Rupsa Bhattacharjee, PhD is a Post-Doctoral Associate Specialist in the Radiology and Biomedical Imaging Department.She is a medical imaging and image processing researcher with a primary interest in developing algorithms for to improve the utility of MRI imaging as diagnostic and prognostic tool. She obtained her doctoral degree in Biomedical Engineering working on Susceptibility weighted MR imaging (SWI) in human subjects with tumors and acute ischemic stroke. After graduation, in 2021, she joined the Musculoskeletal and Imaging Research Group at UCSF as post-doctoral fellow to study joint diseases with compositional MRI techniques combined with machine learning tools.


In her current role, she is specifically focused on exploring the role of MRI, PET, and machine learning algorithms to extract imaging biomarkers of several musculoskeletal conditions such as knee and hip osteoarthritis. Her primary project involvements are in, but not limited to:
1) Simultaneous Imaging of Tissue Biochemistry and Metabolism (PET-MRI) associated with Biomechanics in Patella Femoral Joint Osteoarthritis
2) Understanding the complex pathophysiology of joint degeneration, knee and hip joint interactions, impact of gait biomechanics, are all critical to determine the mechanistic basis of hip OA.
3) Ultra-Fast Knee MRI with Deep Learning

She has worked as MRI Application Specialist in Philips Healthcare for 8 years, closely with various healthcare facilities to set up and optimize MRI scan acquisition parameters according to curtailed needs (1.5T & 3.0T). During her years in industry, she has partnered with Clinical scientists to jointly run MRI projects on test and trial basis in India market Philips MRI collaborative research centers. [New product introduction and First-of-kind trials: Compressed SENSE, Amide Proton Transfer, Ultra-short TE, Multi-Nuclei Spectroscopy, Spiral acquisition, Quantitative Susceptibility Mapping, IVIM, Elastography etc.] She has been an active part of Philips India AI-ambassadors team for AI based knowledge sharing and educational, collaborative approaches.