Flow-based simulation in transverse sinus stenosis pre- and post-stenting: pressure prediction accuracy, hemodynamic complexity, and relationship to pulsatile tinnitus

Abstract

Background

The proximity of transverse sinus stenosis (TSS) to inner ear structures and the temporal bone makes it a substantial cause of pulsatile tinnitus (PT). Treatment typically involves venous sinus stenting. This study investigates the hemodynamic stressors in TSS patients with PT along the pulse-transmitting temporal bone area and evaluates its treatment effects.

Methods

Four patients with idiopathic intracranial hypertension, PT, and TSS, and four control patients were imaged using MR venography (MRV) and flat panel CT (FP-CT). Patient-specific blood flow simulations were conducted using boundary conditions based on quantitative MR angiography before and after VSS. Catheter-based trans-stenotic pressure gradient measurements were used to validate the simulation results.

Results

The prediction of pressure gradients was close to catheter-based measurements using FP-CT-based segmentations (absolute deviation of 0.35 mm Hg) and is superior to MRV-based reconstructions (absolute deviation of 6.9 mm Hg). In TSS patients, the sinus temporal bone contact areas revealed notably higher time-averaged wall shear stress by 47±22% and velocity values by 41±18% compared with the sinus brain side. The relative residence time decreased by 57±58%. After stenting, the hemodynamic parameters dropped at the temporal side and throughout the sigmoid sinus. Almost all control patient hemodynamics remained lower than post-interventional results.

Conclusion

Our simulations based on patient-specific flows highly predicts pressure gradients across the stenosis. Flow conditions in TSS reveal flow jet formation and high shear rates at the temporal bone, potentially causing sound transmission. The treatment reduces these stressors, demonstrating its targeted therapeutic effect.

WHAT IS ALREADY KNOWN ON THIS TOPIC

• Pulsatile tinnitus (PT) is frequently linked to vascular anomalies, particularly transverse sinus stenosis (TSS), which is known to disrupt venous outflow and is associated with idiopathic intracranial hypertension. Hemodynamic changes in TSS patients, such as increased pressure gradients, disturbed flow patterns, and high-frequency fluctuations distal to the stenosis, might contribute to PT development.

WHAT THIS STUDY ADDS

• This study uses patient-specific flow simulations with quantitative MR angiography measurements to model the hemodynamics of TSS. We found that flat-panel CT segmentation models were superior to MR venography in accurately resolving patient-specific pressure gradients. Additionally, by incorporating the temporal bone structures, we identified complex hemodynamics at the sinus-bone contact areas, which were altered following treatment with venous stenting, revealing the potential of therapeutic intervention.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

• Current treatment decisions of TSS are heavily based on invasive measurements. This study advocates for the inclusion of local anatomic information, such as the temporal bone, into image-based flow simulations, potentially allowing for a non-invasive classification of stenosis severity and evaluation of treatment effects. This approach could transform the management of TSS-related PT by providing a more comprehensive, patient-specific diagnostic tool for clinicians.

Introduction

Transverse sinus stenosis (TSS) is characterized by a narrowing of the transverse sinus that disrupts venous outflow and is often associated with increased intracranial pressure and idiopathic intracranial hypertension (IIH) with/without pulsatile tinnitus (PT).¹ TSS has been increasingly recognized in clinical settings due to advancements in neuroimaging techniques, such as MR venography (MRV). Patients with TSS may present with a range of symptoms, including headache, visual disturbances and pulsatile tinnitus (PT)—a condition in which rhythmic, heartbeat-synchronized sounds are perceived. PT accounts for 10–15% of all tinnitus cases² and is frequently associated with underlying vascular anomalies,³ especially venous pathologies.4,6 PT can significantly impact quality of life, sleep, as well as brain function.^{7 8} Current treatments, including endovascular stenting, aim to alleviate symptoms and reduce pressure gradients, but there is ongoing debate about indications and efficacy.⁹ When associated with TSS, PT is thought to arise from disturbed hemodynamics within the sinus, making the prediction of flow dynamics and pressure gradients critical to understanding symptom mechanisms and guiding interventions.

Despite its clinical importance, predicting hemodynamic changes in TSS remains a challenge. Image-based blood flow simulations have remarkably enhanced the understanding of TSS and its association with PT. However, existing computational fluid dynamics (CFD) studies often lack patient-specific boundary conditions, limiting their accuracy in predicting trans-stenotic pressure gradients. Notably, morphological differences and flow conditions in veins and sinuses have been identified as key factors influencing hemodynamics in these structures.^{10 11} Furthermore, prior work has largely overlooked the role of surrounding anatomical structures, such as the temporal bone, which are critical for understanding sound transmission and flow instabilities that may contribute to PT.

Recent studies have highlighted the need for improved modeling approaches. For example, Sidora et al demonstrated that incorporating realistic flow boundary conditions significantly enhances the accuracy of CFD predictions.¹² Similarly, Hong et al identified disturbed flow patterns, such as laminar vortical flow and high-frequency fluctuations, as key contributors to PT in TSS patients.¹³ However, the absence of external structural data and quantitative analysis of critical regions, such as the inner ear, remains a limitation in existing research. This gap is particularly significant given the association of PT with bone erosion and flow instabilities distal to the stenosis.¹⁴

This study addresses these limitations by using multimodal medical imaging to extract patient-specific venous lumens and surrounding skull domains, including the temporal bone. It demonstrates a novel CFD simulation that incorporates patient-specific inflow boundary conditions to predict pressure gradients and validates it against in vivo trans-stenosis pressure gradient measurements. By analyzing hemodynamic parameters in IIH patients with TSS pre- and post-intervention, as well as in healthy controls, this approach aims to offer a deeper understanding of the hemodynamic mechanisms underlying PT.

Methods

Patient selection, imaging, and manometry data

In this retrospective, single-center study, patients diagnosed with IIH and PT were reviewed. IIH diagnosis was based on the modified Dandy criteria: symptoms of increased intracranial pressure (ICP), increased lumbar puncture opening pressure with normal cerebrospinal fluid composition, no localizing findings on neurological examination, and no structural brain lesion or other cause of increased ICP. Inclusion criteria included presence of TSS on MRV, as well as a complete set of imaging and procedural data. Healthy patients (without IIH or TSS) were selected for comparison in the simulations and served as a control group. All TSS patients underwent endovascular venous stenting at the University of Illinois Chicago, between January and December 2023. All patients received multimodal imaging and data acquisition, including:

1. Flat panel CT (FP-CT) (DynaCT, Siemens Healthineers, Hoffman Estates, IL) acquired with isotropic resolution (0.36 mm), to visualize the arterial and venous vasculature, as well as the surrounding bony anatomy. FP-CT imaging was performed 10–20 s post-contrast arterial injection to capture venous phase contrast.

2. MRV captured sinus vessels with isotropic voxel size of 0.80 mm, using equipment from GE HealthCare (Chicago, IL) and Siemens Healthineers (Hoffman Estate, IL).

3. Phase-contrast quantitative MR angiography (QMRA) was applied to quantify the flow inside the vascular lumen using a non-invasive optimal vessel analysis tool (NOVA, VasSol Inc, River Forest, IL).¹⁵ Time-resolved flow data perpendicular to the respective vessel axis were obtained for all major cerebral arteries and dural venous sinuses.

4. Intracranial venous manometry measured the trans-stenotic pressure gradient for all TSS patients using a 0.021 inch inner lumen microcatheter (Prowler Plus, Cerenovus, Fremont, CA). Mean values were calculated over a 30 s interval.

In the control group, only baseline QMRA flow data were available and invasive pressure measurements were not performed.

Local hemodynamic conditions were analyzed as depicted in figure 1, consisting of four main steps: patient-specific morphological extraction from multimodal imaging, stenosis segmentation and stent modeling, as well as hemodynamic analysis.

Figure 1. (A) The multimodal image data serve as a base for patient-specific morphological extraction. MR venography (MRV) data are used to extract the sinus vasculature. (B) The stenosis area is segmented based on flat panel CT (FP-CT) in addition to the skull anatomy. Extracting the stent, based on post-interventional FP-CT data, allows adjustment of sinus vessels. The asterisk marks the location of the stenosis, which is corrected due to the treatment. Using the Euclidian distance, the temporal bone contact patch (purple surface) and its opposite site (yellow surface) are extracted. (C) Hemodynamic stressors are analyzed along the contact patches by applying patient-specific boundary conditions based on phase-contrast quantitative MR angiography (QMRA).

Venous lumen extraction and temporal bone contact surface extraction

The sinus vessels were segmented mainly using MRV data, representing the relevant vascular regions from the superior sagittal sinus to the jugular vein (JV), while excluding adjacent skull bones. Threshold-based segmentation using MeVisLab v3.4.1 (MeVis Medical Solutions AG, Bremen, Germany) generated an initial segmentation mask, converted into a three-dimensional (3D) triangular surface mesh using marching cubes. The meshes were refined in Blender v3.0 (Blender Foundations, Amsterdam, The Netherlands) to remove surface artifacts, ensure smooth surfaces, and define volumes from the transverse sinus (TS) to the JV, preserving the venous morphology for hemodynamic simulation.¹⁶

For the TSS group, discrepancies were identified in stenosis morphology between MRV and FP-CT data. Therefore, MRV and FP-CT data were co-registered. Here the registration manual module of MeVisLab was utilized, allowing for a rigid body registration. It was conducted globally and rigid, since no elastic components were used. To avoid any unnecessary transformations, translation and rotation were applied only. The corresponding transformation matrix was then applied to the FP-CT dataset, aligning it with the coordinate system of the MRV data. After that, stenotic regions were re-segmented from FP-CT images, excluding skull bones via interval thresholding, in order to exploit FP-CT’s superior resolution. This process yielded two pre-interventional models per TSS patient (online supplemental figure S1): one initially derived from MRV and a second one refined with FP-CT (in the following referred to as MRV and FP-CT based model). Due to the extensive artifacts in the FP-CT data in the area of the skull base and the JV, the MRV data in the peripheral regions were required. Consequently, the FP-CT model was added to the peripheral MRV model part within Blender, including transition and stitching between the co-registered multimodal vessel geometries.

Post-interventional models were virtually generated by integrating post-stenting FP-CT data. The 3D stent images were extracted and the stenosis of the vessel surface was corrected to slightly overlay with the stent struts (figure 1B). The skull bone was segmented from the FP-CT data using thresholding to analyze local flow conditions at the temporal bone side, adjacent to the segmented sinus. Here, only the right-sided region of interest was extracted. The shortest Euclidean distance between the sinus and temporal bone models was calculated. Contact surfaces were mapped onto the transverse and sigmoid sinus regions (figure 1B). This procedure was conducted for the pre- and post-interventional TSS models and the control group.

Image-based blood flow simulations

For each patient-specific model, the inlet and outlet cross-sections were extruded by at least five diameters to ensure developed flow conditions. The image-based blood flow simulations were conducted with the finite volume-based solver STAR-CCM+v17.06 (Siemens PLM Software Inc, Plano, TX). From the 3D surface model, an unstructured volumetric mesh was generated using polyhedral and five layers of prism cells at the lumen using a base size of 0.2 mm. This resulted in a total number of elements between 1.7 and 2.9 million. Mesh independence was previously tested using a case in which the pressure gradient and the change in velocity were calculated. Doubling the number of elements only results in a change of 2% in the pressure gradient and 0.4% in the post-stenotic velocity. Patient-specific inlet boundary conditions were derived from QMRA flow measurements, with mean inlet volume flow-rate values ranging from 246 to 479 mL/min. For all simulations, the inlets were treated as mass flow inlets and transient mass flow rate profiles were applied as inlet conditions based on the time-dependent QMRA measurements of the patients. The individual JV outflow boundaries were defined as simple outlets with a flow rate of 100% of the inflow. This also guarantees the patient-specific outflow here. All vessel walls were assumed to be rigid and blood was modeled as an incompressible (with a density of ρ=1055 kg/m³) as well as Newtonian (with a dynamic viscosity of η=0.004 Pa·s) fluid. For the numerical simulation, convection terms in the momentum equations were discretized using a second order scheme. A segregated flow solver was used where pressure and velocity is coupled using the SIMPLE algorithm. Furthermore, a first order temporal solver with a resolution was set at Δt=0.001 s, with two cardiac cycles computed to ensure accurate results: one to exclude the possible effects of the initialization; and a second to export 20 discrete time steps of the results for further post-processing

Analysis of hemodynamic parameters

All hemodynamic parameters describing the pressure, shear and flow behavior were calculated using the advanced post-processing software EnSight v10.2 (ANSYS Inc, Canonsburg, PA).^{17 18} The underlying equations can be found in the online supplemental material.

1. Trans-stenotic pressure gradient (Δp): Absolute intravascular pressure difference proximal and distal to the stenosis, used to assess stenting candidacy.

2. Mean time-averaged wall shear stress (AWSS_mean): Tangential shear stress exerted to the luminal vessel wall averaged over one cardiac cycle in the area of interest.

3. Maximum time-averaged wall shear stress (AWSS_max): Maximum AWSS within the area of interest.

4. Oscillatory shear index (OSI): Scalar metric describing the direction and magnitude changes of the shear stress vectors over one cardiac cycle.

5. Relative residence time (RRT): Marker of the blood flow distribution levels.

6. Velocity: Absolute values of the local velocity vector magnitudes.

7. Oscillatory velocity index (OVI): Scalar metric describing the direction and magnitude changes in the velocity vectors over one cardiac cycle.

8. Vorticity: Describes the tendency for a global vortex formation of the blood flow derived from the velocity vectors.

For comparison, the shear- and flow-related parameters were calculated for the TS contact patch areas—the bone side being the sinus wall facing the temporal bone (visible in purple in figure 1B,C), and the brain side being the sinus wall opposite to the temporal bone and facing the brain (visible in yellow in figure 1B,C). Comparisons were extended to the entire sigmoid sinus segment for comprehensive analysis.

Results

Study population

Among the TSS group, all patients presented with typical IIH symptoms (headache, PT, visual disturbances, etc). All but one patient (patient 1) had significant pressure gradient on manometry. All patients experienced symptom resolution following stenting. Table 1 details the clinical characteristics, including pre- and post-interventional flow and pressure measurements of the patients.

Table 1. Clinical patient data, including pre- and post-interventional QMRA-based flow, as well as microcatheter-based pressure measurements for the TSS group. For the control group baseline flow is indicated.

	ID	History	Pre RTrans flow (mL/min)	Post RTrans flow (mL/min)	Pre Δp (mm Hg)	Post Δp (mm Hg)
TSS group	1	IIH with PT, HA, and blurry vision	246	353	2	1
	2	IIH with PT, HA, and binocular vision loss	380	312	15	2
	3	IIH with PT, HA, and papilledema	327	344	11	1
	4	IIH with PT, HA, visual deficits, and papilledema	479	377	19	4
			RTrans flow (mL/min)
Control group	5	–	401		–	–
	6	–	789		–	–
	7	–	480		–	–
	8	–	314		–	–

FP-CT, flat panel CT; HA, headache; IIH, idiopathic intracranial hypertension; Post, post-interventional; Pre, pre-interventional; PT, pulsatile tinnitus; QMRA, quantitative MR angiography; RTrans, right transverse sinus; TSS, transverse sinus stenosis.

Influence of highly resolved FP-CT data on the trans-stenotic pressure drop

Table 2A shows the time-averaged pressure gradient across the stenosis for the two pre-interventional models (MRV and FP-CT based models) of the four TSS patients. The trans-stenotic pressure gradient was calculated between two diameters proximal and distal to the stenosis and was compared with patient-specific invasive catheter-based measurements (serving as the ground truth). Simulations based on the MRV models underestimated the pressure gradient in three out of four TSS patients, with an average deviation of 6.9 mm Hg (range −12.3 to +0.3 mm Hg). This variation is beyond what is acceptable in clinical practice. In contrast, FP-CT-based models demonstrated higher accuracy and showed a closer approximation to the in vivo measurements, with a mean overestimation of just 0.35 mm Hg (range −0.1 to +1.1 mm Hg), providing clinically relevant predictions. These differences are attributed to the higher morphological accuracy of FP-CT-derived stenosis models compared with MRV-based models (see online supplemental figure S1).

Table 2. (A) Comparison of the trans-stenotic pressure gradients for the TSS patients based on the catheter measurements as well as segmented MRV and FP-CT models, respectively. (B) Quantitative hemodynamic comparison of the bone- and brain-facing side as well as the whole sigmoid sinus area for both treatment stages for the TSS patients.

(A) Trans-stenotic pressure gradient for TSS patients
Patient ID	Δpcatheter in mm Hg	ΔpMRV in mm Hg (absolute deviation)	ΔpFP-CT in mm Hg (absolute deviation)
1	2	2.3 (+0.3)	2.5 (+0.5)
2	15	4.1 (−10.9)	14.9 (−0.1)
3	11	6.3 (−4.7)	10.9 (−0.1)
4	19	6.7 (−12.3)	20.1 (+1.1)

(B) Shear- and flow-related quantities
		TSS patients				Healthy control patients
		Bone side	Brain side	Dev from bone side	Sgm	Bone side	Brain side	Dev from bone side	Sgm
AWSSmean (Pa)	Pre	5.95	3.14	−47%	4.99	1.36	1.11	−19%	1.21
	Post	1.61	1.04	−35%	1.38
	Dev from pre	−73%			−72%
AWSSmax (Pa)	Pre	39.87	23.55	−41%	40.50	4.78	4.66	−2%	3.78
	Post	4.71	4.05	−14%	4.72
	Dev from pre	−88%			−88%
OSI	Pre	0.0051	0.0056	8%	0.0055	0.00027	0.0012	344%	0.0006
	Post	0.0017	0.0034	102%	0.0023
	Dev from pre	−67%			−58%
RRT (1/Pa)	Pre	1.79	2.81	57%	2.12	1.15	1.78	54%	1.42
	Post	1.30	1.81	40%	1.58
	Dev from pre	−28%			−25%
Velocity (m/s)	Pre	0.25	0.15	−41%	0.20	0.15	0.11	−26%	0.14
	Post	0.17	0.12	−28%	0.15
	Dev from pre	−31%			−25%
OVI	Pre	0.0042	0.0053	28%	0.0048	0.00009	0.0004	337%	0.0002
	Post	0.0006	0.0014	132%	0.0009
	Dev from pre	−86%			−81%
Vorticity (1/s)	Pre	426.3	252.6	−41%	348.9	113.4	112.4	−1%	110.8
	Post	161.8	134.4	−17%	148.6
	Dev from pre	−62%			−57%

For the control patients, only single baseline data are used, representing a healthy reference. In addition to the absolute values, relative deviations (Dev) are presented, which are shown in italics for better readability.

AWSS_max, maximum time-averaged wall shear stress; AWSS_mean, mean time-averaged wall shear stress; Dev, deviation; MRV, MR venography; OSI, oscillatory shear index; OVI, oscillatory velocity index; Post, post-interventional; Pre, pre-interventional; RRT, relative residence time; Sgm, sigmoid sinus area; TSS, transverse sinus stenosis.

Pre- and post-interventional hemodynamics along the temporal bone

Table 2B provides a quantitative overview of the shear- and flow-related parameters, especially distal to the stenotic regions. In the pre-interventional state, the cohort exhibited significant increases in AWSS_mean, AWSS_max and velocity, which were on average 43% higher on the sinus side facing the temporal bone compared with the brain-facing side. In contrast, RRT values were 57% higher on the brain-facing side. Furthermore, a reduction of the tendency of vortex formation is indicated by 41% decreased vorticity values towards the brain-facing side.

Post-intervention, reductions in hemodynamic parameters were observed on the temporal bone-facing side across all metrics, with the strongest decreases in AWSS_max (−88%) and OVI (−86%). Similar reductions were observed on the brain-facing side and within the sigmoid sinus segment. Compared with the healthy control cohort, most post-interventional metrics, except for oscillatory indices (OSI and OVI), converged toward the normal hemodynamic ranges, indicating a shift in the direction of physiological flow patterns. However, some metrics, such as AWSS_max and AWSS_mean, remained below values observed in the healthy control.

Velocity-driven flow stabilization after venous stenting

To add to the quantitative results, figure 2 illustrates case-specific qualitative findings. To identify a velocity threshold associated with potential sound transmission near the vessel wall, velocity-encoded isovolumes were generated at peak systole. Figure 2A illustrates the clear reduction in velocity after treatment. For each case, the cut-off velocity is shown, below which no considerable post-interventional flow progression occurs. Higher velocity values were predominantly observed in stenotic regions near the temporal bone.

Figure 2. Qualitative results of the occurring velocity fields and AWSS for both treatment conditions of the TSS patients as well as the control group. The upper row of both patient cohort blocks shows isovolumes of the peak systolic velocities indicated in red (A and C). These values represent a patient-specific threshold under which velocity fields are visible in the post-interventional state of the TSS patients (A). For reference also patient-specific threshold velocities are shown for the control patients (C). Furthermore, the vessel location inside the segmented skull is indicated on the right side of the models. The AWSS distribution of pre- and post-interventional treatment stages of the TSS patients (B) and of the control patients are indicated as well (D). AWSS, time-averaged wall shear stress; TSS, transverse sinus stenosis.

The average velocity threshold for the TSS patients in this study was 0.42 m/s. Control patients exhibited lower thresholds (average 0.31 m/s), showing a similar trend to post-interventional TSS cases (figure 2C). These velocity distributions are further illustrated in figure 2B, which shows notable reductions in AWSS following stenosis correction, particularly in regions distal to the stenosis near the temporal bone. Post-intervention, the AWSS gradient was neutralized, leading to a more uniform shear distribution within the sinus. Figure 2D demonstrates that post-interventional shear profiles closely resemble those of the healthy control group.

Discussion

IIH and PT are often linked to venous pathologies, particularly TSS.¹⁹ As high-resolution flow imaging such as four-dimensional flow MRI are not widely accessible due to their high cost and extended post-processing times,²⁰ treatment decisions are typically based on morphological imaging and invasive manometry to evaluate sinus stenosis associated with IIH.²¹ While recent advancements have introduced image-based flow measurement tools like QMRA or calculations based on digital subtraction angiography to aid in selecting therapy candidates, these methods have yet to provide high-resolution data on flow behaviors distal to the stenosis or offer non-invasive pressure gradient predictions across stenotic regions.^{22 23} As such, intracranial venous hemodynamics, especially in complex pathologies such as TSS, remain poorly understood.

This study combined image-based blood flow simulations in TSS patients with spatial relations at the sound-transmitting temporal bone, potentially causing PT. The large amount of multimodal image data, such as MRV and FP-CT, flow quantifications based on QMRA, and invasive pressure measurements, enabled patient-specific modeling that exceeded state-of-the-art standards. This is especially important in venous modeling, as patient-specific variability in venous flow renders generic boundary conditions unreliable.23,25 This was further demonstrated recently by Fillingham et al where MR-based flow measurements were applied for a single TSS patient to validate patient-specific pressure gradients along different venous areas.²⁶ Our proposed study integrated patient-specific blood flow simulations with spatial analyses of the sound-transmitting temporal bone to investigate the pathophysiology of TSS and its relation to PT for multiple cases.

In the proposed study, flow measurements based on QMRA were available for all TS vessel inlets, both pre- and post-interventional. This allowed for realistic, non-uniform inflow conditions in the simulations. Furthermore, the multimodal approach substantially improved the accuracy of morphological segmentations. FP-CT data enhanced the stenosis geometry previously derived from MRV, addressing the lower resolution of MRV, which tends to underestimate stenosis severity and trans-stenosis pressure gradients (table 2A and online supplemental figure S1). FP-CT-based models achieved clinically acceptable pressure gradient predictions, with a small mean error of 0.35 mm Hg, highlighting FP-CT’s superiority in constructing reliable models for assessing venous outflow obstruction (table 2A). This distinction is crucial as pressure gradients of 5–8 mm Hg are often used to determine the necessity of the stenting, and misestimation due to suboptimal imaging modalities could lead to inappropriate treatment decisions, including unnecessary or missed stenting.²⁷

Another key finding of this study is the ability to map hemodynamic patterns at the sound-transmitting structures adjacent to the temporal bone. FP-CT facilitated the direct quantification of flow dynamics in vascular regions proximal to the inner ear, revealing critical differences in shear stress rates between the brain-facing and bone-facing sides distal to the stenosis regions. Notably, the bone-facing side exhibited considerably more complex hemodynamics pre-intervention, characterized by elevated shear peaks that likely contribute to sound transmission and PT symptoms. Post-stenting simulations demonstrated a substantial reduction in these shear rates, particularly on the bone-facing side, leading to a decrease in PT-related symptoms. These findings provide strong evidence supporting the use of stenting to alleviate pathological hemodynamics in patients with TSS. Previous studies have primarily focused on the hemodynamics at the stenotic region itself, with limited exploration of distal flow patterns or spatial relationships with the temporal bone.^{13 28} Pereira et al identified complex flow patterns near the inner ear but lacked quantification and broader patient applicability.²⁹ This study overcomes these limitations by using patient-specific inlet boundary conditions and validating simulated trans-stenotic pressure gradients against invasive measurements, thus offering a robust and clinically relevant framework for assessing TSS.

This study establishes that patient-specific flow simulations can accurately predict trans-stenotic pressure gradients and quantify complex hemodynamics that contribute to IIH and PT. The findings demonstrate that FP-CT-based modeling is a superior tool for capturing the severity of stenosis and its hemodynamic impact, particularly in determining candidacy for stenting. By reducing the reliance on invasive diagnostic methods, these simulations provide a non-invasive and precise means to assess whether the pressure gradient justifies intervention, paving the way for improved management of TSS-related conditions.

Limitations

This study is subject to several limitations due to its interdisciplinary nature. The relatively small sample size is a key limitation; however, given the rarity of patients with IIH and PT associated with TSS, the inclusion of multiple imaging modalities and flow measurements required careful patient selection. As a result, some initial cases were excluded to ensure that the simulations were based on robust, patient-specific data. Additionally, few simplifications were made in the blood flow simulations. For example, vessel walls were assumed to be rigid, which is a common assumption in intracranial flow modeling. Incorporating wall properties, particularly in regions distal to the stenosis, would allow for fluid–structure interactions, but image data lacked sufficient detail on wall characteristics as no histological images were available. Simplified assumptions, such as uniform wall thickness, may introduce additional uncertainties into the results.³⁰ Furthermore, this study used state-of-the-art simulation techniques with a medium temporal resolution of 0.001 s. Although high-fidelity approaches, such as direct numerical simulations, could improve predictive accuracy—particularly in resolving high-frequency flow instabilities—they would require significantly higher computational resources.

Conclusions

This interdisciplinary study incorporates multimodal imaging data and patient-specific flow boundary conditions to investigate the hemodynamics of IIH patients with PT and TSS. The simulation results accurately predicted the pressure gradient across the stenosis, with FP-CT-based models outperforming MRV-based simulations in terms of accuracy. Additionally, the study identified the steep AWSS at the contact surface to the temporal bone as the primary driving force for pulse-synchronous sound transmission, a key factor in PT development. Based on these findings, this study advocates for the inclusion of surrounding anatomical structures in future simulations to identify potential biomarkers for PT, offering a non-invasive approach to understanding and diagnosing this condition without additional patient risk.

Data availability statement

Data are available upon reasonable request.