arxiv: 2605.03536 · v1 · submitted 2026-05-05 · 💻 cs.CE · cs.NA· math.NA

Recognition: unknown

Device-Induced Thrombus Formation in Cerebral Aneurysms: Linking Patient-Specific Clot Modeling and Functional Occlusion to Virtual Angiographic Assessment

Barbara Wohlmuth, Fabian Holzberger, Malebogo Ngoepe, Markus Muhr, Struan Hume

Authors on Pith no claims yet

Pith reviewed 2026-05-07 12:41 UTC · model grok-4.3

classification 💻 cs.CE cs.NAmath.NA

keywords cerebral aneurysmendovascular treatmentthrombus formationvirtual angiographycomputational hemodynamicsflow diversionstent-assisted coilingocclusion assessment

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The pith

A coupled simulation framework shows early thrombus formation drives much of the perfusion suppression and altered contrast washout seen in virtual angiograms after aneurysm device placement.

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

The paper develops a computational framework that links models of acute fibrin thrombus formation directly to virtual contrast transport simulations, allowing early clot growth in treated cerebral aneurysms to be read out through DSA-style images. It applies this approach to three standard endovascular strategies—coiling, flow diversion, and stent-assisted coiling—under realistic pulsatile flow conditions across three representative aneurysm shapes. The simulations find that while the devices themselves reduce inflow, residual contrast entry and trapping often remain, and the developing thrombus accounts for a large share of the observed drop in perfusion together with changed washout timing. These combined effects appear clearly in the generated angiographic sequences, and vortical flow features are shown to promote thrombosis in at least one morphology. The work therefore offers a way to translate detailed mechanical modeling into the occlusion metrics that clinicians already use.

Core claim

Coupling acute fibrin thrombus formation with virtual angiography under pulsatile hemodynamics reveals that early clot growth contributes substantially to functional occlusion, producing visible reductions in perfusion and shifts in contrast washout patterns even when devices leave some residual contrast access, with these signatures appearing directly in the simulated DSA-like images.

What carries the argument

The coupled acute fibrin thrombus formation model and virtual contrast transport simulation that converts device-induced clotting into clinically interpretable angiographic signals.

If this is right

Early thrombus formation substantially augments the perfusion suppression achieved by inflow reduction alone.
Residual contrast access and trapping can persist after device placement and are visible in virtual images.
Altered washout patterns produced by thrombus growth are directly reflected in the simulated angiographic sequences.
Vortical flow structures promote device-induced thrombosis in at least some aneurysm morphologies.
The framework supplies a route for evaluating occlusion outcomes through metrics already familiar in clinical DSA assessment.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

Patient-specific versions of the model could be used to rank which device type is most likely to produce rapid functional occlusion for a given aneurysm shape.
Linking the simulation output to real-time DSA data streams might help explain why certain treatments achieve incomplete isolation.
Extending the thrombus model beyond the acute phase could allow prediction of long-term occlusion durability.
The same coupling technique could be applied to other vascular sites where devices are used to induce localized clotting.

Load-bearing premise

The acute fibrin thrombus formation model and virtual contrast transport simulation accurately represent real in-vivo processes and DSA imaging without direct experimental or clinical validation.

What would settle it

Side-by-side comparison of the generated virtual angiograms against actual post-treatment DSA sequences from patients or animal models that have the same aneurysm geometries and device placements.

Figures

Figures reproduced from arXiv: 2605.03536 by Barbara Wohlmuth, Fabian Holzberger, Malebogo Ngoepe, Markus Muhr, Struan Hume.

**Figure 1.** Figure 1: The three exemplary cases of different cerebral aneurysm morphologies treated in this work. (Left) Case 1: Small, saccular aneurysm where both, stenting and/or coiling could be employed. An exemplary coil is inserted for visualization of the method. (Middle) Case 2: Large fusiform aneurysm and a prime example for stent-assisted coiling. (Right) Case 3: Very large saccular aneurysm, suitable for (material i… view at source ↗

**Figure 2.** Figure 2: (left) Vascular-tree geometry including an aneurysm, reconstructed from CT-images, (middle) Angiographic images from during the coil insertion procedure on that geometry. First, at the beginning, with only the guiding wire visible, and second, towards the end of the procedure, with a surgical balloon (outlined in green) used to stabilize the coil in place, (right) Schematic sketch of a coil’s micro-wire sh… view at source ↗

**Figure 3.** Figure 3: Simulated insertion of two coils from [32]. (Left) shows the insertion of an initial framing coil (red) through a micro-catheter, (right) is a subsequently inserted filling coil (blue) with the micro-catheter being removed again. D2/2, and are hence not physically penetrating. In order to obtain a three-dimensional (object-) representation of the final coil shape for further (numerical) analysis, e.g., to … view at source ↗

**Figure 4.** Figure 4: 3d-reconstructed flow diverter geometries within their respective vessel geometries (transparent): (Left) Case 1, (Middle) Case 2 and (Right) Case 3. The aneurysms are shown in transparent red, while the red loops mark the ostium of the respective aneurysm or, in case 2, the beginning and end of the fusiform dilation of the parenting vessel, i.e., the region of interest. 2.3. Stent-assisted coiling To simu… view at source ↗

**Figure 5.** Figure 5: Stent-assisted coiling via balloon for Case 2. (Left) The initial configuration of the surrogate balloon before inflation. (Middle) The final shape of the inflated balloon aligning with the FD mesh. (Right) the result of the subsequent stent-assisted coiling with three different coils (colors only for better distinction). The surrogate balloon is then removed for the fluid- and thrombus growth simulations.… view at source ↗

**Figure 6.** Figure 6: Truncated Fourier approximation of the inlet velocity and outlet pressure profiles. Pressure offset: 75.07, Velocity offset: 19.59, Period: T = 10 9 ≈ 1.111 s (one heart-beat). 3.2. Biochemistry The transport equation accounts for convection and diffusion of biochemical proteins that contribute to the clotting process, and is detailed in the transport model for a passive scalar in the view at source ↗

**Figure 7.** Figure 7: (Left) Aneurysm wall thrombin release function gIIa(t) (see equation (9)) over time, (right) the respective parameter values for the model-function. To account for the expression of thrombin from the aneurysm wall, a thrombin release function, shown against time in view at source ↗

**Figure 8.** Figure 8: on the left. To obtain qualitatively comparable contrast distributions, the proportionality factor k (see (12)) was chosen based on a visual comparison with the angiographic appearance of case E reported in [10, view at source ↗

**Figure 9.** Figure 9: Simulated final thrombi (in red) in different treatment scenarios. (Left) empty, untreated aneurysm (for reference), (Middle) FD treated aneurysm with the thrombus mainly attaching to the aneurysm walls, leaving a considerable void volume inside, (Right) Coiling-based treated aneurysm with thrombus growing “along” the coiling wire. The thrombus results for this case enable direct comparison of the treatm… view at source ↗

**Figure 10.** Figure 10: Failed coiling case with the coil protruding into the parenting vessel. (First left) Failed Coil geometry, (Second left) resulting thrombus comparable to the one of the successful coiling in view at source ↗

**Figure 11.** Figure 11: Simulated final thrombi (in red) comparing different treatment cases. Note that the case with an empty / untreated aneurysm did not produce any thrombus and is hence left out here. (Left) FD-only treatment with very scarce thrombus growth on the aneurysm dome only. (Right) Stent-assisted coiling treatment using three coils with the resulting thrombus covering larger parts of the aneurysm dome. Treatment c… view at source ↗

**Figure 12.** Figure 12: Simulated final thrombi (in red) in different treatment cases. (Top left) Empty, untreated aneurysm, (Top middle) FD treated aneurysm, (Top right) Visualization of the coiling. (Bottom left) Thrombus with coils, (Bottom middle) Cross-section of the coil-induced thrombus, (Bottom right) Again a cross-section, but once more for the alternative model where not only the aneurysm dome but also the coil emits t… view at source ↗

**Figure 13.** Figure 13: Flow-pattern and early thrombus geometry comparison for different treatment cases: The (rows) stand for three characteristic points in time while the (columns) compare different treatment methods (first column shows the empty aneurysm for reference) view at source ↗

**Figure 14.** Figure 14: Comparison of clot volume versus time for different treatment modes used in Case 3. The initial clot development rate are similar across all three treatment modes, however the volume stabilises at a lower value for coiling view at source ↗

**Figure 15.** Figure 15: 3D tracer field visualization of Case 3 showing the aneurysm occlusion in different setups. Each column corresponds to the temporal snapshot at peak systoles resulting from a simulation over four heart-beat cycles. In the first row (a)-(d): No coil placed. In the second row (e)-(h): 2 coils placed. Third row (i)-(l): 5 coils placed. Fourth row (m)-(p): After coil induced thrombosis. Last row (q)-(t): Aft… view at source ↗

**Figure 16.** Figure 16: 2D DSA of Case 3 showing the projected occlusion results for two setups similar to view at source ↗

**Figure 17.** Figure 17: 3D tracer field visualization of Case 1 showing aneurysm occlusion in different setups. Each column corresponds to the temporal snapshot at peak systoles resulting from a simulation over four heart-beat cycles. The first row (a)-(d): No coil placed. The second row (e)-(h): coil placed, but no thrombosis. The third row (i)-(l): After coil-induced thrombosis. The virtual contrast agent results showed a clea… view at source ↗

**Figure 18.** Figure 18: 3D tracer field visualization of Case 2 showing aneurysm occlusion in different setups. Each column corresponds to the temporal snapshot at peak systoles resulting from a simulation over four heart-beat cycles. In the first row (a)-(d): No coil placed. In the second row (e)-(h): stent-assisted coiling in place. In the third row (i)-(l): After stent-assisted coiling induced thrombosis. In the last row (m)-… view at source ↗

read the original abstract

Endovascular treatment of cerebral aneurysms aims to achieve functional occlusion and isolation of the aneurysm sac from bloodflow. In clinical practice, treatment success is assessed primarily through digital subtraction angiography (DSA), which visualizes contrast-agent inflow and washout but does not directly resolve thrombus formation driving early occlusion. We present a computational framework that couples acute fibrin thrombus formation with virtual angiography, enabling early thrombus growth to be interpreted through clinically familiar DSA-like imaging. Three common treatment strategies: endovascular coiling, flow diversion, and stent-assisted coiling, are modeled under pulsatile hemodynamics and linked to simulated contrast transport. Across three representative aneurysm morphologies, the simulations demonstrate that while devices reduce inflow, residual contrast access and trapping may persist, with early thrombus formation contributing substantially to perfusion suppression and altered washout patterns. These effects are clearly reflected in the virtual angiographic imaging. The importance of vortical structures in device-induced thrombosis is highligthed in one of the cases. By seeking to align modelling and simulation tools with clinically-relevant metrics, with a particular focus on occlusion outcome, this work presents a good starting point for bridging the gap between these two paradigms.

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.

Desk Editor's Note private letter to a colleague

The paper couples thrombus formation modeling to virtual angiography for aneurysm devices but the main claims about thrombus substantially adding to occlusion rest on unvalidated kinetics and contrast transport.

read the letter

The paper's main move is to link an acute fibrin thrombus model to simulated contrast transport so that the output looks like clinical DSA images. They run this for coiling, flow diversion, and stent-assisted coiling in three aneurysm shapes under pulsatile flow. The simulations show devices reduce inflow but leave some contrast trapping, and early thrombus growth explains a noticeable share of the perfusion drop and washout changes. One case also flags vortical flow as important for thrombosis location. That framing is useful because it tries to speak the language of interventionalists rather than just reporting wall shear or velocity fields. The internal coupling looks consistent on paper, and the choice to include multiple device types and realistic pulsatile conditions is reasonable for a demonstration. The soft spot is exactly what the abstract flags: this is presented as a starting point, and the results depend on thrombus formation rate constants and contrast coefficients that receive no experimental or clinical benchmark here. Without sensitivity checks or comparison to real clot growth rates or DSA sequences, it is hard to tell how much the attributed effect of thrombus versus device geometry is driven by the chosen parameters. The stress-test concern about the unvalidated fibrin model and virtual angiography pipeline holds up based on what is shown. This is aimed at computational researchers in hemodynamics and thrombosis who want to connect models to imaging endpoints. A reader already working on aneurysm simulations would get concrete ideas for output formats, but anyone expecting predictive accuracy for patient outcomes would need the missing validation layer first. I would send it for peer review. The framework is new enough that referees can push on the parameter dependence and suggest benchmarks without the work being dismissed outright.

Referee Report

3 major / 2 minor

Summary. The manuscript presents a computational framework coupling patient-specific acute fibrin thrombus formation modeling with virtual angiography to assess functional occlusion in cerebral aneurysms treated by endovascular coiling, flow diversion, or stent-assisted coiling. Under pulsatile hemodynamics, simulations for three representative aneurysm morphologies demonstrate that device-induced inflow reduction is augmented by early thrombus growth, which substantially suppresses perfusion and alters contrast washout patterns as visualized in the simulated DSA-like imaging; vortical flow structures are noted as important in one morphology.

Significance. If the coupled model holds, the work offers a clinically aligned way to interpret DSA findings in terms of underlying thrombus dynamics rather than device geometry alone, potentially aiding prediction of early occlusion outcomes. The explicit linkage of thrombus kinetics to virtual angiographic metrics is a constructive step toward bridging computational hemodynamics with treatment assessment paradigms.

major comments (3)

[Abstract/Methods] Abstract and Methods: The central claim that 'early thrombus formation contributing substantially to perfusion suppression' is not supported by any reported parameter values, rate constants for fibrin formation or platelet adhesion, sensitivity analysis, or direct validation against experimental/clinical thrombus growth rates or DSA washout data; without these the attribution of effects to thrombus versus device inflow reduction cannot be evaluated.
[Results] Results: No quantitative metrics (e.g., time to occlusion, residual contrast volume, or washout half-times) comparing device-only versus device-plus-thrombus cases are provided across the three morphologies, leaving the 'substantially' qualifier and the virtual angiographic reflection of thrombus effects unquantified.
[Methods] Methods: The virtual contrast transport simulation and its coupling to the thrombus model lack benchmarking against in-vivo or in-vitro DSA imaging under device-altered flows; this directly affects the reliability of the claimed alignment with clinical assessment.

minor comments (2)

[Abstract] Abstract: Typo in 'highligthed' (should be 'highlighted').
[Abstract] Abstract: The self-referential phrase 'this work presents a good starting point' should be revised to an objective statement about the framework's scope and limitations.

Simulated Author's Rebuttal

3 responses · 2 unresolved

We thank the referee for the constructive and detailed comments. We have addressed each major point below, providing clarifications and committing to revisions that strengthen the manuscript without overstating the current results.

read point-by-point responses

Referee: [Abstract/Methods] Abstract and Methods: The central claim that 'early thrombus formation contributing substantially to perfusion suppression' is not supported by any reported parameter values, rate constants for fibrin formation or platelet adhesion, sensitivity analysis, or direct validation against experimental/clinical thrombus growth rates or DSA washout data; without these the attribution of effects to thrombus versus device inflow reduction cannot be evaluated.

Authors: We agree that explicit parameter reporting is essential. The thrombus model employs rate constants for fibrin formation and platelet adhesion drawn from established literature on acute thrombus kinetics under shear. In the revised manuscript we will add a table in Methods listing all parameter values, their literature sources, and the governing equations. A full sensitivity analysis and direct experimental/clinical validation of growth rates against DSA data are beyond the scope of this computational framework; we will explicitly note this limitation in the Discussion and qualify the 'substantially' claim as arising from comparative device-only versus device-plus-thrombus simulations rather than absolute validation. revision: partial
Referee: [Results] Results: No quantitative metrics (e.g., time to occlusion, residual contrast volume, or washout half-times) comparing device-only versus device-plus-thrombus cases are provided across the three morphologies, leaving the 'substantially' qualifier and the virtual angiographic reflection of thrombus effects unquantified.

Authors: We accept this observation. The existing simulation datasets contain the necessary time-resolved contrast fields. In the revised Results we will add quantitative metrics—washout half-times, residual contrast volume at 5 s and 10 s post-injection, and effective perfusion suppression ratios—for device-only versus device-plus-thrombus cases across all three morphologies. These will be presented in a new table and referenced in the text to make the contribution of early thrombus growth explicit and quantifiable. revision: yes
Referee: [Methods] Methods: The virtual contrast transport simulation and its coupling to the thrombus model lack benchmarking against in-vivo or in-vitro DSA imaging under device-altered flows; this directly affects the reliability of the claimed alignment with clinical assessment.

Authors: The contrast transport is solved via an advection-diffusion equation on the thrombus-modified velocity field using standard numerical methods previously validated for aneurysm CFD. We will expand the Methods section to include explicit references to prior benchmarking studies of similar contrast-transport models and to describe the one-way coupling procedure in greater detail. New in-vivo or in-vitro DSA benchmarking under device conditions is not feasible within the present study; we will acknowledge this as a limitation and identify it as future work. revision: partial

standing simulated objections not resolved

Direct validation of thrombus growth rates and DSA washout patterns against experimental or clinical data under device-altered flows
Comprehensive sensitivity analysis of all thrombus-model parameters

Circularity Check

0 steps flagged

No significant circularity; derivation self-contained in simulation framework

full rationale

The accessible manuscript text consists of the abstract and high-level description of a coupled computational framework for thrombus formation and virtual angiography. No equations, parameter-fitting procedures, or derivation steps are quoted that reduce predictions to inputs by construction. Claims about thrombus contribution to occlusion are presented as outcomes of the simulations across morphologies, without self-definitional loops, fitted inputs renamed as predictions, or load-bearing self-citations that collapse the central result. The work explicitly positions itself as a starting point without invoking uniqueness theorems or ansatzes from prior author work as forcing mechanisms. This is the normal case of an independent modeling study whose results can be benchmarked externally.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 0 invented entities

Only abstract available, so ledger is inferred from typical elements in computational hemodynamics and thrombosis papers; full text would list exact parameters and assumptions.

free parameters (2)

Thrombus formation rate constants
Parameters governing acute fibrin thrombus growth under device-induced flow conditions are required to produce the claimed occlusion effects.
Contrast transport coefficients
Values controlling simulated contrast inflow, washout, and trapping in the virtual angiography module.

axioms (2)

domain assumption Thrombus model captures real acute fibrin formation dynamics in aneurysms
Invoked to link device hemodynamics to functional occlusion.
domain assumption Virtual angiography reproduces key features of clinical DSA
Required for the claim that effects are reflected in the imaging.

pith-pipeline@v0.9.0 · 5529 in / 1363 out tokens · 56794 ms · 2026-05-07T12:41:08.985725+00:00 · methodology

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