Updates in Cerebrovascular Imaging.
Authors
Affiliations (10)
Affiliations (10)
- Department of Neurology, Medical University of South Carolina, Charleston, South Carolina, United States.
- Department of Neurology, Wellstar MCG Health, Augusta University, Augusta, Georgia, United States.
- Department of Radiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, United States.
- Department of Neurosurgery, Mallinckrodt Institute of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, United States.
- Department of Neurology, Mallinckrodt Institute of Radiology, Washington University School of Medicine in Saint Louis, St Louis, Missouri, United States.
- Department of Radiology, Boston Medical Center, Boston, Massachusetts, United States.
- Department of Radiology, NYU Langone Health Center, New York, NY, United States.
- Department of Neurosurgery, NYU Langone Health Center, New York, NY, United States.
- Department of Neurology, Boston Medical Center, Boston, Massachusetts, United States.
- Department of Neurosurgery, Medical University of South Carolina, Charleston, South Carolina, United States.
Abstract
Cerebrovascular imaging has undergone significant advances, enhancing the diagnosis and management of cerebrovascular diseases such as stroke, aneurysms, and arteriovenous malformations. This chapter explores key imaging modalities, including non-contrast computed tomography, computed tomography angiography, magnetic resonance imaging (MRI), and digital subtraction angiography. Innovations such as high-resolution vessel wall imaging, artificial intelligence (AI)-driven stroke detection, and advanced perfusion imaging have improved diagnostic accuracy and treatment selection. Additionally, novel techniques like 7-T MRI, molecular imaging, and functional ultrasound provide deeper insights into vascular pathology. AI and machine learning applications are revolutionizing automated detection and prognostication, expediting treatment decisions. Challenges remain in standardization, radiation exposure, and accessibility. However, continued technological advances, multimodal imaging integration, and AI-driven automation promise a future of precise, non-invasive cerebrovascular diagnostics, ultimately improving patient outcomes.