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Record W4410478422 · doi:10.1016/j.cmpb.2025.108868

Validation of CTA-based closed-loop coronary artery flow simulations against intravascular Doppler velocity and pressure measurements

2025· article· en· W4410478422 on OpenAlexafffund
Anahita Abbasnejad Seresti, Alison L. Marsden, Andrew M. Kahn, Ryan Reeves, Ehtisham Mahmud, Belal Al Khiami, Lawrence Ang, Muhammad Owais Khan

Bibliographic record

VenueComputer Methods and Programs in Biomedicine · 2025
Typearticle
Languageen
FieldMedicine
TopicCoronary Interventions and Diagnostics
Canadian institutionsUniversity of TorontoToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsDoppler effectLoop (graph theory)CardiologyArteryMedicineFlow (mathematics)Blood flowInternal medicineRadiologyBiomedical engineeringPhysicsMechanicsMathematics

Abstract

fetched live from OpenAlex

BACKGROUND AND OBJECTIVES: The modeling assumptions involved in computational fluid dynamics (CFD) simulations of coronary arteries using coronary computed angiography (CTA) have not been thoroughly validated. These modeling assumptions can lead to uncertainties in simulated velocities and pressure, and consequently, other hemodynamic markers, such as wall shear stresses. In this study, we validated a state-of-the-art coronary CTA-CFD simulation strategy against intravascular Doppler velocity and pressure measurements. METHODS: 3D coronary models were reconstructed using coronary CTA in 13 patients. Intravascular Doppler velocities and pressures were obtained in 18 arteries over 120 ± 55 cardiac cycles to validate CTA-CFD simulations. A lumped parameter network (LPN) was tuned to capture each patient's heart and distal coronary circulation, and coupled to the CFD solver. For each patient, Murray's Law coefficient was varied from 2.0 to 3.0 in increments of 0.2. The simulated velocities and pressures were compared to intravascular measurements. RESULTS: The correlation between intravascular and CTA-CFD parameters showed no statistically significant correlation for velocity (r=-0.13 and p = 0.60), while both flow rates (r = 0.77, p < 0.01) and pressures (r = 0.88, p < 0.01) demonstrated strong correlation and statistical significance. When considering the intra-patient cycle-to-cycle variabilities in invasive measurements, velocity in 11 of 18 and pressures in 7 of 18 coronary arteries were within one standard deviation of intravascular measurement variability. CONCLUSION: CTA-CFD simulations showed statistically significant correlations for intravascular flows and pressures, whereas no meaningful correlation was observed for velocity. These findings highlight the influence of measurement variability and modeling assumptions. Future studies should consider the inherent uncertainties in CTA-CFD, especially when estimating absolute hemodynamic parameters such as velocity and pressure, and aim to refine boundary conditions and validation strategies to improve accuracy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.069
GPT teacher head0.371
Teacher spread0.302 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2025
Admission routes2
Has abstractyes

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