Phase-Contrast Magnetic Resonance Imaging (PC-MRI) surpasses all other imaging methods in quality and completeness for measuring time-varying volumetric blood flows and has shown potential to improve both diagnosis and risk assessment of cardiovascular diseases. However, like any measurement of physical phenomena, the data are prone to noise, artefacts and has a limited resolution. Therefore, PC-MRI data itself do not fulfil physics fluid laws making it difficult to distinguish important flow features. For data analysis, physically plausible and high-resolution data are required. Computational fluid dynamics provides high-resolution physically plausible flows. However, the flow is inherently coupled to the underlying anatomy and boundary conditions, which are difficult or sometimes even impossible to adequately model with current techniques. We present a novel methodology using data assimilation techniques for PC-MRI noise and artefact removal, generating physically plausible flow close to the measured data. It also allows us to increase the spatial and temporal resolution. To avoid sensitivity to the anatomical model, we consider and update the full 3D velocity field. We demonstrate our approach using phantom data with various amounts of induced noise and show that we can improve the data while preserving important flow features, without the need of a highly detailed model of the anatomy.

@en

Phase-Contrast Magnetic Resonance Imaging (PC-MRI) measures volumetric and time-varying blood flow data, unsurpassed in quality and completeness. Such blood-flow data have been shown to have the potential to improve both diagnosis and risk assessment of cardiovascular diseases (CVDs) uniquely. Typically PC-MRI data is visualized using stream- or pathlines. However, time-varying aspects of the data, e.g., vortex shedding, breakdown, and formation, are not sufficiently captured by these visualization techniques. Experimental flow visualization techniques introduce a visible medium, like smoke or dye, to visualize flow aspects including time-varying aspects. We propose a framework that mimics such experimental techniques by using a high number of particles. The framework offers great flexibility which allows for various visualization approaches. These include common traditional flow visualizations, but also streak visualizations to show the temporal aspects, and uncertainty visualizations. Moreover, these patient-specific measurements suffer from noise artifacts and a coarse resolution, causing uncertainty. Traditional flow visualizations neglect uncertainty and, therefore, may give a false sense of certainty, which can mislead the user yielding incorrect decisions. Previously, the domain experts had no means to visualize the effect of the uncertainty in the data. Our framework has been adopted by domain experts to visualize the vortices present in the sinuses of the aorta root showing the potential of the framework. Furthermore, an evaluation among domain experts indicated that having the option to visualize the uncertainty contributed to their confidence on the analysis. @en

Cardiac flow is still not fully understood, and is currently an active research topic. Using phase-contrast magnetic resonance imaging (PC-MRI) blood flow can be measured. For the inspection of such flow, researchers often rely on methods that require additional scans produced by different imaging modalities to provide context. This requires labor-intensive registration and often manual segmentation before any exploration of the data is performed. This work provides a framework that allows for a quick exploration of cardiac flow without the need of additional imaging and time-consuming segmentation. To achieve this, only the 4D data from one PC-MRI scan is used. A context visualization is derived automatically from the data, and provides context for the flow. Instead of relying on segmentation to deliver an accurate context, the heart’s ventricles are approximated by half-ellipsoids that can be placed with minimal user interaction. Furthermore, seeding positions for flow visualization can be placed automatically in areas of interest defined by the user and based on derived flow features. The framework enables a user to do a fast initial exploration of cardiac flow, as is demonstrated by a use case and a user study involving cardiac blood flow researchers.@en

Magnetic Resonance Imaging (MRI) enables volumetric and time-varying measurements of blood-flow data. Such data have shown potential to improve diagnosis and risk assessment of various cardiovascular diseases. Hereby, a unique way of analysing patient-specific haemodynamics becomes possible. However, these measurements are susceptible to artifacts, noise and a coarse spatio-temporal resolution. Furthermore, typical flow visualization techniques rely on interpolation. For example, using pathlines requires a high quality temporal resolution. While numerical simulations, based on mathematical flow models, address some of these limitations, the involved modelling assumptions (e.g., regarding the inflow and mesh) do not provide patientspecific data to the degree actual measurements would. To overcome this issue, data assimilation techniques can be applied to use measured data in order to steer a physically-based simulation of the flow, combining the benefits of measured data and simulation. Our work builds upon such an existing solution to increase the temporal resolution of the measured data, but achieves significantly higher fidelity. We avoid the previous damping and interpolation bias towards one of the measurements, by simulating bidrectionally (forwards and backwards through time) and using sources and sinks. Our method is evaluated and compared to the, currently-used, conventional interpolation scheme and forward-only simulation using measured and analytical flow data. It reduces artifacts, noise, and interpolation error, while being closer to laminar flow, as is expected for flow in vessels.@en

Modern MRI measurements deliver volumetric and time-varying blood-flow data of unprecedented quality. Visual analysis of these data potentially leads to a better diagnosis and risk assessment of various cardiovascular diseases. Recent advances have improved the speed and quality of the imaging data considerably. Nevertheless, the data remains compromised by noise and a lack of spatiotemporal resolution. Besides imaging data, also numerical simulations are employed. These are based on mathematical models of specific features of physical reality. However, these models require realistic parameters and boundary conditions based on measurements. We propose to use data assimilation to bring measured data and physically-based simulation together, and to harness the mutual benefits. The accuracy and noise robustness of the coupled approach is validated using an analytic flow field. Furthermore, we present a comparative visualization that conveys the differences between using conventional interpolation and our coupled approach.@en