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Communicating complex blood flow patterns generated from computational fluid dynamics (CFD) simulations to clinical audiences for the purposes of risk assessment or treatment planning is an ongoing challenge. While attempts have been made to develop new software tools for such clinical visualization of CFD data, these often overlook established medical imaging/visualization practice and data infrastructures. Here, leveraging the clinical ubiquity of the DICOM file format, we present techniques for the translation of CFD data to DICOM series, facilitating interactive visualization in standard radiological software.Unstructured CFD data (volumetric fields of velocity magnitude, Q-criterion, and pathlines) are resampled to structured grids. Novel raster-based techniques that simulate experimental optical blurring are presented for bringing simulated pathlines into structured image volumes. DICOM series are created by strategically encoding these data into the file's PixelArray tag. Lumen surface information is also strategically encoded into a different range of pixel intensities, allowing hemodynamics and morphology to be co-visualized in a single volume using opacity-based rendering transfer functions.We show that 3D temporal CFD data represented as structured DICOM series can be rendered interactively in Horos, a widely-used medical imaging/radiology software. Our transfer function-based approach allows for representations of scalar isosurfaces, volumetric rendering, and tubular pathlines to be modified in real-time, resembling conventional unstructured visualizations. Careful selection of voxelization ROIs helps to ensure that data are kept lightweight for real-time rendering and minimal storage.While our approach inherently sacrifices some of the advanced visualization capabilities of specialized software tools, we believe our closer consideration of standardization can help to facilitate meaningful clinical interaction. This work opens up possibilities for the complete integration of measured and simulated data in established radiological software environments and workflows from PACS storage to 3D/4D visualization.

This paper is associated with a poster winner of a 2020 American Physical Society's Division of Fluid Dynamics (DFD) Milton van Dyke Award for work presented at the DFD Gallery of Fluid Motion. The original poster is available online at the Gallery of Fluid Motion, https://doi.org/10.1103/APS.DFD.2020.GFM.P0004.

Recent studies using high-fidelity computational fluid dynamics (CFD) have revealed high-frequency flow instabilities consistent with clinical reports of bruits and “musical murmurs”, which have been speculated to contribute to aneurysm growth and rupture. We hypothesized that harmonic flow instabilities (“spectral bandedness”) in aneurysm CFD data may be associated with rupture status. Before testing this hypothesis, we first present a novel method for quantifying and visualizing spectral bandedness in cardiovascular CFD datasets based on musical audio-processing tools. Motivated by previous studies of aneurysm hemodynamics, we also computed a selection of existing metrics that have demonstrated association with rupture in large studies. In a dataset of 50 bifurcation aneurysm geometries modeled using high-fidelity CFD, our spectral bandedness index (SBI) was the only metric significantly associated with rupture status (AUC = 0.76, p = 0.002), with a specificity of 79% (correctly predicting 19/24 unruptured cases) and sensitivity of 65% (correctly predicting 17/26 ruptured cases). Three-dimensional flow visualizations revealed coherent regions of high SBI to be associated with strong near-wall inflow jets and vortex-shedding/flutter phenomena in the aneurysm sac. We speculate that these intracycle, coherent flow instabilities may preferentially contribute to the progressive degradation of the aneurysm wall through flow-induced vibrational mechanisms, and that their absence in high-fidelity CFD may be useful for identifying intracranial aneurysms at lower risk of rupture.

Everyday perception and action are fundamentally multisensory. Despite this, the sole reliance on visualization for the representation of complex 3D spatiotemporal data is still widespread. In the past we have proposed various prototypes for the sonification of dense data from computational fluid dynamics (CFD) simulations of turbulent-like blood flow, but did not robustly consider the perception and associated meaning-making of the resultant sounds. To reduce some of the complexities of these data for sonification, in this work we present a feature-based approach, applying ideas from auditory scene analysis to sonify different data features along perceptually-separable auditory streams. As there are many possible features in these dense data, we followed the analogy of “caricature” to guide our definition and subsequent amplification of unique spectral and fluctuating features, while effectively minimizing the features common between simulations. This approach may allow for better insight into the behavior of flow instabilities when compared to our previous sonifications and/or visualizations, and additionally we observed benefits when some redundancy was maintained between modalities.