Predicting the growth of brain aneurysms using advanced imaging techniques
Official title: Statistical tensor regression models for intracranial aneurysm growth prediction
Funded at University of California, San Francisco · NIH-11101258 · Data updated 2026-11-12
This study is looking at how some brain aneurysms that don’t show any symptoms might change over time, using special MRI technology to help predict which ones could grow or burst in the next five years, so doctors can make better treatment choices for patients.
Quick facts
| Grant type | R01 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of California, San Francisco NIH-funded |
| Lab location | 1 site (San Francisco, United States) |
| Project start | June 15, 2024 |
| Project end | May 31, 2029 |
| Funding year | 2025 |
| Award (this year) | $529,664 |
| NIH project number | 5R01NS132766-02 |
| Project ID | NIH-11101258 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research are individuals diagnosed with unruptured intracranial aneurysms who are being monitored for growth.
✕Less likely to help
Patients with ruptured aneurysms or those without any diagnosed aneurysms may not benefit from this research.
What this research studies
This research focuses on understanding how certain brain aneurysms, which are often asymptomatic, may grow or rupture over time. By utilizing advanced 4D Flow MRI technology, the study aims to develop a statistical framework that can accurately predict the growth of unruptured intracranial aneurysms (UIAs) over a five-year period. The approach involves analyzing hemodynamic features and using machine learning techniques to enhance imaging resolution and predict growth trajectories. This could help in assessing the risk of aneurysm rupture and guide treatment decisions.
Why it matters
Potential benefit: If successful, this research could lead to better prediction of aneurysm growth, allowing for more personalized treatment plans and potentially reducing the risk of rupture.
How similar studies have gone: Previous research has shown promise in using advanced imaging techniques for aneurysm assessment, but this specific approach using tensor regression models is novel.
Where this research is happening
-
University of California, San Francisco
San Francisco, United States Active
Researchers
- Principal investigator: Jiang, Fei — University of California, San Francisco
- Study coordinator: Jiang, Fei
Related research
Other NIH-funded projects on the same topics.
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Understanding how brain aneurysm walls and shape change over time
This project studies how small brain aneurysms grow and whether MRI features of the aneurysm wall and its shape can predict growth so doctors can better decide when treatment is needed.
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Predicting brain aneurysm growth with advanced 4D MRI and tensor math
A research project uses 4D flow MRI and new math models to predict how brain aneurysms grow over time, helping doctors decide on safer treatments for people with UIAs.
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How aging and aged (senescent) cells may cause brain aneurysm rupture
This research tests whether removing aging 'senescent' cells can lower the chance that a brain aneurysm will burst for people with intracranial aneurysms.
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Reprogramming blood-vessel lining cells in inherited brain aneurysms
This project aims to find out whether fixing gene-related changes in the cells that line brain arteries can lower aneurysm risk in people with a family history.
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How tiny cell structures (podosomes) may weaken brain arteries and cause aneurysms
Testing whether podosomes — small structures inside blood vessel cells — make brain arteries weaker and raise the risk of aneurysm rupture for people with or at risk of brain aneurysms.
-
How cells' cleanup of vessel attachments may affect brain aneurysms
This project looks at whether a cellular cleanup process in blood‑vessel lining cells weakens brain arteries and contributes to aneurysms in people at risk for intracranial aneurysm.
About this research
- This is an active NIH-funded research project — typically early-stage science, not a clinical trial accepting patient enrollment.
- Some NIH-funded labs run parallel clinical studies or seek volunteers for related work. To check, contact the principal investigator or institution listed above.
- For full project details, budget, and progress reports, visit the official NIH RePORTER page.