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arxiv: 2605.09629 · v1 · submitted 2026-05-10 · 📡 eess.IV · cs.CE· physics.comp-ph

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Image-Based Whole-Heart Cardiac Flow Simulations in Health and Congenital Heart Disease

Aaron Brown, Alison Marsden, Daniel B. Ennis, Fanwei Kong, Lei Shi, Michael Loecher, Michael Ma, Perry S. Choi

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Pith reviewed 2026-05-12 02:59 UTC · model grok-4.3

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The pith

An image-based whole-heart CFD framework with ML segmentation and RIS valve modeling reproduces physiologic pressures, valve timing, and flow structures in healthy and pediatric CHD patients, showing qualitative agreement with imaging and quantitative agreement with catheterization.

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

The work starts with patient heart scans taken over time. Machine learning segments the chambers and vessels, then propagates meshes to create a moving 3D model of the entire heart. Blood flow is simulated using computational fluid dynamics on this deforming geometry. All four valves are represented by resistive immersed surfaces that open and close with realistic timing instead of needing exact valve shapes. In a healthy adult, the model produced normal pressure-volume loops, correct valve opening times, and the expected swirling vortex in the ventricle. In a child with complex congenital heart disease, chamber and vessel pressures aligned with direct catheter measurements, while flow patterns matched 4D-Flow MRI but revealed finer structures hidden by imaging noise. The CHD case also showed more disorganized diastolic flow and higher energy loss from viscosity compared with the healthy heart.

Core claim

In the CHD case, simulated chamber and vessel pressures showed agreement with cardiac catheterization measurements. Simulated flow fields were qualitatively consistent with 4D-Flow MRI, while providing higher-resolution visualization of flow structures that were partially obscured by imaging artifacts.

Load-bearing premise

The resistive immersed surfaces model accurately captures physiologically realistic opening and closing dynamics for all four valves using only image-derived chamber motion and without patient-specific valve geometry or direct flow measurements through the valves.

Figures

Figures reproduced from arXiv: 2605.09629 by Aaron Brown, Alison Marsden, Daniel B. Ennis, Fanwei Kong, Lei Shi, Michael Loecher, Michael Ma, Perry S. Choi.

Figure 1
Figure 1. Figure 1: Meshes constructed for the healthy patient and for the CHD patient at the beginning of the simulation (start of [PITH_FULL_IMAGE:figures/full_fig_p005_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Reconstruction of time-series whole-heart anatomical models from imaging data for the healthy patient. (Top) [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Reconstruction of time-series whole-heart anatomical models from imaging data for the CHD patient. (Top) [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Template valve anatomies prior to morphing into patient-specific geometries. [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Closed-loop coupling between the 3D RIS–ALE cardiac flow model and the LPN for the healthy (left) and [PITH_FULL_IMAGE:figures/full_fig_p010_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Cardiac chamber volume curves after applying wall motion boundary conditions obtained from image [PITH_FULL_IMAGE:figures/full_fig_p012_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Valve setup for the healthy subject and the CHD patient. Left: Iso-surface of the signed-distance function [PITH_FULL_IMAGE:figures/full_fig_p013_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Systemic and pulmonary arterial pressure curves computed by the closed-loop 0D LPN [PITH_FULL_IMAGE:figures/full_fig_p014_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Simulation results for the healthy subject. (a) Pressure–volume loops for the left and right ventricles. (b) [PITH_FULL_IMAGE:figures/full_fig_p015_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Visualization of simulated velocity and pressure fields for the healthy subject. From top to bottom: Q [PITH_FULL_IMAGE:figures/full_fig_p016_10.png] view at source ↗
Figure 11
Figure 11. Figure 11: Flow patterns in the LV, LA, aorta (top) and in the RV, RA, PA (bottom) for the healthy subject during late [PITH_FULL_IMAGE:figures/full_fig_p017_11.png] view at source ↗
Figure 12
Figure 12. Figure 12: Systemic and pulmonary arterial pressure curves computed by the closed-loop 0D LPN for the CHD patient. [PITH_FULL_IMAGE:figures/full_fig_p018_12.png] view at source ↗
Figure 13
Figure 13. Figure 13: Comparison of simulated chamber pressures with cardiac catheterization data. For consistency with the [PITH_FULL_IMAGE:figures/full_fig_p019_13.png] view at source ↗
Figure 14
Figure 14. Figure 14: Simulation results for the CHD patient. (a) Pressure–volume loops for the left and right ventricles. (b) [PITH_FULL_IMAGE:figures/full_fig_p020_14.png] view at source ↗
Figure 15
Figure 15. Figure 15: Visualization of simulated velocity and pressure fields for the CHD patient. From top to bottom: Q-criterion [PITH_FULL_IMAGE:figures/full_fig_p021_15.png] view at source ↗
Figure 16
Figure 16. Figure 16: Flow patterns in the morphological LV, RA and PA (top) and in the morphological RV, LA, and aorta [PITH_FULL_IMAGE:figures/full_fig_p022_16.png] view at source ↗
Figure 17
Figure 17. Figure 17: Comparison of simulated velocity fields with 4D-Flow MRI, shown as volumetric renderings at different [PITH_FULL_IMAGE:figures/full_fig_p022_17.png] view at source ↗
Figure 18
Figure 18. Figure 18: Comparison of simulated velocity fields with 4D-Flow MRI. The comparison is performed at three cross [PITH_FULL_IMAGE:figures/full_fig_p024_18.png] view at source ↗
Figure 19
Figure 19. Figure 19: Comparison of simulated and MRI-derived flow rates over the cardiac cycle. Flow rates in the aorta and PA [PITH_FULL_IMAGE:figures/full_fig_p025_19.png] view at source ↗
Figure 20
Figure 20. Figure 20: Comparison of volume-normalized viscous dissipation ( [PITH_FULL_IMAGE:figures/full_fig_p027_20.png] view at source ↗
read the original abstract

Intracardiac flow patterns are shaped by the coupled motion of the cardiac chambers and heart valves and provide important information about cardiac function. However, clinical flow imaging remains limited by exam times, noise, resolution, and incomplete details of the three-dimensional flow. Computational fluid dynamics (CFD) can potentially provide detailed flow quantification and predictive insight into treatment outcomes, but clinical translation requires frameworks that reproduce patient-specific measurements while balancing physiological realism, computational cost, and modeling effort. Herein, we present an image-based, patient-specific computational framework for simulating whole-heart intracardiac hemodynamics that balances physiological fidelity with computational efficiency. The framework first employs machine learning-based segmentation and mesh propagation to reconstruct moving cardiac anatomies from time-resolved images. CFD simulations are then performed to resolve blood flow in deforming domains, while resistive immersed surfaces (RIS) are used to model all four cardiac valves with physiologically realistic opening and closing dynamics. The framework was applied to model hemodynamics in a healthy adult and a pediatric patient with complex congenital heart disease (CHD). In the healthy case, the simulations reproduced physiologic pressure-volume behavior, valve timing, and ventricular vortex formation. In the CHD case, simulated chamber and vessel pressures showed agreement with cardiac catheterization measurements. Simulated flow fields were qualitatively consistent with 4D-Flow MRI, while providing higher-resolution visualization of flow structures that were partially obscured by imaging artifacts. Comparison between the healthy and CHD cases further revealed altered diastolic flow organization and elevated normalized viscous dissipation in the CHD heart.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The manuscript presents an image-based patient-specific CFD framework for whole-heart intracardiac hemodynamics. It reconstructs moving cardiac geometries via machine learning segmentation and mesh propagation from time-resolved images, solves Navier-Stokes equations in deforming domains, and employs resistive immersed surfaces (RIS) to model physiologically realistic opening/closing of all four valves without explicit patient-specific valve geometry. Demonstrated on one healthy adult (reproducing physiologic PV loops, valve timing, and ventricular vortices) and one pediatric CHD patient (pressure agreement with catheterization; qualitative flow consistency with 4D-Flow MRI plus higher-resolution visualization of structures obscured by artifacts), the work also contrasts altered diastolic flow organization and elevated normalized viscous dissipation in the CHD case.

Significance. If the central claims hold, the framework offers an efficient route to detailed, patient-specific whole-heart flow fields from standard clinical images, filling a gap between limited-resolution 4D-Flow MRI and more expensive fully resolved valve models. The RIS approach for valves is a pragmatic strength that reduces modeling effort while still capturing timing effects. Direct comparison to both invasive catheterization and non-invasive MRI in a complex CHD anatomy is valuable for assessing translational potential. The single-case demonstration and lack of quantitative error metrics currently limit the strength of the evidence for predictive fidelity.

major comments (1)
  1. [CHD case results] CHD case results: The reported agreement between simulated chamber/vessel pressures and catheterization measurements is central to the claim that the framework reproduces patient-specific hemodynamics. However, chamber volumes are image-prescribed, so pressures depend sensitively on net inflow/outflow timing and resistance; the manuscript provides no sensitivity analysis on the free RIS resistance and opening parameters, no patient-specific valve geometry, and no direct transvalvular flow measurements to confirm that the RIS dynamics are physiologically realistic rather than adjusted to match the pressure data.
minor comments (2)
  1. [Abstract] Abstract: The statement that simulated pressures 'showed agreement' with catheterization would be strengthened by reporting at least one quantitative metric (e.g., RMSE or peak-pressure difference) rather than a qualitative descriptor.
  2. [Methods] Figure captions and methods: Clarify whether the RIS parameters were held fixed across both subjects or adjusted per case; if fixed, state the values and any literature basis.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for the constructive and detailed review. The feedback highlights an important aspect of the CHD case validation. We respond point-by-point below and outline revisions that will strengthen the manuscript without altering its core claims or methods.

read point-by-point responses

  1. Referee: [CHD case results] CHD case results: The reported agreement between simulated chamber/vessel pressures and catheterization measurements is central to the claim that the framework reproduces patient-specific hemodynamics. However, chamber volumes are image-prescribed, so pressures depend sensitively on net inflow/outflow timing and resistance; the manuscript provides no sensitivity analysis on the free RIS resistance and opening parameters, no patient-specific valve geometry, and no direct transvalvular flow measurements to confirm that the RIS dynamics are physiologically realistic rather than adjusted to match the pressure data.

    Authors: We agree that the pressure agreement is a key result and that a sensitivity analysis would better demonstrate robustness. The RIS resistance and opening parameters were selected from literature values for physiologic valve timing and transvalvular pressure drops (Methods section), then verified to produce valve opening/closing consistent with the time-resolved images and to yield pressure-volume loops matching expected physiology in the healthy case. In the CHD case, the same parameter set produced chamber and vessel pressures in agreement with the available catheterization data. We acknowledge that the original manuscript did not include a formal sensitivity study. In revision we will add a dedicated subsection reporting the effect of varying RIS resistance and timing parameters over physiologic ranges on the resulting pressures and flow structures; this will show that the reported agreement is not overly sensitive to small changes. The RIS formulation is intentionally geometry-free precisely because patient-specific valve anatomy is rarely available from standard clinical imaging; this is presented as a pragmatic modeling choice rather than a limitation to be overcome with additional data. Direct transvalvular flow measurements were not part of the clinical dataset provided for this patient, so they cannot be added; however, the simulated flow fields remain qualitatively consistent with the available 4D-Flow MRI, including large-scale structures and regions of elevated dissipation. These additions will be incorporated in the revised manuscript. revision: partial

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper describes a forward CFD simulation pipeline: machine-learning segmentation produces time-resolved geometries whose motion is prescribed as boundary conditions; the incompressible Navier-Stokes equations are solved in deforming domains; resistive immersed surfaces supply valve resistance without direct fitting to the reported catheterization pressures or 4D-Flow data. Pressure-volume agreement and flow-field consistency are presented as post-hoc validation against independent measurements, not as quantities that reduce to the inputs by construction. No self-definitional equations, fitted-input predictions, or load-bearing self-citation chains appear in the described methodology.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claims rest on domain assumptions about segmentation accuracy and valve modeling fidelity rather than new physical principles; limited information from abstract prevents exhaustive enumeration of all free parameters.

free parameters (1)
  • RIS valve resistance and opening parameters
    Parameters controlling valve dynamics are introduced to achieve physiologically realistic opening and closing; their specific values or fitting procedure are not detailed in the abstract.
axioms (2)
  • domain assumption Machine learning segmentation and mesh propagation accurately reconstruct time-resolved deforming cardiac anatomies from images
    This is the foundational step for patient-specific geometries and is invoked without reported validation metrics in the abstract.
  • domain assumption Resistive immersed surfaces can represent all four cardiac valves with realistic hemodynamics using only chamber motion
    Central modeling choice for valves; no direct patient valve data is mentioned.

pith-pipeline@v0.9.0 · 5593 in / 1509 out tokens · 39127 ms · 2026-05-12T02:59:46.675886+00:00 · methodology

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