Using machine learning to improve brain artery imaging
Official title: Automated Machine Learning-Based Brain Artery Segmentation, Anatomical Prior Labeling, and Feature Extraction on MR Angiography
Funded at University of Rochester · NIH-10759721 · Data updated 2026-11-12
This study is looking to make it easier and faster to check the health of brain arteries using advanced computer technology, which could help doctors better understand and diagnose conditions related to small blood vessels in the brain and how they affect thinking skills.
Quick facts
| Grant type | R03 grant |
|---|---|
| Study type | NIH-funded research |
| Funding institution | University of Rochester NIH-funded |
| Lab location | 1 site (Rochester, United States) |
| Project start | September 8, 2023 |
| Project end | August 31, 2025 |
| Funding year | 2023 |
| Award (this year) | $154,000 |
| NIH project number | 1R03NS134395-01 |
| Project ID | NIH-10759721 on NIH RePORTER |
Who could benefit from this research
✓Could help
Ideal candidates for this research include individuals diagnosed with cerebrovascular diseases or those at risk, particularly those with cognitive impairments related to small vessel disease.
✕Less likely to help
Patients without cerebrovascular conditions or those not undergoing imaging studies may not benefit from this research.
What this research studies
This research focuses on enhancing the evaluation of brain arteries using advanced machine learning techniques applied to magnetic resonance angiography (MRA). It aims to automate the segmentation of cerebral vessels, which is currently a time-consuming manual process, thereby improving the speed and accuracy of diagnosing cerebrovascular diseases. The study will utilize existing databases to develop a framework that requires minimal training data while extracting important features from the images. Additionally, it will explore the connections between these vessel features and cognitive performance in patients with cerebral small vessel disease.
Why it matters
Potential benefit: If successful, this research could lead to faster and more accurate diagnoses of cerebrovascular diseases, ultimately improving patient outcomes.
How similar studies have gone: While there have been some machine learning approaches in medical imaging, this specific application of automated segmentation in brain artery imaging is relatively novel.
Where this research is happening
-
University of Rochester
Rochester, United States Active
Researchers
- Principal investigator: Uddin, Md Nasir — University of Rochester
- Study coordinator: Uddin, Md Nasir
Related research
Other NIH-funded projects on the same topics.
-
Rapid automated breast lump diagnosis without a radiologist or sonographer
A new AI-powered ultrasound tool helps determine if a palpable breast lump is likely benign or malignant, using a simplified technique that can be used by people with minimal ultrasound training.
-
ML-ROVER: Reducing Unneeded Lab Tests in Kids with AI
Aims to use machine learning to cut unnecessary blood tests in children in intensive care, helping avoid anemia and improve care quality.
-
Aggressive fever-reducing therapy to protect the brains of children with CNS malaria
A study planned for children with CNS malaria will test whether aggressive fever-reducing treatment can lower death and brain-related problems.
-
Aggressive fever-reducing therapy to protect brains in children with CNS malaria
A plan to test whether giving strong fever-reducing medications to children with central nervous system malaria can lower death and brain injury.
-
Aggressive fever-fighting treatment to protect the brain in kids with CNS malaria
A plan to test whether giving strong fever-reducing treatment alongside standard care can lower death and brain injury in children with CNS malaria.
-
Aggressive fever-reducing treatment to protect brains in kids with CNS malaria
A plan to test whether giving strong fever-reducing medicine alongside standard malaria treatment can lower deaths and brain injuries in children with CNS malaria.
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.