AIMC Topic: Cerebral Angiography
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Predictive modeling and machine learning show poor performance of clinical, morphological, and hemodynamic parameters for small intracranial aneurysm rupture.
Scientific reports
Jul 5, 2025
Small intracranial aneurysms (SIAs) (< 5 mm) are increasingly detected due to advanced imaging, but predicting rupture risk remains challenging. Rupture, though rare, can cause devastating subarachnoid hemorrhage. This study analyzed 141 SIAs (101 un...
The Role of AI-driven Volumetric Aneurysm Analysis in the Management of Cerebral Aneurysms.
Neuroimaging clinics of North America
Jun 11, 2025
This article looks at the current state of aneurysm risk modeling, exploring the limitations of linear measurement. It reviews articles using Food and Drug Administration (FDA)-approved artificial intelligence-driven volumetric measurement tools both...
Application and optimization of the U-Net++ model for cerebral artery segmentation based on computed tomographic angiography images.
European journal of radiology
Apr 27, 2025
Accurate segmentation of cerebral arteries on computed tomography angiography (CTA) images is essential for the diagnosis and management of cerebrovascular diseases, including ischemic stroke. This study implemented a deep learning-based U-Net++ mode...
perfDSA: Automatic Perfusion Imaging in Cerebral Digital Subtraction Angiography.
International journal of computer assisted radiology and surgery
Apr 24, 2025
PURPOSE: Cerebral digital subtraction angiography (DSA) is a standard imaging technique in image-guided interventions for visualizing cerebral blood flow and therapeutic guidance thanks to its high spatio-temporal resolution. To date, cerebral perfus...
Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.
IEEE journal of biomedical and health informatics
Apr 4, 2025
Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and computed tomography angiography (CTA) is essential in providing supportive information for diagnosing and treatment planning of multiple intracranial vascul...
Research on predicting radiographic exposure time in imaging based on neural network prediction models.
Clinical neurology and neurosurgery
Jan 20, 2025
OBJECTIVE: To explore the anatomical and clinical factors that affect the radiographic exposure time in radial artery cerebral angiography and to establish a model.
Evaluating a clinically available artificial intelligence model for intracranial aneurysm detection: a multi-reader study and algorithmic audit.
Neuroradiology
Jan 15, 2025
PURPOSE: We aimed to validate a clinically available artificial intelligence (AI) model to assist general radiologists in the detection of intracranial aneurysm (IA) in a multi-reader multi-case (MRMC) study, and to explore its performance in routine...
Diagnosis of intracranial aneurysms by computed tomography angiography using deep learning-based detection and segmentation.
Journal of neurointerventional surgery
Dec 26, 2024
BACKGROUND: Detecting and segmenting intracranial aneurysms (IAs) from angiographic images is a laborious task.
Deep Learning Based Detection of Large Vessel Occlusions in Acute Ischemic Stroke Using High-Resolution Photon Counting Computed Tomography and Conventional Multidetector Computed Tomography.
Clinical neuroradiology
Nov 25, 2024
PURPOSE: Deep learning (DL) methods for detecting large vessel occlusion (LVO) in acute ischemic stroke (AIS) show promise, but the effect of computed tomography angiography (CTA) image quality on DL performance is unclear. Our study investigates the...
Impact of deep Learning-enhanced contrast on diagnostic accuracy in stroke CT angiography.
European journal of radiology
Oct 28, 2024
PURPOSE: To examine the impact of deep learning-augmented contrast enhancement on image quality and diagnostic accuracy of poorly contrasted CT angiography in patients with suspected stroke.
Cerebral Angiography
Middle Aged
Reproducibility of Results
Radiographic Image Enhancement
Humans
Deep Learning
Radiographic Image Interpretation, Computer-Assisted
Aged
Retrospective Studies
Contrast Media
Stroke
Sensitivity and Specificity
Signal-To-Noise Ratio
Female
Male
Computed Tomography Angiography
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