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Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity

Pepe Eulzer, Kevin Richter, Anna Hundertmark, Ralf Wickenhöfer, Carsten M. Klingner, Kai Lawonn

TL;DR

The study addresses the gap between resource-intensive CFD and time-sensitive clinical decision-making by introducing a similarity-based visual analytics framework that retrieves and visualizes instantaneous flow predictions from a database of precomputed carotid bifurcation simulations. By extracting CTA-derived geometries, aligning them in a common space, and combining three geometry-aware similarity metrics into a single score, the system identifies representative flow models that approximate patient-specific hemodynamics, enabling rapid exploration of peak velocity and other flow parameters. Numerical evaluation shows peak-velocity prediction errors within interobserver variability, and physician interviews confirm high usability and potential for clinical deployment, suggesting substantial practical impact for stroke diagnostics and treatment planning. The approach offers a scalable path to integrate CFD insights into routine practice and could extend to intracranial vessels or aneurysms, where static imaging often precludes direct flow assessment.

Abstract

The emergence of computational fluid dynamics (CFD) enabled the simulation of intricate transport processes, including flow in physiological structures, such as blood vessels. While these so-called hemodynamic simulations offer groundbreaking opportunities to solve problems at the clinical forefront, a successful translation of CFD to clinical decision-making is challenging. Hemodynamic simulations are intrinsically complex, time-consuming, and resource-intensive, which conflicts with the time-sensitive nature of clinical workflows and the fact that hospitals usually do not have the necessary resources or infrastructure to support CFD simulations. To address these transfer challenges, we propose a novel visualization system which enables instant flow exploration without performing on-site simulation. To gain insights into the viability of the approach, we focus on hemodynamic simulations of the carotid bifurcation, which is a highly relevant arterial subtree in stroke diagnostics and prevention. We created an initial database of 120 high-resolution carotid bifurcation flow models and developed a set of similarity metrics used to place a new carotid surface model into a neighborhood of simulated cases with the highest geometric similarity. The neighborhood can be immediately explored and the flow fields analyzed. We found that if the artery models are similar enough in the regions of interest, a new simulation leads to coinciding results, allowing the user to circumvent individual flow simulations. We conclude that similarity-based visual analysis is a promising approach toward the usability of CFD in medical practice.

Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity

TL;DR

The study addresses the gap between resource-intensive CFD and time-sensitive clinical decision-making by introducing a similarity-based visual analytics framework that retrieves and visualizes instantaneous flow predictions from a database of precomputed carotid bifurcation simulations. By extracting CTA-derived geometries, aligning them in a common space, and combining three geometry-aware similarity metrics into a single score, the system identifies representative flow models that approximate patient-specific hemodynamics, enabling rapid exploration of peak velocity and other flow parameters. Numerical evaluation shows peak-velocity prediction errors within interobserver variability, and physician interviews confirm high usability and potential for clinical deployment, suggesting substantial practical impact for stroke diagnostics and treatment planning. The approach offers a scalable path to integrate CFD insights into routine practice and could extend to intracranial vessels or aneurysms, where static imaging often precludes direct flow assessment.

Abstract

The emergence of computational fluid dynamics (CFD) enabled the simulation of intricate transport processes, including flow in physiological structures, such as blood vessels. While these so-called hemodynamic simulations offer groundbreaking opportunities to solve problems at the clinical forefront, a successful translation of CFD to clinical decision-making is challenging. Hemodynamic simulations are intrinsically complex, time-consuming, and resource-intensive, which conflicts with the time-sensitive nature of clinical workflows and the fact that hospitals usually do not have the necessary resources or infrastructure to support CFD simulations. To address these transfer challenges, we propose a novel visualization system which enables instant flow exploration without performing on-site simulation. To gain insights into the viability of the approach, we focus on hemodynamic simulations of the carotid bifurcation, which is a highly relevant arterial subtree in stroke diagnostics and prevention. We created an initial database of 120 high-resolution carotid bifurcation flow models and developed a set of similarity metrics used to place a new carotid surface model into a neighborhood of simulated cases with the highest geometric similarity. The neighborhood can be immediately explored and the flow fields analyzed. We found that if the artery models are similar enough in the regions of interest, a new simulation leads to coinciding results, allowing the user to circumvent individual flow simulations. We conclude that similarity-based visual analysis is a promising approach toward the usability of CFD in medical practice.
Paper Structure (17 sections, 4 figures)

This paper contains 17 sections, 4 figures.

Table of Contents

  1. Introduction
  2. Medical Background
  3. Related Work
  4. Task Analysis
  5. Methods
  6. Creating a Database of Carotid Flow Models
  7. Extracting Carotid Bifurcation Geometries
  8. Visualizing the Alignment Space Neighborhood
  9. Comparative Visualization of Multiple Flow Models
  10. Evaluation
  11. Numerical Evaluation
  12. Structured Interviews
  13. Results and Discussion
  14. Interview Results
  15. Limitations
  16. ...and 2 more sections

Figures (4)

  • Figure 1: Carotid bifurcation anatomy.
  • Figure 2: The user interface of the visualization framework. (a) Toolbar with parameter selection and color map. (b) 3D view of the vessel surface geometry. (c) 3D views of the selected cases from the flow database. (d) Panel to control which similarity metrics are queried. (e) Bar charts showing how similar a match is regarding each metric. (f) Vessel surface maps giving an overview of the flow parameters.
  • Figure 3: A probe lens can be interactively dragged over the vessel to focus on the flow in a specific region. Flow properties, such as the maximal velocity within the selection, are extracted automatically.
  • Figure 4: Boxplots of the error between the ground truth simulation and prediction. H: The prediction was selected based on the Hausdorff distance. $\bar{S}_{gds}$: The prediction was selected based on our combined similarity metric.