Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets

Do-Eun Lee; Seungyeon Kim; Sung-Ho Ahn; Tae-Rin Lee; Wheesung Lee

REVIEW 5 major objections 5 minor 18 references

Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets

T0 review · 5 major / 5 minor · reviewed 2026-08-12 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read A graph neural network predicts pressure and blood flow in cerebral vessel networks that were held out of training, using only the vessel geometry from MRA scans.

desk verdict A solid proof-of-concept for a GNN surrogate of a simple Poiseuille cerebral flow model, with clinical claims that outrun the validation. read the letter →

arxiv 2411.17971 v1 pith:HS4MNAVM submitted 2024-11-27 eess.IV cs.AIcs.CEcs.LG

Seungyeon Kim , Wheesung Lee , Sung-Ho Ahn , Do-Eun Lee , Tae-Rin Lee This is my paper · ORCID

classification eess.IVcs.AIcs.CEcs.LG

keywords cerebralbloodflowgraphneuralnetworkvascularpredictionmagneticresonanceangiographygROMPoiseuillestenosisreduced-ordermodel

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that a graph neural network can act as a fast surrogate for blood-flow simulation in the human brain: given a patient's cerebral artery network, it outputs flow rate and pressure at every node without solving the fluid equations. The ground-truth labels come from a Poiseuille-flow model with conservation of flow at bifurcations, applied to 35 patients' MRA-derived vessel graphs and augmented 25-fold to 875 datasets with varied inlet pressures and vessel radii. By training on 28 vessel networks and testing on 7 completely different ones in each cross-validation fold, the paper claims the model generalizes to previously unseen vascular structures, reporting Pearson correlations of 0.727 for pressure and 0.824 for flow rate and accuracies of about 84.8% and 86.6% within a 10% error threshold. A sympathetic reader would take this as evidence that neural surrogates can approximate hemodynamic physics on pathological cerebral geometries fast enough for real-time diagnostics, with the caveat that the reference physics is itself a simplified model.

What carries the argument

The machinery has three parts. First, each MRA scan is turned into a graph whose nodes carry position and radius and whose edges carry length and axis information, after voxel segmentation and clustering-based noise removal. Second, the reference labels are produced by the Poiseuille law $Q = \pi d^4 \Delta P / (128 \mu L)$ with a node diameter averaged from the two endpoints, combined with the conservation law $\sum_i Q_i = 0$ at bifurcations; this linear system yields pressure and flow for every vessel. Third, the GNN is gROM, a MeshGraphNets-style encoder–processor–decoder in which nodes exchange messages along edges, trained with mean absolute error and Adam over 500 epochs. The augmentation step randomizes inlet pressure between 12,000 and 18,000 Pa and vessel radii by factors of 0.8–1.2, so the model sees a range of inflow conditions and pathological geometries; this combination of physics-generated labels, graph-structured message passing, and augmentation is what carries the generalization claim.

What would settle it

Run 3D computational fluid dynamics or phase-contrast MRI on one or more of the 35 patients' stenotic middle-cerebral-artery networks and compare measured pressure and flow to the GNN's predictions; if the GNN matches the simplified Poiseuille labels but not the measurements, then the claim that it predicts cerebral blood flow in a clinically meaningful sense fails, since its training labels would not be physiologically faithful.

Watch

Extended reading notes

Core claim

The central claim is that the gROM architecture, originally developed for cardiovascular and pulmonary vessel simulations, transfers to cerebral arteries with stenosis and predicts blood flow and pressure on network structures never seen during training. The paper's evidence is a 5-fold cross-validation in which each fold trains on 28 distinct patient-derived vessel graphs and tests on 7 left-out graphs, each expanded to 25 datasets; the selected model achieves a Pearson correlation of 0.727 for pressure and 0.824 for flow rate, with 84.8097% of pressure nodes and 86.6405% of flow nodes falling within 10% of the physics-model reference. The authors also state that accuracy drops on complex stenotic topologies, which they attribute to the difficulty of modeling pathological vasculature. The discovery, if it holds, is that message passing on vessel graphs plus data augmentation is enough to emulate the reference hemodynamic solver across patient-specific geometries.

Load-bearing premise

The load-bearing premise is that the Poiseuille flow model with averaged node diameters and flow conservation at bifurcations produces accurate ground-truth pressure and flow values for stenotic cerebral arteries, because the GNN is trained and evaluated only against this model, never against measured blood flow or high-fidelity 3D CFD.

Editorial extensions

If this is right

If the reported correlations hold, clinicians could obtain per-vessel pressure and flow estimates from a single MRA-derived vessel graph in real time, bypassing expensive 3D computational-fluid-dynamics runs.
The held-out-network evaluation implies that the model's knowledge transfers across patients, not just across input conditions for a single patient's anatomy.
Because the labels come from the Poiseuille plus conservation model, the model's accuracy ceiling is set by that simplified physics; the reported numbers measure agreement with the model, not with measured hemodynamics.
The method's partial success on stenosis-containing MCA networks suggests the surrogate can absorb some pathological geometry, but the paper's own discussion notes reduced accuracy on complex topologies, indicating that further data and augmentation are needed before clinical use.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A testable extension not pursued in the paper is to compare the GNN's predictions on the same patients against 3D CFD or phase-contrast MRI flow measurements; agreement there would validate the surrogate against physiology rather than against its own training labels.
Because the message-passing design is not tied to a specific vessel type, the same training recipe could plausibly be extended to other cerebrovascular pathologies such as aneurysms or arteriovenous malformations, and to pulsatile flow by adding a time dimension to node or edge features.
The accuracy metric normalizes absolute error by the maximum true value in the network, so errors at low-flow branches are de-emphasized; a per-branch or per-vessel-segment error report would give a stricter picture of clinical usefulness.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

Two things you should know. The paper is a genuine proof-of-concept: it takes Pegolotti et al.'s gROM, applies it to cerebral artery networks extracted from 35 MRA-TOF patients, augments vessel radius and inlet pressure, and shows the network can approximate a Poiseuille-based flow solver on held-out patient geometries (Pearson 0.727 pressure, 0.824 flow). The Discussion honestly concedes that accuracy drops on unseen topologies and that stenotic cases are hardest. That honesty is real and worth crediting.

The soft spot is the one the stress-test flags, and it lands. Every training and test label is generated by Eq. (1) with a single averaged diameter per segment, d=(di+dj)/2. Since resistance scales as d^-4, that averaging is precisely where focal stenoses hurt, and the cohort is stenosis patients. So the reported correlations are internal agreement with the authors' own surrogate, not evidence about real hemodynamics. The paper never validates against measured flow, high-fidelity CFD, or even a more careful 1D model. That does not kill the paper, but it should kill the abstract's phrasing about 'real-time cerebrovascular diagnostics.'

Some secondary issues: no baselines are presented despite the claim of superiority over existing models; the five-fold evaluation picks the best epoch per fold, which can inflate numbers; there are no error bars; and the outlet boundary conditions are not specified, so the label generation isn't fully reproducible. These are all fixable or at least reportable.

On balance the central mechanics hold: the GNN clearly learns the surrogate. The paper is a legitimate domain adaptation of gROM to cerebral vasculature, and it is transparent about limitations. Who gets value: researchers working on GNN surrogates for vascular flows, and groups wanting a quick surrogate for a known simplified model. It is not yet a clinical tool.

I would send it to peer review for a conference-level or applied venue, with the expectation that the authors re-scope the claims, add a baseline or two, and either release code or fully specify the label-generation pipeline. A serious referee can help them sharpen it into a useful contribution.

Referee Report

5 major / 5 minor

Summary. The paper proposes a graph neural network (gROM, based on MeshGraphNets and prior work by Pegolotti et al.) to predict blood flow rate and pressure in cerebral vascular networks reconstructed from MRA images of 35 patients, mostly with stenosis. Training and test labels are generated by a Poiseuille flow model (Eq. 1) with node-averaged diameters and flow conservation at bifurcations (Eq. 2). The dataset is augmented to 875 graphs by varying inlet pressure and vessel radius. A 5-fold patient-level cross-validation is performed, and the paper reports accuracy (84.81 for pressure, 86.64 for flow rate) and Pearson correlations (0.727 and 0.824), concluding that the GNN can generalize to unseen vascular networks and is suitable for real-time cerebrovascular diagnostics.

Significance. If the GNN were validated against independent hemodynamic data, it could be a useful fast surrogate for blood flow simulation in clinical settings. The paper uses a clinical dataset and patient-level cross-validation, and the architecture follows a published reduced-order model (gROM), which is a reasonable starting point. However, the current evaluation only demonstrates that the GNN can approximate the authors' own simplified Poiseuille model, not that it predicts real cerebral blood flow. The claim of superiority over existing models is not supported by any direct comparison. As a surrogate-model study, the contribution is modest but non-trivial; as a clinical prediction study, the central claim is not substantiated.

major comments (5)

[§2.3, Eqs. (1)–(2)] The training and test labels are generated by the authors' own Poiseuille model with node-averaged diameter d=(di+dj)/2. Because resistance scales as d^-4, this averaging is particularly fragile in focal stenoses, where the internal lumen profile is not represented and the pressure drop across the lesion may be substantially misestimated. The paper provides no validation of this reference model against measured flow, 3D CFD, or any independent data. Consequently, the reported Pearson correlations in Table 1 measure agreement with a self-generated surrogate, not clinical predictive validity. This is a load-bearing limitation, and the paper must either add external validation or be reframed as a study of approximation of the Poiseuille model.
[§2.3 and §3.1] The boundary conditions for the mathematical model are not fully specified. The paper mentions inlet node IDs, outlet node IDs, and a random inlet pressure range (12,000–18,000 Pa), but it does not state the outlet pressure or flow condition used to solve Eqs. (1)–(2). Without this information, the ground-truth labels are not reproducible, and the physiological plausibility of the model cannot be assessed.
[§3.1, Evaluation Methods] The model selection procedure is not robust: for each fold, the version with 'the best performance, as measured every 100 epochs' is chosen, which can inflate performance estimates due to overfitting to the validation set. No error bars, standard deviations, or per-fold results are reported for the accuracy or Pearson coefficients. Additionally, the accuracy metric in Eq. (3) normalizes the error by max(v_true) over the entire network, so nodes with small absolute values are counted as accurate even if their relative error is large; this is not a clinically meaningful tolerance. Please report fold-wise statistics and use a per-node relative error or a clinically relevant absolute threshold.
[§4, Discussion] The Discussion states that the model 'exhibited reduced accuracy when applied to previously unseen network configurations' and 'particularly struggled with complex topologies due to stenosis in all datasets.' This directly contradicts the abstract's claim that the GNN is effective at 'handling intricate and pathological vascular networks.' The central claim should be revised to reflect the actual findings, or the results should be stratified by topology and stenosis severity to identify where the model does and does not generalize.
[§3.1, Comparison to prior work] The sentence claiming 'superior performance compared to existing cerebral blood flow prediction models [12]' is not supported by any experimental comparison in the paper. Reference [12] is a coronary artery hemodynamics model, not a cerebral blood flow model, and no baseline (such as the original gROM of Pegolotti et al. [5] or a traditional solver) is implemented on the same data. A direct quantitative comparison with at least one existing method is necessary.

minor comments (5)

[§3.1, Eq. (3)] The formula for R is malformed: 'R = 1 n X H(...)' does not display the summation and normalization correctly; it should be presented as R = (1/n) sum_{i=1}^n H(|v_pred - v_true| / max(v_true)) × 100%.
[Fig. 1] The caption contains apparent typos: 'fpgROM' and 'neCerebral Artery Networks' should likely be 'gROM' and 'Cerebral Artery Networks'.
[§2.2] The sentence 'the radius of each node is calculated' does not specify whether the radius is derived from the local vessel cross-section, from a centerline distance, or from some other measurement; please provide the specific definition.
[§3.1, Dataset Acquisition] The imaging parameters list an 'inter-slice spacing of 20 mm' alongside a slice thickness of 0.5 mm; this inconsistency should be clarified, as it strongly affects the reconstruction of vessel geometry.
[§2.4 / §3.1, Implementation Details] The GNN architecture hyperparameters are not reported: the number of message-passing steps (or layers), the hidden feature dimensions, the batch size, and the total number of trainable parameters are all needed to reproduce the experiments.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: the GNN is trained and tested against a stated Poiseuille surrogate, not against that surrogate by construction; the one self-citation is supplementary.

full rationale

The paper's derivation chain is: segment MRA vessels into graphs, assign node radii and edge lengths, solve the Poiseuille-plus-conservation system of Eqs. (1)-(2) for ground-truth pressures and flow rates, then train a gROM/GNN to predict those quantities and evaluate on held-out networks with 5-fold cross-validation. The GNN is a learned approximation to this solver, not an identity: it can fail, and the Discussion explicitly concedes reduced accuracy on unseen complex topologies due to stenosis. Thus the reported Pearson correlations (0.727 pressure, 0.824 flow) are internal agreement with the authors' own mathematical model, which is a limitation of external validity (no comparison to measured hemodynamics or 3D CFD), not a circularity in the logical sense. The only self-citation is ref. [9] for the mathematical model, but Eqs. (1)-(2) are restated in the paper, so the argument does not reduce to the citation; the model is an input assumption, not a renamed prediction. No fitted parameter is relabeled as a prediction, no uniqueness theorem is imported from the authors' prior work, and no ansatz is smuggled in via citation. The paper's central claim is conditional on the surrogate's realism, which is a correctness risk, not a circularity.

Assumptions & free parameters 3 free parameters · 4 assumptions · 0 invented entities

The central claim rests on the validity of the Poiseuille-based surrogate model, which is taken from the authors' prior work [9] without external benchmarking. The augmentation ranges are hand-picked, and the metric threshold is arbitrary. No new physical entities are introduced.

free parameters (3)

inlet pressure augmentation range = 12,000 to 18,000 Pa
Chosen by hand to cover a wide range of inflow conditions (Sec. 3.1); affects the distribution of training labels.
vessel radius scaling range = 0.8 to 1.2 times original
Chosen by hand to reflect pathological stenosis ranges (Sec. 3.1); directly changes graph edge features and the resulting Poiseuille pressures and flows.
accuracy threshold = 10% (H(x) threshold)
The definition of accuracy in Eqs. (3)-(4) uses a 10% normalized error threshold, a choice that determines the reported accuracy values.

assumptions (4)

domain assumption Poiseuille flow with averaged diameters is a valid model for stenotic cerebral arteries
Invoked in Eq. (1), Section 2.3, as the basis for all training labels; no validation against real flow or 3D CFD.
standard math Conservation of flow at bifurcations (sum Q_i = 0)
Eq. (2) in Section 2.3 is the standard Kirchhoff-style conservation law used to close the linear system; it is physically standard but simplified.
domain assumption MRA segmentation and graph extraction faithfully represent vessel geometry and radius
Section 2.1-2.2 assumes the thresholded and DBSCAN-filtered voxel skeleton, with node radii, is an accurate representation of the true vascular network.
ad hoc to paper Twenty-five augmented versions per patient are representative of clinical variability
Section 3.1 creates 25 datasets per patient by random perturbation; the choice of 25 and the uniform distributions are not justified by data.

how reviews work

0 comments

Cite this review

Pith. "Pith review of Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets." pith.science (2026). https://pith.science/paper/HS4MNAVM

@misc{pith2026241117971,
  author       = {Pith},
  title        = {Pith review of: Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/HS4MNAVM}},
  note         = {Machine review of arXiv:2411.17971}
}

read the original abstract

Accurate prediction of cerebral blood flow is essential for the diagnosis and treatment of cerebrovascular diseases. Traditional computational methods, however, often incur significant computational costs, limiting their practicality in real-time clinical applications. This paper proposes a graph neural network (GNN) to predict blood flow and pressure in previously unseen cerebral vascular network structures that were not included in training data. The GNN was developed using clinical datasets from patients with stenosis, featuring complex and abnormal vascular geometries. Additionally, the GNN model was trained on data incorporating a wide range of inflow conditions, vessel topologies, and network connectivities to enhance its generalization capability. The approach achieved Pearson's correlation coefficients of 0.727 for pressure and 0.824 for flow rate, with sufficient training data. These findings demonstrate the potential of the GNN for real-time cerebrovascular diagnostics, particularly in handling intricate and pathological vascular networks.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

18 extracted references · 17 canonical work pages

[12]

scikit-image: image processing in python,

Stefan Van der Walt, Johannes L Sch ¨onberger, Juan Nunez-Iglesias, Franc ¸ois Boulogne, Joshua D Warner, Neil Yager, Emmanuelle Gouillart, and Tony Yu, “scikit-image: image processing in python,” PeerJ, vol. 2, pp. e453, 2014

work page 2014
[5]

Informed consent was obtained from all individual participants included in the study

COMPLIANCE WITH ETHICAL STANDARDS This study was approved by the Institutional Review Board of Pusan National University Yangsan Hospital, with approval number 55-2024-044. Informed consent was obtained from all individual participants included in the study

work page 2024
[1]

For example, Moore et al

INTRODUCTION Various simulation models have been developed to predict cerebral blood flow (CBF) in the brain. For example, Moore et al. [1] developed a 1D flow model for the circle of Willis (CoW) and compared the simulation results with 3D com- putational fluid dynamics simulation. Alastruey et al. [2] assessed the effect of CoW geometry variations on ce...

work page 2009
[2]

Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets

METHOD 2.1. Vessel Segmentation and Refinement Segmentation of blood vessels is performed after importing magnetic resonance angiography (MRA) images in NIfTI for- mat. The images are preprocessed by smoothing with Gaus- sian filters, followed by hysteresis thresholding to remove noise and detect vessel voxels [6]. The coordinates of points above the thre...

work page Pith review arXiv 2024
[3]

Experimental Setup Dataset Acquisition In this study, we utilized MRA datasets, comprising a total of 35 cases

EXPERIMENTS 3.1. Experimental Setup Dataset Acquisition In this study, we utilized MRA datasets, comprising a total of 35 cases. The dataset includes 25 pa- tients diagnosed with cerebral infarction, 3 with transient is- chemic attacks, 2 with basilar artery stenosis, and 5 with other cerebrovascular conditions, such as paresthesia, vascular de- mentia, a...

work page
[4]

Comparison of accuracy and Pearson’s correlation coefficients for pressure and flow rate predictions using the GNN

DISCUSSION This paper applied a gROM to predict blood flow and pres- sure in human cerebral vasculature, utilizing MRA datasets Table 1. Comparison of accuracy and Pearson’s correlation coefficients for pressure and flow rate predictions using the GNN. Pressure Flow rate Accuracy 84.8097 86.6405 Pearson’s correlation coefficients 0.727 0.824 from 35 indiv...

work page
[6]

ACKNOWLEDGMENTS This work was supported by the Technology development Program(RS-2023-00303878) funded by the Ministry of SMEs and Startups(MSS, Korea)

work page 2023
[7]

One-dimensional and three-dimensional mod- els of cerebrovascular flow,

SM Moore, KT Moorhead, JG Chase, T David, and J Fink, “One-dimensional and three-dimensional mod- els of cerebrovascular flow,” 2005

work page 2005

Show all 18 references

[8]

Modelling the circle of willis to assess the effects of anatomical variations and occlusions on cerebral flows,

JPKH Alastruey, KH Parker, J Peir ´o, SM Byrd, and SJ Sherwin, “Modelling the circle of willis to assess the effects of anatomical variations and occlusions on cerebral flows,” Journal of biomechanics, vol. 40, no. 8, pp. 1794–1805, 2007

2007
[9]

Sim- ulation of the human intracranial arterial tree,

Leopold Grinberg, Tomer Anor, Elizabeth Cheever, Joseph R Madsen, and George Em Karniadakis, “Sim- ulation of the human intracranial arterial tree,” Philo- sophical Transactions of the Royal Society A: Mathe- matical, Physical and Engineering Sciences , vol. 367, no. 1896, pp....

2009
[10]

Multiscale modeling and simulation of brain blood flow,

Paris Perdikaris, Leopold Grinberg, and George Em Karniadakis, “Multiscale modeling and simulation of brain blood flow,” Physics of Fluids , vol. 28, no. 2, pp. 021304, 2016

2016
[11]

Learning reduced-order models for cardiovascular simulations with graph neural net- works,

Luca Pegolotti, Martin R Pfaller, Natalia L Rubio, Ke Ding, Rita Brugarolas Brufau, Eric Darve, and Alison L Marsden, “Learning reduced-order models for cardiovascular simulations with graph neural net- works,” Computers in Biology and Medicine , vol. 168, pp. 107676, 2024

2024
[13]

Scikit-learn: Machine learning in python,

Fabian Pedregosa, Ga ¨el Varoquaux, Alexandre Gram- fort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vin- cent Dubourg, et al., “Scikit-learn: Machine learning in python,” the Journal of machine Learning research, vol. 12,...

2011
[14]

Ex- ploring network structure, dynamics, and function using networkx,

Aric Hagberg, Pieter J Swart, and Daniel A Schult, “Ex- ploring network structure, dynamics, and function using networkx,” Tech. Rep., Los Alamos National Labora- tory (LANL), Los Alamos, NM (United States), 2008

2008
[15]

Quantifying cerebral blood flow in the whole brain in a diffusion model with multiple sources from cerebrovas- cular structures,

Hyeryoung Cho, Vickie B Shim, and Tae-Rin Lee, “Quantifying cerebral blood flow in the whole brain in a diffusion model with multiple sources from cerebrovas- cular structures,” Engineering Reports, p. e12499
[16]

Learning mesh- based simulation with graph networks,

Tobias Pfaff, Meire Fortunato, Alvaro Sanchez- Gonzalez, and Peter W Battaglia, “Learning mesh- based simulation with graph networks,” arXiv preprint arXiv:2010.03409, 2020

2010 arXiv
[17]

Computational fluid dynamic simulation of human carotid artery bifur- cation based on anatomy and volumetric blood flow rate measured with magnetic resonance imaging,

Hamidreza Gharahi, Byron A Zambrano, David C Zhu, J Kevin DeMarco, and Seungik Baek, “Computational fluid dynamic simulation of human carotid artery bifur- cation based on anatomy and volumetric blood flow rate measured with magnetic resonance imaging,” Interna- tional journal...

2016
[18]

V oxel2hemodynamics: An end-to-end deep learning method for predicting coronary artery hemodynamics,

Ziyu Ni, Linda Wei, Lijian Xu, Qing Xia, Hong- sheng Li, Shaoting Zhang, and Dimitris Metaxas, “V oxel2hemodynamics: An end-to-end deep learning method for predicting coronary artery hemodynamics,” in International Workshop on Statistical Atlases and Computational Models of th...

2023

Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.

[1] [12]

scikit-image: image processing in python,

Stefan Van der Walt, Johannes L Sch ¨onberger, Juan Nunez-Iglesias, Franc ¸ois Boulogne, Joshua D Warner, Neil Yager, Emmanuelle Gouillart, and Tony Yu, “scikit-image: image processing in python,” PeerJ, vol. 2, pp. e453, 2014

work page 2014

[2] [5]

Informed consent was obtained from all individual participants included in the study

COMPLIANCE WITH ETHICAL STANDARDS This study was approved by the Institutional Review Board of Pusan National University Yangsan Hospital, with approval number 55-2024-044. Informed consent was obtained from all individual participants included in the study

work page 2024

[3] [1]

For example, Moore et al

INTRODUCTION Various simulation models have been developed to predict cerebral blood flow (CBF) in the brain. For example, Moore et al. [1] developed a 1D flow model for the circle of Willis (CoW) and compared the simulation results with 3D com- putational fluid dynamics simulation. Alastruey et al. [2] assessed the effect of CoW geometry variations on ce...

work page 2009

[4] [2]

Graph Neural Network for Cerebral Blood Flow Prediction With Clinical Datasets

METHOD 2.1. Vessel Segmentation and Refinement Segmentation of blood vessels is performed after importing magnetic resonance angiography (MRA) images in NIfTI for- mat. The images are preprocessed by smoothing with Gaus- sian filters, followed by hysteresis thresholding to remove noise and detect vessel voxels [6]. The coordinates of points above the thre...

work page Pith review arXiv 2024

[5] [3]

Experimental Setup Dataset Acquisition In this study, we utilized MRA datasets, comprising a total of 35 cases

EXPERIMENTS 3.1. Experimental Setup Dataset Acquisition In this study, we utilized MRA datasets, comprising a total of 35 cases. The dataset includes 25 pa- tients diagnosed with cerebral infarction, 3 with transient is- chemic attacks, 2 with basilar artery stenosis, and 5 with other cerebrovascular conditions, such as paresthesia, vascular de- mentia, a...

work page

[6] [4]

Comparison of accuracy and Pearson’s correlation coefficients for pressure and flow rate predictions using the GNN

DISCUSSION This paper applied a gROM to predict blood flow and pres- sure in human cerebral vasculature, utilizing MRA datasets Table 1. Comparison of accuracy and Pearson’s correlation coefficients for pressure and flow rate predictions using the GNN. Pressure Flow rate Accuracy 84.8097 86.6405 Pearson’s correlation coefficients 0.727 0.824 from 35 indiv...

work page

[7] [6]

ACKNOWLEDGMENTS This work was supported by the Technology development Program(RS-2023-00303878) funded by the Ministry of SMEs and Startups(MSS, Korea)

work page 2023

[8] [7]

One-dimensional and three-dimensional mod- els of cerebrovascular flow,

SM Moore, KT Moorhead, JG Chase, T David, and J Fink, “One-dimensional and three-dimensional mod- els of cerebrovascular flow,” 2005

work page 2005