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Computational Research Lab

Highlights

  • 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.
  • Photoacoustic Assessment of RF Ablation Lesions

    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.

  • Deformation Based Morphometry

    Deformation Based Morphometry

    Large Deformation Registration

    A multi-resolution approach is applied within a pipeline of affine and diffeomorphic registration to achieve a dense large deformation mapping.

  • 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.

Goals

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

Selected Publications

  1. Fuentes D, Elliott A, Weinberg JS, Shetty A, Hazle JD, Stafford RJ. An inverse problem approach to recovery of in vivo nanoparticle concentrations from thermal image monitoring of MR-guided laser induced thermal therapy. Ann Biomed Eng 41(1):100-11, 1/2013. e-Pub 8/2012. PMCID: PMC3524364.
  2. Fuentes D, Yung J, Hazle JD, Weinberg JS, Stafford RJ. Kalman filtered MR temperature imaging for laser induced thermal therapies. IEEE Trans Med Imaging 31(4):984-94, 4/2012. e-Pub 12/2011. PMID: 22203706.
  3. Feng Y, Fuentes D. Model-based planning and real-time predictive control for laser-induced thermal therapy. Int J Hyperthermia 27(8):751-61, 2011. PMID: 22098360.

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.

Contact

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

David Fuentes, PhD

David Fuentes, PhD

Assistant Professor & Lab PI
(713) 745-3377
dtfuentes@mdanderson.org

Faculty Profile

Research Profile

Media Gallery

Ongiong Research Support

MRI for Minimally Invasive Therapy

↓ In-vivo Tissue Damage Monitoring

A Portable Treatment Planning System for MR-Guided Thermal Therapy

↓ Planning Tool After Actual Laser
Fiber
Placement


© 2014 The University of Texas MD Anderson Cancer Center