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 Pack Pain

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

Integrating Genomics and Imaging to Predict Pain Progression

Publications

Machine learning-based automated scan prescription of lumbar spine MRI acquisitions.

Persistent mucus plugs in proximal airways are consequential for airflow limitation in asthma.

Is hip capsule morphology associated with hip pain in patients without another structural correlate?

Synthetic Knee MRI T1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers.

Multiparametric MRI of Knees in Collegiate Basketball Players: Associations With Morphological Abnormalities and Functional Deficits.

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Practical Applications of Artificial Intelligence in Spine Imaging: A Review.

Chondral Injury Associated With ACL Injury: Assessing Progressive Chondral Degeneration With Morphologic and Quantitative MRI Techniques.

Advanced MRI Approaches for Evaluating Common Lower Extremity Injuries in Basketball Players: Current and Emerging Techniques.

Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis.

Effects of T1p Characteristics of Load-Bearing Hip Cartilage on Bilateral Knee Patellar Cartilage Subregions: Subjects With None to Moderate Radiographic Hip Osteoarthritis.

Associations between Weight Change, Knee Subcutaneous Fat and Cartilage Thickness in Overweight and Obese Individuals: 4-Year Data from the Osteoarthritis Initiative.