Tuesday, November 27 | 12:15 p.m.-12:45 p.m. | NR397-SD-TUA7 | Lakeside, NR Community, Station 7
Researchers from the Netherlands have developed an artificial intelligence (AI) algorithm capable of segmenting brain vasculature on 4D CT angiography (CTA) scans, according to this Tuesday poster presentation.Segmenting the entire brain vasculature is an essential part of evaluating the brain with 4D CTA, presenter Midas Meijs, a doctoral candidate at Radboud University Medical Center in Nijmegen, told AuntMinnie.com.
Seeking to automate this task, Meijs and colleagues trained and tested a convolutional neural network, U-Net, on the 4D CTA scans of 162 patients suspected of having had a stroke. U-Net took into account both temporal and spatial features from the 4D imaging data.
The automated deep-learning algorithm was able to segment the complete brain vasculature on 4D CTA scans with high accuracy, regardless of the size of individual blood vessels, the group found. Furthermore, the algorithm processed the full 4D CTA dataset in less than 90 seconds.
Using an AI algorithm to assist in the segmentation of cerebral vasculature may improve visualization of the brain and ultimately help clinicians assess brain blood flow and detect potential pathology, Meijs said.
"Automated segmentation in 4D CTA is an important step toward the automated localization and evaluation of vascular pathology," he said.
![Axial images from unenhanced calcium score cardiac CT (left) and curved planar reformation images from CT angiography (right) show that higher long-term exposure to air pollution is associated with greater coronary artery calcium and more obstructive coronary artery disease (CAD). Top row: Images in a 68-year-old male patient with higher 10-year mean ambient air pollution exposure (7.9 μg/m3 for particulate matter measuring ≤2.5 μm in diameter [PM2.5] and 17.4 parts per billion [ppb] for NO2) with extensive CAD (coronary artery calcium score [CACS] >1,000 and obstructive CAD [≥70% diameter stenosis]). Bottom row: Images in a 57-year-old female patient with lower 10-year mean ambient air pollution exposure (6.3 μg/m3 for PM2.5 and 4.6 ppb for NO2) with no CAD (CACS = 0 and no obstructive stenosis).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/06/hanneman.r6SMLzkezo.png?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)
