intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented.
RESULTS
RESULTS
The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high-resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law
CONCLUSIONS
CONCLUSIONS
This method facilitates the generation of large datasets of high-resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging.
Identifiants
pubmed: 36950870
doi: 10.1002/mp.16379
pmc: PMC10517083
mid: NIHMS1910119
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
5505-5517Subventions
Organisme : NCI NIH HHS
ID : F30CA250408
Pays : United States
Organisme : NIBIB NIH HHS
ID : R21 EB024553
Pays : United States
Organisme : NCI NIH HHS
ID : T32 CA009206
Pays : United States
Organisme : NCI NIH HHS
ID : F30 CA250408
Pays : United States
Organisme : NCI NIH HHS
ID : T32CA009206
Pays : United States
Organisme : NIGMS NIH HHS
ID : T32 GM140935
Pays : United States
Organisme : NCI NIH HHS
ID : T32CA009206
Pays : United States
Organisme : NCI NIH HHS
ID : F30CA250408
Pays : United States
Informations de copyright
© 2023 American Association of Physicists in Medicine.
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