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Xianli Lv

Beijing Tsinghua Changgung Hospital

Dr. Xianli Lv, MD, is an associate professor of endovascular neurosurgery, at the Department of Neurosurgery, Beijing Tsinghua Changgung Hospital, School of Clinical Medicine, Tsinghua University, China. He is selected for the “2023 Career-long and yearly Top 2% Global Scientists List” released by Stanford University in the United States. His research focuses on neuroendovascular therapy of intracranial aneurysm, cerebral arteriovenous malformation, intracranial dural arteriovenous fistula, spinal vascular malformation, and pediatric cerebrospinal vascular malformation. He has one national invention patent and one utility model patent to his credit and has authored 195 peer-reviewed scientific articles and edited 10 books. His h-index is 31 in 2024(Scopus). He is an editorial member of Interventional Neuroradiology, Stroke and Vascular Neurology, Journal of Neuroradiology, The Neuroradiology Journal, and World Journal of Radiology. He is also a deputy editor of Neuroscience Informatics and Frontiers in Neurology. Dr. Lv is a member of the World Stroke Organization (WSO) and the World Federation of Interventional Therapeutic Neuroradiology (WFITN). He was featured on the cover of World Journal of Radiology in April 2022 and June 2024, and journal of China Science and Technology Achievements in August 2023.

Xianli Lv

5books edited

6chapters authored

Latest work with IntechOpen by Xianli Lv

Neuroimaging has developed rapidly, such as ultrasound, CT scanning, MRI, functional MRI, 7T MRI, and digital subtraction angiography, providing high-resolution acquisition and better contrast, making it easier to detect lesions and structural changes in brain diseases. Targeted diseases in neuroimaging include tumors, vascular diseases, neurodegenerative diseases, and psychiatric disorders, including Alzheimer’s disease, Parkinson’s disease, multiple sclerosis, epilepsy, severe depression, and schizophrenia. The ability of electroencephalography and magnetoencephalography to detect changes in brain function in other dementia suggests that they may also be promising biomarkers for early vascular cognitive impairment. In recent years, machine learning has achieved significant success in providing automated analysis for neuroimaging research, and its role may increase in the future. For clinical doctors, understanding these methods and mastering explanatory skills are crucial.

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Frontiers in Neuroimaging IntechOpen
Frontiers in Neuroimaging Edited by Xianli Lv

Frontiers in Neuroimaging

Edited by Xianli Lv

Books edited

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