David Fuentes

Assistant Professor, PhD
Department of Imaging Physics
The University of Texas MD Anderson Cancer Center

1515 Holcombe Blvd., Unit 1902
Houston, TX 77030
1881 East Road, Unit 1902
Houston, TX 77054
off: (713) 745 3377
fax: (713) 563 5084
E-Mail: fuentesdt[at]gmail.com

Research Interests:
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.

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

Curriculum vitae

Articles Under Review
[1] C Walker, D. Fuentes, Peder Larson, Vikas Kundra, Daniel Vigneron, and James Bankson. Effects of Excitation Angle Strategy on Quantitative Analysis of Hyperpolarized Pyruvate. MRM, 2018. in review. [ bib ]
[2] SL Thrower, SK Kandala, D. Fuentes, W Stefan, J Sovizi, MX Huang, K Mathieu, and JD Hazle. A sparse reconstruction algorithm for superparamagnetic relaxometry. Medical Physics, 2018. in review. [ bib ]
[3] Joseph D Butner, D. Fuentes, Bulent Oat, George A Calin, Xiaobo Zhou, John Lowengrub, Vittorio Cristini, and Zhihui Wang. A multiscale agent-based model of ductal carcinoma in situ. PLOS Computational Biology, 2018. in review. [ bib ]
[4] Javad Sovizi, Sara L Thrower, Kelsey B Mathieu, Wolfgang Stefan, John D Hazle, and D. Fuentes. Data-driven Image Reconstruction in Superparamagnetic Relaxometry. Computer Methods and Programs in Biomedicine, 2017. in review. [ bib ]
[5] J. Lin, E. Gates, J. Weinberg, J. Hamilton, S. Prabhu, G. Fuller, V. Baladandayuthapani, J. D. Hazle, D. Fuentes, and D. Schellingerhout. Radiological-Pathological Correlations and Imaging Signatures for Gliomas. Journal of Clinical Oncology, 2017. in review. [ bib ]
[6] D. Fuentes, K. Ahmed, J.S. Lin, R. Ali, A. Kaseb, M. Hassan, J. Sruk, J.D. Hazle, A. Qayyum, and K. Elsayes. Automated Volumetric Assessment of Hepatocellular Carcinoma Response to Sorafenib: A pilot study. Abdominal Radiology, 2017. in review. [ bib ]
[7] K. Ahmed, D. Fuentes, J.S. Lin, R. Ali, A. Kaseb, W. Wei, J.D. Hazle, A. Qayyum, and K. Elsayes. Volumetric RECIST Assessment of Hepatocellular Carcinoma Response to Treatment using a Random Forest based Automated Segmentation. Journal of Hepatocellular Carcinoma, 2017. in review. [ bib | .pdf ]

Journal Articles/Book Chapters Cover Pages IEEE TBME May 2010 IJH Aug 2011

[1] Constance Owens, Christine Peterson, Chad Tang, Eugene Koay, Wen Yu, Dennis Mackin, Jing Li, Mohammad Salehpour, D. Fuentes, Laurence Court, and Jing Yang. Lung tumor segmentation methods: Impact on the uncertainty of radiomics features for non-small cell lung cancer. PloS one, 2018. accepted. [ bib ]
[2] Ahmed M Khalaf, D. Fuentes, Ali I Morshid, Mata R Burke, Ahmed O Kaseb, Manal Hassan, John D Hazle, and Khaled M Elsayes. Role of wnt/β-catenin signaling in hepatocellular carcinoma, pathogenesis, and clinical significance. Journal of hepatocellular carcinoma, 5:61, 2018. [ bib ]
[3] D. Fuentes, N Munoz, C Guo, U Polak, A Minhaj, WJ Allen, M Gustin, and ENK Cressman. A molecular dynamics approach towards evaluating osmotic and thermal stress in the extracellular environment. International Journal of Hyperthermia, 2018. accepted. [ bib ]
[4] D. Mitchell, S. Fahrenholtz, CJ MacLellan, D. Bastos, G. Rao, S. Prabhu, J. Weinberg, JD Hazle, RJ Stafford, and D. Fuentes. A heterogeneous tissue model for treatment planning for magnetic resonance-guided laser interstitial thermal therapy. International Journal of Hyperthermia, pages 1--10, 2018. [ bib ]
[5] D. Hormuth, A. Jarrett, E. ABF Lima, M. .T. McKenna, D. Fuentes, and T. Yankeelov. Mechanism based modeling of tumor growth and treatment response constrained by multiparametric imaging data. JCO Clinical Cancer Informatics, 2018. accepted. [ bib ]
[6] Samuel John Fahrenholtz, Reza Madankan, Shabbar Danish, John D Hazle, R Jason Stafford, and D. Fuentes. Theoretical model for laser ablation outcome predictions in brain: calibration and validation on clinical MR thermometry images. International Journal of Hyperthermia, pages 1--11, 2017. [ bib ]
[7] Marites P Melancon, Tomas Appleton Figueira, D. Fuentes, Li Tian, Yang Qiao, Jianhua Gu, Mihai Gagea, Joe E Ensor, Nina M Muñoz, Kiersten L Maldonado, and A. Tam. Development of an Electroporation and Nanoparticle-based Therapeutic Platform for Bone Metastases. Radiology, page 161721, 2017. [ bib ]
[8] J. Yung, D. Fuentes, C. J. MacLellan, F. Maier, J. D. Hazle, and R. J. Stafford. Referenceless Magnetic Resonance Temperature Imaging using Gaussian Process Modeling. Medical Physics, 44(7):3545--3555, 2017. [ bib ]
[9] V. Beechar, S. Prabhu, D. Bastos, J. Weinberg, RJ Stafford, D. Fuentes, K. Hess, and G. Rao. Volumetric Response of Progressing Post-SRS Lesions Treated with Laser Interstitial Thermal Therapy. Journal of Neuro-Oncology, pages 1--9, 2017. [ bib ]
[10] Javad Sovizi, Kelsey B Mathieu, Sara L Thrower, Wolfgang Stefan, John D Hazle, and D. Fuentes. Gaussian Process Classification of Superparamagnetic Relaxometry Data: Phantom Study. Artificial Intelligence in Medicine, 82:47--59, 2017. [ bib ]
[11] C. J. MacLellan, D. Fuentes, S. Prabhu, G. Rao, J. Weinberg, J. D. Hazle, and R. J. Stafford. A methodology for thermal dose model parameter development using perioperative MRI. International Journal of Hyperthermia, pages 1--10, 2017. [ bib ]
[12] Trevor Mitcham, Houra Taghavi, James Long, Cayla Wood, D. Fuentes, Wolfgang Stefan, John Ward, and Richard Bouchard. Photoacoustic-based so2 estimation through excised bovine prostate tissue with interstitial light delivery. Photoacoustics, 7:47--56, 2017. [ bib ]
[13] J. Lin, D. Fuentes, J. Weinberg, S. Prabhu, V. Baladandayuthapani, J. D. Hazle, and D. Schellingerhout. Performance Assessment for Brain Magnetic Resonance Imaging Registration Methods. American Journal of Neuroradiology, 38(5):973--980, 2017. [ bib ]
[14] Reza Madankan, Wolfgang Stefan, SJ Fahrenholtz, CJ MacLellan, JD Hazle, RJ Stafford, Jeffrey S Weinberg, Ganesh Rao, and D. Fuentes. Accelerated Model-based Signal Reconstruction for Magnetic Resonance Imaging in Presence of Uncertainties. Physics in medicine and biology, 62(1):214, 2016. [ bib ]
[15] Alda L Tam, Tomas A Figueira, Mihai Gagea, Joe E Ensor, Katherine Dixon, Amanda McWatters, Sanjay Gupta, and D. Fuentes. Irreversible Electroporation in the Epidural Space of the Porcine Spine: Effects on Adjacent Structures. Radiology, page 152688, 2016. [ bib ]
[16] J Tinsley Oden, Ernesto ABF Lima, Regina C Almeida, Yusheng Feng, Marissa Nichole Rylander, D. Fuentes, Danial Faghihi, Mohammad M Rahman, Matthew DeWitt, and Manasa Gadde. Toward predictive multiscale modeling of vascular tumor growth. Archives of Computational Methods in Engineering, pages 1--45, 2015. [ bib ]
[17] James A Bankson, Christopher M Walker, Marc S Ramirez, Wolfgang Stefan, D. Fuentes, Matthew E Merritt, Jaehyuk Lee, Vlad C Sandulache, Yunyun Chen, Liem Phan, et al. Kinetic modeling and constrained reconstruction of hyperpolarized 1-13c-pyruvate offers improved metabolic imaging of tumors. Cancer research, pages canres--0171, 2015. [ bib ]
[18] S. Fahrenholtz, T. Moon, M. Franco, D. Medina, J. D. Hazle, R. J. Stafford, F. Maier, S. Danish, A. Gowda, A. Shetty, T. Warburton, and D. Fuentes. A Model Evaluation Study for Treatment Planning of Laser Induced Thermal Therapy. International Journal of Hyperthermia, 31(7):705--714, 2015. [ bib ]
[19] F. Maier, D. Fuentes, J. S. Weinberg, J. Hazle, and R. J. Stafford. Robust Phase Unwrapping using a Sorted List, Multi-clustering Algorithm. Magnetic Resonance in Medicine, 73(4):1662--1668, 2015. [ bib | DOI | http ]
[20] D. Fuentes, J. Contreras, J. Yu, R. He, E. Castillo, R. Castillo, and T. Guerrero. Morphometry-based measurements of the structural response to whole-brain radiation. International Journal of Computer Assisted Radiology and Surgery, 10:393--401, 2014. [ bib | DOI | http ]
[21] Edward Castillo, Richard Castillo, D. Fuentes, and Thomas Guerrero. Computing global minimi to a constrained b-spline image registration problem from optimal l1 perturbations to block match data. Medical physics, 41(4):041904, 2014. [ bib ]
[22] Christopher J MacLellan, D. Fuentes, Andrew M Elliott, Jon Schwartz, John D Hazle, and R Jason Stafford. Estimating nanoparticle optical absorption with magnetic resonance temperature imaging and bioheat transfer simulation. International Journal of Hyperthermia, 30(1):47--55, 2013. [ bib ]
[23] E. Yeniaras, D. Fuentes, S.J. Fahrenholtz, J.S. Weinberg, F. Maier, J.D. Hazle, and R.J. Stafford. Design and initial evaluation of a treatment planning software system for MRI-guided laser ablation in the brain. International Journal of Computer Assisted Radiology and Surgery, pages 1--9, 2013. [ bib | DOI | http ]
[24] S. Fahrenholtz, R. J. Stafford, J. Hazle, and D. Fuentes. Generalised polynomial chaos-based uncertainty quantification for planning MRgLITT procedures. International Journal of Hyperthermia, 29(4):324--335, 2013. PMC3924420. [ bib | http ]
[25] R. Castillo, E. Castillo, D. Fuentes, M. Ahmad, A. M Wood, M. S. Ludwig, and T. Guerrero. A Reference Dataset for Deformable Image Registration Spatial Accuracy Evaluation using the COPDgene Study Archive. Physics in Medicine and Biology, 58(9):2861, 2013. PMC3677192. [ bib ]
[26] D. Fuentes, A. Elliott, J. S. Weinberg, A. Shetty, J. D. Hazle, and R. J. Stafford. An Inverse Problem Approach to Recovery of In-Vivo Nanoparticle Concentrations from Thermal Image Monitoring of MR-Guided Laser Induced Thermal Therapy. Ann. BME., 41(1):100--111, 2013. PMC3524364. [ bib | http ]
[27] D. Fuentes, J. Yung, J. D. Hazle, J. S. Weinberg, and R. J. Stafford. Kalman Filtered MR Temperature Imaging for Laser Induced Thermal Therapies. Trans. Medical Imaging, 31(4):984--994, 2012. Special Issue on Interventional Imaging, PMC3873725. [ bib | http ]
[28] Y. Feng and D. Fuentes. Real-Time Predictive Surgical Control for Cancer Treatment Using Laser Ablation [Life Science]. Signal Processing Maga, IEEE, 28(3):134 --138, May 2011. [ bib ]
[29] Y. Feng and D. Fuentes. Model-Based Planning and Real-Time Predictive Control for Laser-Induced Thermal Therapy. Inter. Journal Hyperthermia, 27(8):751--761, 2011. invited review. PMC3930104. [ bib ]
[30] D. Fuentes, C. Walker, A. Elliott, A. Shetty, J. Hazle, and R. J. Stafford. MR Temperature Imaging Validation of a Bioheat Transfer Model for LITT. International Journal of Hyperthermia, 27(5):453--464, 2011. Cover Page, PMC3930085. [ bib | DOI ]
[31] R. J. Stafford, D. Fuentes, A. Elliott, and K. Ahrar. Laser Induced Thermal Therapy for Ablation. Crit. Rev. Biomed. Eng., 38(1):79--100, 2010. [ bib ]
[32] D. Fuentes, Y. Feng, A. Elliott, A. Shetty, R. J. McNichols, J. T. Oden, and R. J. Stafford. Adaptive Real-Time Bioheat Transfer Models for Computer Driven MR-guided Laser Induced Thermal Therapy. IEEE Trans. Biomed. Eng., 57(5), 2010. Cover Page, PMC3857613. [ bib | http ]
[33] D. Fuentes, R. Cardan, R. J. Stafford, J. Yung, G. D. Dodd-III, and Y. Feng. High Fidelity Computer Models for Prospective Treatment Planning of RF Ablation with in vitro Experimental Correlation. J. of Vascular and Interventional Radiology, 21(11):1725--1732, 2010. PMC2966506. [ bib | http ]
[34] D. Fuentes, J. T. Oden, K. R. Diller, J. Hazle, A. Elliott, A. Shetty, and R. J. Stafford. Computational Modeling and Real-Time Control of Patient-Specific Laser Treatment Cancer. Ann. BME., 37(4):763, 2009. PMC4064943. [ bib | http ]
[35] Y. Feng, D. Fuentes, A. Hawkins, J. Bass, and M. N. Rylander. Model-Based Optimion and Real-Time Control for Laser Treatment of Heterogeneous Soft Tissues. CMAME, 198(21-26):1742--1750, 2009. Advances in Simulation-Based Engineering Sciences Special Issue Honoring Prof. J. Tinsley Oden, PMC2871336. [ bib | http ]
[36] Y. Feng, D. Fuentes, A. Hawkins, J. Bass, M. N. Rylander, A. Elliott, A. Shetty, R. J. Stafford, and J. T. Oden. Nanoshell-Mediated Laser Surgery Simulation for Prostate Cancer Treatment. Engineering with Computers, 25(1):3--13, 2009. PMC2905827. [ bib | DOI ]
[37] J. T. Oden, K. R. Diller, C. Bajaj, J. C. Browne, J. Hazle, I. Babuška, J. Bass, L. Demkowicz, Y. Feng, D. Fuentes, S. Prudhomme, M. N. Rylander, R. J. Stafford, and Y. Zhang. Dynamic Data-Driven Finite Element Models for Laser Treatment of prostate cancer. Num. Meth. PDE, 23(4):904--922, 2007. PMC2850081. [ bib | http ]
[38] D. Fuentes, D. Littlefield, J.T. Oden, and S. Prudhomme. Extensions of goal-oriented error estimation methods to simulations of highly-nonlinear response of shock-loaded elastomer-reinforced structures. Comput. Methods Appl. Mech. Engrg., 195:4659--4680, 2006. [ bib | http ]
Conference Proceedings

[1] Drew Mitchell, Ken-Pin Hwang, Tao Zhang, and D. Fuentes. Information theory optimion of acquisition parameters for improved synthetic MRI reconstruction. In Medical Imaging 2018: Physics of Medical Imaging, volume 10573, page 105733A. International Society for Optics and Photonics, 2018. [ bib ]
[2] W Stefan, K Mathieu, SL Thrower, D. Fuentes, C Kaffes, J Sovizi, and JD Hazle. Automated algorithms for improved pre-processing of magnetic relaxometry data. In Medical Imaging 2018: Physics of Medical Imaging, volume 10573, page 105733R. International Society for Optics and Photonics, 2018. [ bib ]
[3] Lynn Bi, Javad Sovizi, Kelsey Mathieu, Wolfgang Stefan, Sara Thrower, John Hazle, and D. Fuentes. Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles. In Medical Imaging 2018: Biomedical Applications in Molecular, Structural, and Functional Imaging, volume 10578, page 105782G. International Society for Optics and Photonics, 2018. [ bib ]
[4] SL Thrower, D. Fuentes, W Stefan, J Sovizi, K Mathieu, and JD Hazle. Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry. In Medical Imaging 2018: Physics of Medical Imaging, volume 10573, page 1057327. International Society for Optics and Photonics, 2018. [ bib ]
[5] CJ MacLellan, M Melancon, F Salatan, Q Yang, KP Hwang, D. Fuentes, and RJ Stafford. MO-FG-BRA-09: Quantification of Nanoparticle Heating and Concentration for MR-Guided Laser Interstitial Thermal Therapy. Medical physics, 42(6):3566--3566, 2015. [ bib ]
[6] SJ Fahrenholtz, R Madankan, JD Hazle, RJ Stafford, and D. Fuentes. Su-c-bra-03: Prediction of laser induced thermal therapy: Results of model training and cross validation. Medical physics, 42(6):3196--3196, 2015. [ bib ]
[7] T Appleton Figueira, D. Fuentes, A McWatters, K Dixon, M Gagea-Iurascu, and A Tam. Determining the energy threshold for irreversible electroporation of the spinal cord with mathematical modeling. Journal of Vascular and Interventional Radiology, 26(2):S118--S119, 2015. [ bib ]
[8] E. Yeniaras, D. Fuentes, S. Fahrenholtz, R. He, J. Hazle, and R. J. Stafford. 3D Slicer Based Approach for Planning and Performing Image Guided Laser Induced Thermal Therapy. In 5th Image Guided Therapy Workshop, NCIGT, Department of Radiology, Brigham and Women's Hospital, and Harvard Medical School, Boston, MA, USA., volume 5, page 20, 2012. [ bib ]
[9] J. Yung, D. Fuentes, J. Hazle, and R. Stafford. A Phantom Validation Study of a 3D Background Phase Model for MR Thermometry. Medical physics, 39(6):3664, 2012. [ bib ]
[10] S. Fahrenholtz, D. Fuentes, R. Stafford, and J. Hazle. Uncertainty Quantification by Generalized Polynomial Chaos for MR-Guided Laser Induced Thermal Therapy. Medical physics, 39(6):3857, 2012. [ bib ]
[11] Y. Feng, D. Fuentes, R. J. Stafford, and J. T. Oden. Model-Based Real-Time Control for Laser Induced Thermal Therapy with Applications to Prostate Cancer Treatment. volume 7175, page 717515. SPIE, 2009. [ bib | DOI | http ]
[12] Y. Feng, D. Fuentes, A. Hawkins, and J. T. Oden. MRTI-Based Optimion and Real-Time Laser Surgical Control for Cancer Treatment Using Fast Inverse Analysis Techniques. In BioMedical Engineering and Informatics, 2008. BMEI 2008., volume 2, pages 168--172, May 2008. [ bib | DOI | .pdf ]
[13] J. T. Oden, D. Fuentes, J. Bass, and Y. Feng. Dynamic-Data-Driven Systems Aid Patient-Specific Cancer Therapy. spie.org, 2008. [ bib | DOI ]
[14] C. Bajaj, J. T. Oden, K. R. Diller, J. C. Browne, J. Hazle, I. Babuska, J. Bass, L. Bidaut, L. Demkowicz, A. Elliott, Y. Feng, D. Fuentes, S. Prudhomme, R. J. Stafford, and Y. Zhang. Using Cyber-Infrastructure for Dynamic Data Driven Laser Treatment of Cancer. In Proceedings Lecture Notes in Computer Science, volume 4487, pages 972--979, 2007. [ bib | .pdf ]
[15] J. T. Oden, K. R. Diller, C. Bajaj, J. C. Browne, J. Hazle, I. Babuska, J. Bass, L. Demkowicz, A. Elliott, Y. Feng, D. Fuentes, S. Prudhomme, M. N. Rylander, R. J. Stafford, and Y. Zhang. Development of a Computational Paradigm for Laser Treatment of Cancer. In Proceedings Lecture Notes in Computer Science, volume 3993, pages 530--537, 2006. [ bib | .pdf ]
Book Chapters

[1] K. R. Diller, J. T. Oden, C. Bajaj, J. C. Browne, J. Hazle, I. Babuška, J. Bass, L. Bidaut, L. Demkowicz, A. Elliott, Y. Feng, D. Fuentes, S. Goswami, A. Hawkins, S. Khoshnevis, B. Kwon, S. Prudhomme, and R. J. Stafford. Advances in Numerical Heat Transfer, editor: W. J. Minkowycz and E. M. Sparrow, volume 3: Numerical Implementation of Bioheat Models and Equations, chapter 9: Computational Infrastructure for the Real-Time Patient-Specific Treatment of Cancer. Taylor & Francis Group, 2008. [ bib | http ]

Other Manuscripts
[1] F Maier, CJ MacLellan, D. Fuentes, E.N.K Cressman, K Hwang, JD Hazle, and RJ Stafford. A Multi-Parametric Pulse Sequence for Characterion of Thermochemical Ablation Injections. 2015. [ bib ]
[2] R Madankan, W Stefan, S Fahrenholtz, CJ MacLellan, JD Hazle, RJ Stafford, JS Weinberg, G Rao, and D. Fuentes. Accelerated Magnetic Resonance Thermometry in Presence of Uncertainties. Arxiv Preprint, http://arxiv.org/abs/1510.08875, 2015. [ bib | http ]
[3] W Stefan, D. Fuentes, E Yeniaras, K Hwang, JD Hazle, and RJ Stafford. Novel Method for Background Phase Removal on MRI Proton Resonance Frequency Measurements. Arxiv Preprint, https://arxiv.org/submit/2112878/view, 2015. [ bib | http ]
[4] C Acosta, D. Fuentes, J Zhou, and Y Feng. A Computational and Experimental study of the Cooling Ect of Liver Vessels During Radiofrequency Ablation. 2014. [ bib ]
[5] D. Fuentes, JT Oden, KR Diller, J Yung, and Y Feng. Computational and mr-guided patient-specific laser induced thermal therapy of cancer. ICES Report, pages 13--33, 2013. [ bib ]
[6] E Castillo, R Castillo, X Gu, J Martinez, D. Fuentes, S Jiang, P Friedman, and T Guerrero. Deformable Image Registration for Breath-hold CT Image Pairs from the COPDgene Study. 2011. [ bib | http ]

Courses Taught
[1] GS02-1032 Principles of Magnetic Resonance Imaging, Lecturer (DCE Lab). The University of Texas Graduate School of Biomedical Sciences (GSBS), Summer 2015. [ bib | http ]
[2] Scientific Computing Bootcamp, Short Course, Organizer. The University of Texas Graduate School of Biomedical Sciences (GSBS), August 2013, 2014, 2015. [ bib ]
[3] GS02-1183 Applied Mathematics for Medical Physics, Lecturer. The University of Texas Graduate School of Biomedical Sciences (GSBS), Fall 2012-. [ bib | http ]