MRI-Based Method Enhances Brain Aneurysm Flow Estimation
New MRI-based method simulates blood flow in brain aneurysms using less data with high accuracy.
Complete the form below to unlock access to ALL audio articles.
Researchers at the Institute of Science Tokyo have developed a computational method to simulate blood flow in brain aneurysms using four-dimensional (4D) flow magnetic resonance imaging (MRI), computational fluid dynamics (CFD) and data assimilation. Unlike existing models that require data from entire arterial networks, the new approach focuses exclusively on the aneurysm site, improving efficiency without compromising accuracy.
Computational fluid dynamics (CFD)
A numerical method that models how fluids like blood flow through different environments, including arteries and aneurysms.
Brain aneurysms – localized bulges in weakened blood vessel walls – pose serious health risks if ruptured. Understanding how blood flows through these structures is critical for assessing patient-specific rupture risk. However, current simulation techniques often demand high-resolution imaging and extensive vessel mapping, which can be computationally expensive and clinically impractical.
Combining 4D flow MRI with CFD for brain aneurysm modeling
Many simulation methods use 4D flow MRI, a technique that captures time-resolved, three-dimensional blood velocity, in combination with CFD models. While these methods offer valuable insights, their effectiveness is limited by a reliance on full-vessel imaging and generic assumptions about boundary conditions.
To overcome these limitations, the new method integrates variational data assimilation – a mathematical framework that merges observational data with numerical modeling – to reconstruct flow patterns inside brain aneurysms with greater specificity. The approach requires only localized velocity data near the aneurysm neck, which can be derived from standard 4D flow MRI.
Variational data assimilation
A technique that integrates measured data into computational models to improve the accuracy of simulations, often used in medicine and meteorology.Using model order reduction to simplify blood flow simulation
The researchers implemented a Fourier series-based model order reduction strategy, which condenses the temporal dynamics of blood flow into a simplified representation. This significantly reduced the computational load while preserving the critical features of the flow field.
Fourier series-based model order reduction
A mathematical approach that simplifies time-dependent simulations by representing flow patterns with a reduced number of frequency components.By narrowing the modeling scope to just the aneurysm region, the method sidesteps the challenge of defining noisy or uncertain boundary conditions along entire vessel branches. This makes the tool particularly well-suited for use in a clinical setting, where computational resources and patient-specific data may be limited.
Clinical validation shows improved accuracy in flow estimation
To validate the method, the team tested it against both synthetic datasets and real patient data. In simulations using synthetic flow profiles, the average velocity mismatch was limited to between 4% and 7%. When applied to MRI data from three patients, the model demonstrated a 37% – 44% reduction in velocity estimation errors compared to conventional 4D flow MRI and CFD approaches.
The model also captured detailed hemodynamic parameters – including wall shear stress and intraluminal pressure – that are critical for understanding aneurysm behavior. These improvements were achieved without the need for high-resolution data from the entire vascular system.
Wall shear stress
A force caused by fluid friction acting along vessel walls, which can influence the development or rupture of aneurysms.
AI-Driven Therapeutic Antibody Design
This application note explores how researchers successfully combined AI with advanced multiplexed gene synthesis to design and validate novel single-domain antibodies with atomic-level precision.View App Note / Case Study
A focused method for brain aneurysm risk assessment
The research offers a promising step toward more practical, patient-specific simulations for use in clinical neurology. By targeting only the aneurysm region, the model delivers accurate estimations of blood flow while reducing the need for extensive imaging or computing power.
This efficiency may support early-stage clinical decision-making by enabling more precise evaluations of aneurysm growth potential or rupture risk using commonly available imaging data.
Reference: Ichimura T, Yamada S, Watanabe Y, Kawano H, Ii S. A practical strategy for data assimilation of cerebral intra-aneurysmal flows using a variational method with boundary control of velocity. Comput Methods Programs Biomed. 2025;268:108861. doi: 10.1016/j.cmpb.2025.108861
This article has been republished from the following materials. Note: material may have been edited for length and content. For further information, please contact the cited source. Our press release publishing policy can be accessed here.
This content includes text that has been generated with the assistance of AI. Technology Networks' AI policy can be found here.
