Michael I. Miga, Ph.D. received his B.S. and M.S. from the University of Rhode Island in Mechanical Engineering and Applied Mechanics, respectively. He received his Ph.D. from Dartmouth College specializing in biomedical engineering. He joined the faculty in the Department of Biomedical Engineering at Vanderbilt University in 2001 and is currently the Harvie Branscomb Professor at Vanderbilt. He is a Professor of Biomedical Engineering, Radiology and Radiological Sciences, Neurological Surgery, and Otolaryngology - Head and Neck Surgery. He is director of the Biomedical Modeling Laboratory, and co-founder of the Vanderbilt Institute for Surgery and Engineering (VISE, www.vanderbilt.edu/vise). He has been PI on several NIH grants concerned with image-guided brain, liver, kidney, and breast surgery. He is also PI and Director of a novel NIH T32 training program entitled, ‘Training Program for Innovative Engineering Research in Surgery and Intervention’ that centers on clinical immersion and is focused on the creation of technology platforms for treatment and discovery in surgery and intervention. In 2014, Dr. Miga was inducted into the American Institute for Medical and Biological Engineering College of Fellows which represents the top 2% of medical and biological engineers. He served as a charter member of the Biomedical Imaging Technology (BMIT-B) and the Bioengineering, Technology, and Surgical Sciences (BTSS) Study Sections of the Center for Scientific Review at NIH from 2010-2014, and 2017-2021, respectively. His research interests are in image-guided surgery and intervention, computational modeling for therapeutic applications, soft-tissue mechanics, biotransport, and inverse problems in therapeutics and imaging.
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Conventional optical tracking systems use cameras sensitive to near-infrared (NIR) light and NIR illuminated/active-illuminating markers to localize instrumentation and the patient in the operating room (OR) physical space. This technology is widely used within the neurosurgical theater and is a staple in the standard of care for craniotomy planning. To accomplish, planning is largely conducted at the time of the procedure in the OR with the patient in a fixed head orientation. We propose a framework to achieve this in the OR without conventional tracking technology, i.e., a “trackerless” approach. Briefly, we investigate an extension of the 3D Slicer which combines surgical planning and craniotomy designation. While taking advantage of the well-developed 3D Slicer platform, we implement advanced features to aid the neurosurgeon in planning the location of the anticipated craniotomy relative to the preoperatively imaged tumor in a physical-to-virtual setup, and then subsequently aid the true physical procedure by correlating that physical-to-virtual plan with an intraoperative magnetic resonance imaging-to-physical registered field-of-view display. These steps are done such that the craniotomy can be designated without the use of a conventional optical tracking technology. To test this approach, four experienced neurosurgeons performed experiments on five different surgical cases using our 3D Slicer module as well as the conventional procedure for comparison. The results suggest that our planning system provides a simple, cost-efficient, and reliable solution for surgical planning and delivery without the use of conventional tracking technologies. We hypothesize that the combination of this craniotomy planning approach and our past developments in cortical surface registration and deformation tracking using stereo-pair data from the surgical microscope may provide a fundamental realization of an integrated trackerless surgical guidance platform.
Development of a diaphragmatic motion-based elastography framework for assessment of liver stiffness
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While the use of finite element analysis has had a long standing precedent within the traditional engineering community, its use within medical imaging, image processing, surgical simulation, and image-guided interventions is relatively recent. This course will be specifically targeted at: a basic introduction to partial differential equations used in medical imaging, understanding the foundation of weighted residual techniques, and implementing the finite element method. As opposed to courses that offer an overview of methodologies, this course is specifically focused at understanding the basic application of finite element analysis to partial differential equations within the domain of medical imaging. Examples of using the finite element method in registration, surgical simulation, image-guided interventions, and image reconstruction will be presented.
This tutorial will cover the basic technical background in the field of image guided procedures and computer aided surgery. The tutorial will begin with an overview of the clinical issues with a focus on minimally invasive techniques in interventional radiology. The basic technology components such as image acquisition devices, localizers, registration techniques, and display methods will be discussed. A historical overview including some of the early work in the field will be presented. Case studies will also be discussed with a focus on the use of electromagnetic tracking for image-guided abdominal interventions. A summary of research issues and directions for future research will also be given.
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