Harvie Branscomb Professor
Professor of Biomedical Engineering
Professor of Radiology and Radiological Sciences
Professor of Neurological Surgery
Professor of Otolaryngology
The focus of the Biomedical Modeling Laboratory (BML) is the development of new paradigms in detection, diagnosis, characterization, and treatment of disease through the integration of computational models into research and clinical practice.
Computational Modeling Enabling Therapy: Translational Engineering for Surgery & Intervention
While patient diagnostic information has expanded dramatically with modern medical imaging, and continues to grow with contemporary informatics research, the advancement of procedural medicine has lagged largely due to the lack of translating biomedical research. The work we are doing paints a very different picture where surgery and intervention advancements no longer represent fragmented injections of technology to advance focal capabilities while neglecting the wider workflow impact. Rather, diagnostic information initiates a process whereby procedure-specific, and patient-specific modular technologies and computational approaches are assembled into novel, perhaps never been realized, systems that optimize therapy delivery. This is a paradigm that challenges convention and shifts patient care to diverse collaborative teams whereby engineers, scientists, statisticians, and informaticists work with physicians selecting the optimal combination of technologies and therapies to treat based on presentation and therapeutic goals. This type of transformative approach requires biomedical research to invest in translational platforms and study designs that enable investigation in arguably the most important of domains, the patient. Projects in the laboratory reflect: (1) enhancing soft-tissue image guided surgery in organs such as the brain, liver, breast, and kidney, (2) model-driven interventional guidance for neoadjuvant chemotherapy, and (3) model-enhanced ablative therapy delivery.
- J. A. Collins, J. S. Heiselman, L. W. Clements, D. B. Brown, and M. I. Miga, "Multiphysics modeling toward enhanced guidance in hepatic microwave ablation: a preliminary framework," Journal of Medical Imaging, vol. 6, pp. 025007-025007, 2019-Apr 2019.
- S. Narasimhan, H. B. Johnson, T. M. Nickles, M. I. Miga, N. Rana, A. Attia, and J. A. Weis, "Biophysical model-based parameters to classify tumor recurrence from radiation-induced necrosis for brain metastases," Medical Physics, vol. 46, pp. 2487-2496, May 2019.
- J. S. Heiselman, L. W. Clements, J. A. Collins, J. A. Weis, A. L. Simpson, S. K. Geevarghese, T. P. Kingham, W. R. Jarnagin, and M. I. Miga, "Characterization and correction of intraoperative soft tissue deformation in image-guided laparoscopic liver surgery," Journal of Medical Imaging, vol. 5, Apr 2018.
- L. W. Clements, J. A. Collins, J. A. Weis, A. L. Simpson, T. P. Kingham, W. R. Jarnagin, and M. I. Miga, "Deformation correction for image guided liver surgery: An intraoperative fidelity assessment," Surgery, vol. 162, pp. 537-547, Sep 2017.
- M. Luo, S. F. Frisken, J. A. Weis, L. W. Clements, P. Unadkat, R. C. Thompson, A. J. Golby, and M. I. Miga, "Retrospective study comparing model-based deformation correction to intraoperative magnetic resonance imaging for image-guided neurosurgery," Journal of Medical Imaging, vol. 4, Jul 2017.
- J. A. Collins, J. A. Weis, J. S. Heiselman, L. W. Clements, A. L. Simpson, W. R. Jarnagin, and M. I. Miga, "Improving Registration Robustness for Image-Guided Liver Surgery in a Novel Human-to-Phantom Data Framework," IEEE Transactions on Medical Imaging, vol. 36, pp. 1502-1510, Jul 2017.
- J. A. Weis, M. I. Miga, and T. E. Yankeelov, "Three-dimensional image-based mechanical modeling for predicting the response of breast cancer to neoadjuvant therapy," Computer Methods in Applied Mechanics and Engineering, vol. 314, pp. 494-512, Feb 1 2017.
- R. H. Griesenauer, J. A. Weis, L. R. Arlinghaus, I. M. Meszoely, and M. I. Miga, "Breast tissue stiffness estimation for surgical guidance using gravity-induced excitation," Physics in Medicine and Biology, vol. 62, pp. 4756-4776, Jun 21 2017.
- M. I. Miga, "Computational Modeling for Enhancing Soft Tissue Image Guided Surgery: An Application in Neurosurgery," Annals of Biomedical Engineering, vol. 44, pp. 128-138, Jan 2016.
- R. H. Conley, I. M. Meszoely, J. A. Weis, T. S. Pheiffer, L. R. Arlinghaus, T. E. Yankeelov, and M. I. Miga, "Realization of a biomechanical model-assisted image guidance system for breast cancer surgery using supine MRI," International Journal of Computer Assisted Radiology and Surgery, vol. 10, pp. 1985-1996, Dec 2015.
- J. A. Weis, M. I. Miga, L. R. Arlinghaus, X. Li, V. Abramson, A. B. Chakravarthy, P. Pendyala, and T. E. Yankeelov, "Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model," Cancer Research, vol. 75, pp. 4697-4707, Nov 15 2015.
- T. E. Yankeelov, N. Atuegwu, D. Hormuth, J. A. Weis, S. L. Barnes, M. I. Miga, E. C. Rericha, and V. Quaranta, "Clinically Relevant Modeling of Tumor Growth and Treatment Response," Science Translational Medicine, vol. 5, May 29 2013.
Dr. Miga joined the faculty in the Department of Biomedical Engineering at Vanderbilt University in the Spring of 2001 and is the Harvie Branscomb Professor at Vanderbilt. He is a Professor of Biomedical Engineering, Radiology and Radiological Sciences, Neurological Surgery, and Otolaryngology. 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 is focused on the creation of translational technologies 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) Study Section of the Center for Scientific Review at NIH from 2010-2014, and in 2017 began serving on the Bioengineering, Technology, and Surgical Sciences (BTSS) Study Section as a charter member. 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.