We study applied mathematics applications in computational science and mathematical modeling of physics based phenomena.
Our goals are to:
- Escalate the role of computational science in providing more optimal planning, targeting, monitoring, and assessment of image-guided procedures.
- Develop and validate human-assisted computational science tools that exploit the unique dynamic closed loop provided by utilizing clinical imaging data as feedback.
- Develop advanced algorithms for computational prediction and data assimilation on state of the art computing architectures.
Patient Specific Treatment Planning System for MR-Guided Thermal Therapy in Brain
The project goal is to develop computer models that can accurately and reliably predict the outcome of MR guided laser induced thermal therapy (MRgLITT). Model Predictions will be used for patient selection and treatment planning.
Developing Predictive Models of Treatment Response to HCC
Analytic continuum scale data processing pipelines are being developed to correlate quantitative imaging information with patient outcome. Continuum scale regression models will be calibrated to personalized clinical data to predict time to progression for the treated lesion and overall survival.
David Fuentes, Ph.D.
My research interests concern the development, implementation, and validation of high performance human assisted computational tools for image-guided interventions. The unique dynamic closed loop control system, facilitated by the coupling of the predictive capabilities of computational simulation with real-time imaging feedback, has the potential to enable novel and robust model-constrained approaches to imaging as well as lay the foundation for reliable minimally invasive computer assisted treatment modalities. My current research focuses on exploiting the predictive abilities of sophisticated numerical algorithms for pretreatment planning, real-time monitoring, and real-time feedback control of laser induced thermal therapies of cancer. The effort to provide accurate predictions and real-time control of the patient specific bioheat transfer are based on finite element techniques that span the fields of: Uncertainty Quantification, Optimion, Control Theory, Parallel Computing, Image Processing, Fluid Mechanics, Solid Mechanics, Error Estimation, and Adaptivity.