PHIND Seminar Series - Akshay Chaudhari, PhD

Dr. Akshay Chaudhari is an Instructor in the Radiological Sciences Lab (RSL) and Precision Health and Integrated Diagnostics (PHIND) sections in department of Radiology who works at the interface of radiology and artificial intelligence. His research interests include developing efficient and safer medical imaging acquisition techniques, repeatable and accurate image analysis tools, and on multi-modality sensor fusion. He graduated with honors with a B.S. in Bioengineering from the University of California San Diego in 2012 and  completed his Ph.D. from Stanford Bioengineering in 2017 focusing on novel MRI methods to perform rapid quantitative musculoskeletal imaging. Dr Chaudhari received the National Science Foundation Graduate Research Fellowship, the Whitaker Fellowship, and the Siebel Fellowship to support his doctoral research. Dr. Chaudhari is the winner of the ISMRM W.S. Moore Young Investigator Award, and has won 6 additional young investigator awards for his work on advanced medical imaging acquisition and analysis techniques, and is a Junior Fellow of the ISMRM.

Magnetic Resonance Imaging (MRI) is a medical imaging modality that offers exquisite resolution and soft-tissue contrast. It is an integral component in diagnostic radiology as well as in basic science research studies due its sensitivity in detecting subtle variations in tissue structure. While MRI can provide a rich source of information, typical acquisition times of 30-40 minutes can limit further widespread use, increase costs, and diminish the patient experience. Moreover, the high-resolution and multi-dimensional MRI datasets can also cause a challenge for efficient and accurate image interpretation. In this talk, through specific examples in musculoskeletal MRI, I will cover recent advances in MRI aided by classical engineering techniques as well as deep learning to substantially reduce the duration of MRI exams and for subsequent image analysis. I will describe how these efforts are helping change the paradigm of MRI by reducing costs and increasing efficiency.