Harvie Branscomb Professor
Professor of Biomedical Engineering
Professor of Radiology and Radiological Sciences
Professor of Neurological Surgery
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 The work we are doing paints a very different picture where surgery and intervention advancements no longer represent fragmented injections of technology to advance focal capabilities while neglecting the wider workflow impact.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. Weis, K. M. Flint, V. Sanchez, T. E. Yankeelov, and M. I. Miga, 'Assessing the accuracy and reproducibility of modality independent elastography in a murine model of breast cancer', Journal of Medical Imaging, Vol. 2, No. 3, 036001-1-11, 2015
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, DOI 10.1007/s11548-015-1235-9, 2015
D. C. Rucker, Y. Wu, L. W. Clements, J. E. Ondrake, T. S.
Pheiffer, A. L. Simpson, W. R. Jarnagin, and M. I. Miga, ‘A mechanics-based nonrigid registration
method for liver surgery using sparse intraoperative data’, IEEE Transactions
on Medical Imaging, Vol. 33, Issue 1, pp. 147-158, 2014
K. Sun, T. S. Pheiffer, A.
L. Simpson, R. C. Thompson, and M. I. Miga, ‘Near real-time computer
assisted surgery for brain shift correction using biomechanical models’, IEEE Journal of Translational Engineering in Health and
Medicine, Vol. 2, pp.
A. L. Simpson, K. Sun, T. S.
Pheiffer, D. C. Rucker, A. K. Sills, R. C. Thompson, and M. I.
Miga, ‘Evaluation of conoscopic holography for estimating tumor resection
cavities in model-based image-guided neurosurgery’, IEEE Transactions on Biomedical Engineering, Vol. 61, Issue 6, pp. 1833-1843, 2014
T. E. Yankeelov, N. Atuegwu,
D. Hormuth, J. A. Weis, S. L. Barnes, M. I. Miga, E. C. Rericha, V. Quaranta, ‘Clinically relevant modeling of tumor growth
and treatment response’, Science Translational Medicine, Vol. 5, Issue 187, pp.
A. Weis, F. Granero-Molto, T. J. Myers, L. Longobardi, A. Spagnoli, and M. I.
Miga, ‘Comparison of microCT and an inverse finite element approach for
biomechanical analysis: Results in a mesenchymal stem cell therapeutic system
for fracture healing’, Journal of Biomechanics, Vol. 45, No. 12, pp. 2164-2170,
I. Chen, A. M.
Coffey, S. Y. Ding, P. Dumpuri, B. M. Dawant, R. C. Thompson, and M. I. Miga,
'Intraoperative brain shift compensation: Accounting for dural septa', IEEE
Transactions on Biomedical Engineering,Vol. 58, No. 3, pp. 499-508, 2011
P. Dumpuri, L.
W. Clements, B. M. Dawant, and M. I. Miga, 'Model-updated image-guided liver
surgery: Preliminary results using surface characterization', Progress in
Biophysics and Molecular Biology, Vol. 103, Issues 2-3, pp. 197-207, 2010.
R. Chen, M. I. Miga, and R. L. Galloway, 'Optimizing electrode placement using
finite-element models in radiofrequency ablation treatment planning', IEEE Transactions on Biomedical
Engineering, Vol. 56, No. 2, 2009
Dr. Miga began his appointment to the faculty at Vanderbilt University in the Spring of 2001 and is currently the Harvie Branscomb Professor, and a Professor of Biomedical Engineering, Radiology and Radiological Sciences, and Neurological Surgery. He is director of the Biomedical Modeling Laboratory, and co-founder of the Vanderbilt Institute in Surgery and Engineering Center (VISE). He is been awarded multiple NIH grants concerned with image-guided brain and liver surgery, as well as robotic devices for intracranial hemorrhage removal. He recently served 4 years as a charter member of the Biomedical Imaging Technology Study Section at NIH. In May of 2014, Dr. Miga was inducted into the American Institute for Medical and Biological Engineering College of Fellows representing the top 2% of medical and biological engineers. His research interests are in image-guided surgery, computational modeling for therapeutic applications, and inverse problems in therapeutics and imaging.