Guest Editors
Xulei Cui
Department of Anesthesiology at Peking Union Medical College Hospital
Dr. Xulei Cui is the Pain Treatment Professional Group in the Department of Anesthesiology at Peking Union Medical College Hospital and a graduate supervisor. She also serves as a member and secretary of the Ultrasound Group of the Anesthesiology Branch of the Chinese Medical Association, the deputy head of the Pain Study Group of the Beijing Medical Association Anesthesiology Branch, a standing committee member of the Pain Intervention Group of the Chinese Medical Doctor Association Ultrasound Interventional Subcommittee, a standing committee member of the Anaesthesia and Pain Ultrasound Subcommittee of the Chinese Society of Ultrasound in Medicine and Engineering, a standing committee member of the Pain Rehabilitation Subcommittee of the Chinese Rehabilitation Medicine Association, and a member of the Regional Anaesthesia and Pain Medicine Branch of the China Healthcare International Exchange Promotion Association. She focuses on applying and researching visualization technology in acute and chronic pain treatment. She edited the first domestic spinal ultrasound-related monograph, "Ultrasound Sectional Analysis of Spinal Anatomy Relevant to Anaesthesia and Pain Treatment." Her scientific and technological achievements won the first prize in Huaxia Medical Science and Technology Award in 2022. She has published over 10 SCI papers as first author or corresponding author, more than 30 Chinese core journal articles, holds eight patents, and has completed one scientific and technological achievement transformation.
Fangliang Xing
Beijing Intelligent Entropy Science & Technology Co. Ltd., Department of R & D
Dr. Fangliang Xing serves as Chief AI Scientist at Beijing Intelligent Entropy Science & Technology Co. Ltd., Department of R & D. He earned his Ph.D. in Engineering Mechanics from Harbin Institute of Technology (2002) and completed postdoctoral research in Computer Science at the University of Science and Technology of China (2004), subsequently becoming an expert reviewer for China's Ministry of Science and Technology. With over 20 years of AI research experience across speech recognition, fuzzy image restoration, wind power prediction, digital twins, FMEA-based fault diagnosis, and advanced intelligent medicine, Dr. Xing has recently focused on medical AI applications including human airway digital reconstruction, CT and MRI fusion, non-invasive CBF (CBV) detection, liver fat fraction (LFF) detection for fatty liver assessment, holographic three-dimensional reconstruction of pulmonary nodules, and sepsis immune status research. His work has achieved significant socioeconomic benefits in industrial applications and earned a considerable reputation in scientific research. Dr. Xing has published over 20 SCI journal articles and completed 88 peer reviews for 14 SCI journals over the past two years. ORCID: 0000-0001-7373-8012.
Collection Overview
Ultrasound multimodal fusion is a methodology that combines ultrasound imaging with other medical imaging modalities, such as CT, MRI, and PET, to enhance the accuracy of medical diagnosis and treatment planning, guidance, navigation, and other medical applications. The advantages of ultrasound multimodal fusion include improving the precision and efficiency of medical diagnosis and treatment, leading to better therapeutic outcomes in various medical fields.
The objective of this Methods Collection is to investigate state-of-the-art techniques in ultrasound multimodal fusion in conjunction with artificial intelligence (AI) methods. Furthermore, this research aims to explore the potential applications of ultrasound multimodal fusion in diverse medical disciplines, such as spinal, oncology, cardiology, neurology, and obstetrics/gynaecology.