Sharmila Majumdar, PhD

co-Executive Director, Science & Technology Resource Group, Scientific Director

Sharmila Majumdar, PhD, is a UCSF Professor and is the Vice Chair for Research and Margaret Hart Surbeck Distinguished Professor in Advanced Imaging in the Departments of Bioengineering and Therapeutic Sciences, Orthopedic Surgery at UCSF and Bioengineering at UC Berkeley. She is Director of the Musculoskeletal Research Interest Group at UCSF, an interdisciplinary group consisting of faculty, post-doctoral scholars and students. She obtained her PhD degree in Engineering and Applied Science from Yale University in 1987, where she stayed as a post-doctoral researcher and Assistant Professor until 1989, when she joined UCSF.

Her research work on imaging, particularly magnetic resonance and micro computed tomography, and development of image processing and analysis tools, has been focused in the areas of osteoporosis, osteo-arthritis and lower back pain. Her research, which is supported by grants from the NIH and corporate entities, and is diverse ranging from technical development to clinical trials. She currently heads an NIH Center of Research Translation focused on Osteoarthritis and Imaging, a joint effort between UCSF and UC Davis.

Dr. Majumdar was selected as a fellow of the American Institute of Medical and Biological Engineers in 2004 and a fellow of the International Society of Magnetic Resonance in Medicine in 2008. In 2007 she was awarded the “Excellence in Direct Teaching and/or Excellence in Mentoring and Advising Award” by the UCSF Haile T. Debas Academy of Medical Educators.

She has published extensively in the Journal of Bone Mineral Research, Magnetic Resonance in Medicine, and other highly regarded journals. She also serves as a reviewer and is on the editorial board of scientific journals, and is a recognized expert in the area of imaging.

Expertise:
Bioengineering, Quantitative Imaging, Image Processing, Musculoskeletal

Specialty:
Musculoskeletal and Quantitative Imaging

Professional Interests:
Radiology, magnetic resonance imaging, image processing, computer-assisted, osteoporosis, osteoarthritis, knee, hip, imaging, bioengineering, cartilage, bone, inter-vertebral disc, musculoskeletal

Education and Training:
• Masters of Science: Yale University, New Haven, Connecticut - Engineering
• Masters of Philosophy: Yale University, New Haven, Connecticut - Applied Science
• Doctor of Philosophy: Yale University, New Haven, Connecticut - Engineering and Applied Science

Applying AI to the Imaging of Chronic Lower Back Pain

Machine and Deep Learning Applied to MR Images to Characterize Degenerative Joint Disease

Integrating Genomics and Imaging to Predict Pain Progression

Publications

Association Between Tibiofemoral Bone Shape Features and Retears After Anterior Cruciate Ligament Reconstruction.

Multi-institutional Evaluation and Training of Breast Density Classification AI Algorithm Using ACR Connect and AI-LAB.

Higher Posterior Tibial Slope and Lower Medial Proximal Tibial Angle of the Knee Are Associated with Delayed Graft Maturation after ACL Reconstruction Surgery with Hamstring Autograft.

Association of cartilage T1? and T2 relaxation time measurement with hip osteoarthritis progression: A 5-year longitudinal study using voxel-based relaxometry and Z-score normalization.

Unmet Needs in Spondyloarthritis: Imaging in Axial Spondyloarthritis.

Improved metal suppression using new generation low-field MRI: a biophantom feasibility study.

The Relationships between Patellofemoral Bone Remodeling, Cartilage Composition, and Vertical Loading Rate: PET/MRI in Isolated Patellofemoral Osteoarthritis.

A Computer Vision Algorithm to Predict Superior Mesenteric Artery Margin Status for Patients with Pancreatic Ductal Adenocarcinoma.

Ligamentum teres lesions are associated with compositional and structural hip cartilage degenerative change: region-specific cartilage degeneration.

Artificial intelligence in musculoskeletal applications: a primer for radiologists.

Clinical and imaging findings associated with preservation of knee joint health over 8 years in individuals aged 65 and over: data from the OAI.

Automated evaluation of hip abductor muscle quality and size in hip osteoarthritis: Localized muscle regions are strongly associated with overall muscle quality.