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Computational Research Lab

Goals

  1. To escalate the role of computational science in providing more optimal planning, targeting, monitoring, and assessment of image-guided procedures.
  2. To develop and validate human assisted computational tools that exploit the unique dynamic closed loop provided by utilizing clinical imaging data as feedback.
  3. To develop advanced algorithms for computational prediction and data assimilation on state of the art computing architectures.

Postdoctoral Position Available

The research focus of the group is the development of predictive computational tools with applications to medical image guided diagnosis and therapy.

Research topics include model calibration, validation, and selection under measurement uncertainty.

The candidate will be expected to work in a highly interdisciplinary research environment with scientists and clinicians throughout MD Anderson and from partner institutions. The candidate will have access to department computing resources, the institutional High Performance Computing Center cluster (HPCC), and allocations to Texas Advanced Computing Center (TACC).

Qualifications:
Candidates should be proficient in scientific computing and should have a PhD by the time of appointment in any Engineering discipline or Applied Mathematics / Statistics.

To be considered for this position, please forward a research statement and curriculum vitae to:

David Fuentes, PhD
Department of Imaging Physics
UT MD Anderson Cancer Center
1515 Holcombe Blvd., Unit 1902
Houston, TX 77030

email: dtfuentes@mdanderson.org

MD Anderson Cancer Center is an equal opportunity employer and does not discriminate on the basis of race, color, national origin, gender, sexual orientation, age, religion, disability or veteran status except where such distinction is required by law. All positions at The University of Texas MD Anderson Cancer Center are security sensitive and subject to examination of criminal history record information. MD Anderson Cancer Center is a smoke-free and drug-free environment.

Highlights

  • Model Assisted Monitoring

    Model Assisted Monitoring

    Patient Specific Calibration

    PRF Based Temperature Imaging

    Non-damaging laser test pulses may simultaneously be used for patient safety to verify the application position as well as used for calibration the parabolic bioheat transfer model on a patient specific basis.

  • Imaging to Mesh Generation Pipeline

    Imaging to Mesh Generation Pipeline

    A schematic of the Imaging-to-FEM mesh pipeline is shown. The hexahedral mesh shown has distinct regions for the prostate and laser applicator.

  • Nano Modeling -Head & Neck Thermal Therapy

    Nano Modeling

    Head & Neck Thermal Therapy

    Finite element based inverse problems techniques are seen capable of accurately reproducing the selective heating of the nanoparticle mediated photothermal therapies. The in-vivo recovered spatial map of optical parameters may also be used as a quantitative marker of the nanoparticle uptake.

Research Support

A Portable Treatment Planning System for MR-Guided Thermal Therapy
2013-2014. National Science Foundation 12-571. PI: D. Fuentes.

MRI for Minimally Invasive Therapy
2013-2014. CABIR-GE In-Kind Research Award. PI: R. J. Stafford Role: Co-Investigator.

Education

  • PhD, Computational and Applied Mathematics, May 2008, The University of Texas at Austin
  • Master of Science, Computational and Applied Mathematics, August 2005, The University of Texas at Austin
  • Bachelor of Science, Aerospace Engineering, Highest Honors, December 2002, The University of Texas at Austin

Contact

David Fuentes, PhD
(713) 745-3377 
dtfuentes@mdanderson.org

Imaging Physics Research

Basic Science Research Labs

Translational Science Research Groups


© 2013 The University of Texas MD Anderson Cancer Center