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.

BACKGROUNDANDPURPOSE: Hemodynamics are thought to play an important role in the rupture of intracranial aneurysms.Wetested whether hemodynamics, determined from computational fluid dynamics models, have additional value in discriminating ruptured and unruptured aneurysms. Such discriminative power could provide better prediction models for rupture. MATERIALS AND METHODS: A cross-sectional study was performed on patients eligible for endovascular treatment, including 55 ruptured and 62 unruptured aneurysms. Association with rupture status was tested for location, aneurysm type, and 4 geometric and 10 hemodynamic parameters. Patient-specific spatiotemporal velocities measured with phase-contrast MR imaging were used as inflow conditions for computational fluid dynamics. To assess the additional value of hemodynamic parameters, we performed 1 univariate and 2 multivariate analyses: 1 traditional model including only location and geometry and 1 advanced model that included patient-specific hemodynamic parameters. RESULTS: In the univariate analysis, high-risk locations (anterior cerebral arteries, posterior communicating artery, and posterior circulation), daughter sacs, unstable inflow jets, impingements at the aneurysm body, and unstable complex flow patterns were significantly present more often in ruptured aneurysms. In both multivariate analyses, only the high-risk location (OR, 3.92; 95% CI, 1.77-8.68) and the presence of daughter sacs (OR, 2.79; 95% CI, 1.25-6.25) remained as significant independent determinants. CONCLUSIONS: In this study population of patients eligible for endovascular treatment, we found no independent additional value of aneurysmal hemodynamics in discriminating rupture status, despite high univariate associations. Only traditional parameters (high-risk location and the presence of daughter sacs) were independently associated with ruptured aneurysms.