Valentina Pedoia, PhD
|School||UCSF School of Medicine|
|Address||1700 4th St|
San Francisco CA 94158
|Politecnico of Milano, Italy||B.S||Telecommunication Engineering, Signal Processing Division||2005|
|Politecnico of Milano, Italy||M.Sc||Telecommunication Engineering, Signal Processing Division||2008|
|Insubria University, Italy||Ph.D||Medical Computer Science ||2013|
|Margaret Hart Surbeck Laboratory of Advanced Imaging ||2015||9th Annual Surbeck Young Investigator's Award|
|International Society of Magn. Reson. in Medicine||2015||Magna Cum Lode Abstract; ISMRM 23th Annual Meeting and Exhibition|
|International Society of Magn. Reson. in Medicine ||2015||Best Poster at the Musculoskeletal MR study group, ISMRM 23th Annual Meeting and Exhibition|
|International Society of Magn. Reson. in Medicine ||2015||Travel Stipend Award at the ISMRM 8th International Workshop on imaging based measures of OA|
|International Society of Magn. Reson. in Medicine ||2015||Highly ranked abstract at the ISMRM 8th International Workshop on imaging based measures of OA|
|UCSF and UC Davis||2015|| Research Grants in Women’s Health, SCOR (Principal Investigator)|
|UCSF||2015||Bruce Hasegawa Award|
Valentina is an imaging scientist with main interest in developing algorithms for advanced image post processing and methodologies for improving diagnosis and treatment follow-up of musculoskeletal diseases such as arthritis and joint injury. She received her Masters in Telecommunication Engineering from Politecnico di Milano and her doctorate in Medical Computer Science from Insubria University, Italy. The focus of her doctoral dissertation was on the development of an automatic segmentation and classification method for brain MRI imaging. She joined the Musculoskeletal Quantitative Imaging Research Group in 2013 as a postdoctoral fellow and she is now an Associate Specialist in the lab. Her current main focus is on the application of atlas-based analysis on quantitative MRI imaging to study local biochemical composition of the knee and hip articular cartilage and to relate that with other measures such as: MRI clinical grading, MRI kinematics and biomechanics measures, bone shape features and patient reported outcome.
Medical imaging, computer vision, machine learning, big data analysis, MRI, clinically-oriented quantitative imaging, cartilage degeneration, osteoarthritis, acute injury, rheumatoid arthritis
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