David Fuentes



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

3SCR2.3612
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.

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

Curriculum vitae



Publications
Articles Under Review
[1] Sovizi, Javad and Thrower, Sara L and Mathieu, Kelsey B and Stefan, Wolfgang and Hazle, John D and Fuentes, David. Data-driven Image Reconstruction in Superparamagnetic Relaxometry. Computer Methods and Programs in Biomedicine, 2017. in review. [ bib ]
[2] 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 ]
[3] Khalaf, A.M. and Fuentes, D. and Morshid, A. and Burke, M. and Kaseb, A.O. and Hassan, M. and Hazle, J.D. and Elsayes, K.M. Role of Wnt/B-Catenin Signaling Pathway in Hepatocellular Carcinoma (HCC) Pathogenesis and Its Clinical Significance. Journal of Hepatocellular Carcinoma, 2017. in review. [ bib ]
[4]  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 ]
[5] 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 ]
[6] D. Mitchell and S. Fahrenholtz and CJ MacLellan and D. Bastos and G. Rao and S. Prabhu and J. Weinberg and JD Hazle and RJ Stafford and D. Fuentes. A heterogeneous tissue model for treatment planning for magnetic resonance-guided laser interstitial thermal therapy. International Journal of Hyperthermia. in review. [ bib ]

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

[1] Melancon, Marites P and Appleton Figueira, Tomas and Fuentes, David and Tian, Li and Qiao, Yang and Gu, Jianhua and Gagea, Mihai and Ensor, Joe E and Muñoz, Nina M and Maldonado, Kiersten L and A. Tam. Development of an Electroporation and Nanoparticle-based Therapeutic Platform for Bone Metastases. Radiology, page 161721, 2017. [ bib ]
[2] 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, 2017. [ bib ]
[3] V. Beechar and S. Prabhu and D. Bastos and J. Weinberg and RJ Stafford and D. Fuentes and 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 ]
[4] Sovizi, Javad and Mathieu, Kelsey B and Thrower, Sara L and Stefan, Wolfgang and Hazle, John D and Fuentes, David. Gaussian Process Classification of Superparamagnetic Relaxometry Data: Phantom Study. Artificial Intelligence in Medicine, pages 1--12, 2017. [ bib ]
[5] 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 ]
[6] Trevor Mitcham, Houra Taghavi, James Long, Cayla Wood, Fuentes, David, 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 ]
[7] 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 ]
[8] R. Madankan, W. Stefan, J. D. Hazle, R. J. Stafford, 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 ]
[9] Tam, Alda L and Figueira, Tomas A and Gagea, Mihai and Ensor, Joe E and Dixon, Katherine and McWatters, Amanda and Gupta, Sanjay and Fuentes, David. Irreversible Electroporation in the Epidural Space of the Porcine Spine: Effects on Adjacent Structures. Radiology, page 152688, 2016. [ bib ]
[10] 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 ]
[11] 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 ]
[12] 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 ]
[13] 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 ]
[14] 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 ]
[15] 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 ]
[16] 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 ]
[17] D. Fuentes, JT Oden, KR Diller, J Yung, Y Feng, JD Hazle, A Shetty, and RJ Stafford. Dynamic Data Driven Application Systems, chapter Computational and MR-guided Patient-Specific Laser Induced Thermal Therapy of Cancer. Springer , 2013. accepted. [ bib ]
[18] 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 ]
[19] 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 ]
[20] 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 ]
[21] 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 ]
[22] 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 ]
[23] 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 ]
[24] 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 ]
[25] 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 ]
[26] 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 ]
[27] 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 ]
[28] 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 ]
[29] 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 ]
[30] 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 ]
[31] 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 ]
[32] J. T. Oden, D. Fuentes, J. Bass, and Y. Feng. Dynamic-Data-Driven Systems Aid Patient-Specific Cancer Therapy. spie.org, 2008. [ bib | DOI ]
[33] 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, 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 ]
[34] 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 ]
[35] 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 ]
[36] Fahrenholtz, Samuel John and Madankan, Reza and Danish, Shabbar and Hazle, John D and Stafford, R Jason and Fuentes, David. 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 ]
Conference Proceedings

[1] Lynn Bi, Javad Sovizi, Sara L Thrower, Kelsey Mathieu, Wolfgang Stefan, John D Hazle, and Fuentes, David. Bayesian inference and model selection for physiologically-based pharmacokinetic modeling of superparamagnetic iron oxide nanoparticles. Medical Imaging, (10578-87), 2018. [ bib ]
[2] 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 ]
[3] 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 ]
[4] 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 ]
[5] 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 ]
[6] 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 ]
[7] 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 ]
[8] 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 ]
[9] 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 ]
[10] 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 ]
[11] 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 ]
Presentations

[1] Dhiego Chaves De Almeida Bastos, Ganesh Rao, Jeffrey S Weinberg, Fuentes, David, Jason Stafford, and Sujit S Prabhu. Surg-27. predictors of local control of post srs brain metastasis treated with litt. Neuro-Oncology, 19(6):vi241, 2017. [ bib ]
[2] Javad Sovizi, Sara L Thrower, Fuentes, David, Wolfgang Stefan, John D Hazle, and Kelsey Mathieu. Binary classification of superparamagnetic relaxometry data for cancer screening, 2017. [ bib ]
[3] Sara L Thrower, Kelsey Mathieu, Wolfgang Stefan, R Romero Aburto, Zhen Lu, Robert C Bast, Javad Sovizi, Fuentes, David, and John D Hazle. Volumetric reconstruction of targeted nanoparticles for superparamagnetic relaxometry, 2017. [ bib ]
[4] Ahmed M Khalaf, Fuentes, David, Kareem Ahmed, Reham Abdel-Wahab, Manal Hassan, Ahmed Omar Kaseb, John D Hazle, and Khaled M Elsayes. Quantitative ct imaging features for hepatocellular carcinoma (hcc) with b-catenin (ctnnb1) gene mutation. Journal of Clinical Oncology, 35(4):253, 2017. poster presentation. [ bib ]
[5] SJ Fahrenholtz, RJ Stafford, R Madankan, JD Hazle, D Fuentes. Flexible Training of MR-Guided Laser Ablation Models Via Global Optimion, July 2016. American Association of Physicists in Medicine (AAPM): Annual Meeting, Washington, DC. [ bib ]
[6] R Madankan, C MacLellan , S Fahrenholtz , J Weinberg , G Rao , J Hazle , R Stafford , D Fuentes . Treatment Planning for Laser Ablation Therapy in Presence of Heterogeneous Tissue: A Retrospective Study, July 2016. American Association of Physicists in Medicine (AAPM): Annual Meeting, Washington, DC. [ bib ]
[7] J Lin , D Fuentes , A Chandler , J Hazle , D Schellingerhout. Validation of Image Registration Methods for Brain Magnetic Resonance Imaging, July 2016. American Association of Physicists in Medicine (AAPM): Annual Meeting, Washington, DC. [ bib ]
[8] C MacLellan, D Fuentes, H Espinoza, S Prabhu, G Rao, J Weinberg, R Stafford. Investigation of MRI Derived Thermal Dose Models, July 2016. American Association of Physicists in Medicine (AAPM): Annual Meeting, Washington, DC. [ bib ]
[9]  JS Lin, D Fuentes, I McCutcheon, S Ferguson, S Prabhu, J Weinberg, GN Fuller, JD Hazle, and D Schellingerhout. Development of a Prediction Technique for Tissue Features using Radiological-Pathological Correlations, April 2016. DI Trainee Research Symposium - Poster. [ bib ]
[10]  S.L. Thrower, D Fuentes, R. Madankan, W. Stefan, and J.D. Hazle. A Sparse Approximation Reconstruction Algorithm for Superparamagnetic Relaxometry, April 2016. DI Trainee Research Symposium - Poster. [ bib ]
[11] F. Maier, C. MacLellan, D. Fuentes, E. N. K. Cressman, K. Hwang, J. Yung, J. D. Hazle, and R. J. Stafford. MR monitoring of thermochemical ablation injections, October 2014. cum laude. Tenth Interventional MRI Symposium. Leipzig, Germany, Poster Presentation. [ bib ]
[12] E. Castillo, R. Castillo, D. Fuentes, and T. Guerrero. A Moving Least Squares Approach for Computing Spatially Accurate Transformations That Satisfy Strict Physiologic Constraints, July 2014. American Association of Physicists in Medicine (AAPM): Annual Meeting, Austin, Texas. [ bib ]
[13] C. MacLellan, D. Fuentes, F. Maier, W. Stefan, J. D. Hazle, and R. J. Stafford. Real-time multi-parametric thermal therapy monitoring: GPU versus CPU, May 2014. ISMRM, Milan, Italy, E-Poster Presentation. [ bib ]
[14] F. Maier, C. MacLellan, K. Hwang, D. Fuentes, J. D. Hazle, and R. J. Stafford. A Hybrid T1/T2*/PRF Pulse Sequence with Improved Spectral Resolution, May 2014. ISMRM, Milan, Italy, E-Poster Presentation. [ bib ]
[15] S. Fahrenholtz, T. Moon, M. Franco, Z. Wang, T. Warburton, and D. Fuentes. A Portable Treatment Planning System for MR-Guided Thermal Therapy, May 2014. NSF SBIR/STTR 2014, Phase II Grantee Conference, Baltimore, Maryland. [ bib ]
[16] S. Fahrenholtz, R. J. Stafford, F. Maier, A. Shetty, and D. Fuentes. Inverse problem statistics of optical parameter inference in brain MR-guided laser induced thermal therapy, May 2014. STM 2014 Annual Meeting. Minneapolis, Minnesota. [ bib ]
[17] D. Fuentes, R. Castillo, E. Castillo, and T. Guerrero. Morphometry Based Measurements of the Structural Response to Whole Brain Radiation, July 2014. American Association of Physicists in Medicine (AAPM): Annual Meeting, Austin, Texas. [ bib ]
[18] S. Fahrenholtz, R. J. Stafford, J. Hazle, A. Shetty, and D. Fuentes. Preliminary predictions from an inverse problem-trained model, Sept 2014. Image-Guided Therapy workshop, Cambridge, MA, USA. [ bib ]
[19] D. Fuentes. The Impact of Uncertainty in Nonlinear Temperature Dependent Constitutive Parameters on Predictive Computer Modeling of MRgLITT Procedures, May 2013. ISMRM, Salt Lake City, Utah, E-Poster Presentation. [ bib ]
[20] D. Fuentes. Fast Steady State Solution for Simulating Bioheat Distribution for Image Guided Laser Ablation, April 2013. STM 2013 Annual Meeting. Aruba. . [ bib ]
[21] D. Fuentes. Planning of MR-Guided Laser Induced Thermal Therapy Using UQ Methods, February 2013. SIAM Computational Science and Engineering. Boston, Massachusetts. [ bib ]
[22] Dmitriy Meshkov, Richard Castillo, Edward Castillo, Min Li, Ngoc Pham, Julianne Pollard, David Fuentes, Adenike Olanrewaju, Brian Hobbs, and Thomas Guerrero. Pre-Radiotherapy FDG PET Predicts Radiation Pneumonitis in Non-Small Cell Lung Cancer Patients, June 2013. Society of Nuclear Medicine and Molecular Imaging, Vancouver BC, Canada. [ bib ]
[23] Dmitriy Meshkov, Richard Castillo, Edward Castillo, David Fuentes, Ngoc Pham, Min Li, Adenike Olanrewaju, Julianne Pollard, Brian Hobbs, and Thomas Guerrero. Clinical Symptoms of Radiation Pneumonitis Correlate with Pulmonary Metabolic Radiation Dose-Response in Lung Cancer Patients, June 2013. NCI Joint Workshop: Technology for Innovation in Radiation Oncology, Bethesda MD, USA. [ bib ]
[24] D. Fuentes. UQ Based Planning of MR Guided Laser Induced Thermal Therapy in Brain, May 2012. SAMSI. UQ Transition Workshop. Research Triangle Park, North Carolina. [ bib ]
[25] D. Fuentes. Kalman Filtered Temperature Imaging for Monitoring MRgLITT procedures, April 2012. STM 2012 Annual Meeting. Portland, Oregon. [ bib ]
[26] D. Fuentes. Prospective Planning of MR guided Laser Induced Thermal Therapy in Brain, July 2011. National Congress on Computational Mechanics. Minneapolis, Minnesota. Conference Presentation. [ bib ]
[27] D. Fuentes. Kalman Filtered MR Temperature Imaging, May 2011. ISMRM 2011 Annual Meeting. Track: MR Guided Focused Ultrasound, Thermotherapy & Thermometry, Montreal, Canada. E-Poster Presentation. [ bib ]
[28] D. Fuentes. High Fidelity Computer Models for Prospective Treatment Planning of Radiofrequency Ablation, April 2011. STM 2011 Annual Meeting. New Orleans, Louisiana. Poster Presentation. [ bib ]
[29] D. Fuentes. Real-Time Model Assisted MR Temperature Imaging for Monitoring LITT Procedures, October 2010. BMES 2010 Annual Meeting. Track: Biomedical Imaging and Optics, Austin, Texas, Oral Presentation. [ bib ]
[30] D. Fuentes. MR Temperature Imaging Validation of a Bioheat Transfer Model for 3D Prospective Planning of LITT, September 2010. Eighth Interventional MRI Symposium. Leipzig, Germany, Poster Presentation. [ bib ]
[31] D. Fuentes. Real-Time Bioheat Transfer Models for Computer Driven MR guided LITT, May 2010. ISMRM, Stockholm, Sweden, E-Poster Presentation. [ bib ]
[32] D. Fuentes. Thermal Image Reconstruction of In Vivo Nanoparticle Concentrations for MR-Guided Laser Induced Thermal Therapy Optimion, February 2010. NanoEngineering for Medicine and Biology, Houston, Texas, Conference Presentation. [ bib ]
[33] D. Fuentes. Computational and MR-guided Patient Specific Thermal Therapy of Cancer, October 2009. Southwest Chapter of the AAPM Fall Meeting. Houston, Texas, Conference Presentation. [ bib ]
[34] J. T. Oden and D. Fuentes. Computational Modeling and Real-Time Control of Patient-Specific Laser Treatment of Cancer, September 2008. Seventh Interventional MRI Symposium. Baltimore, Maryland, Conference Presentation. [ bib | .pdf ]
[35] D. Fuentes. A Data Driven Application System for Laser Treatment of Cancer, July 2007. National Congress on Computational Mechanics. San Francisco, California, Conference Presentation. [ bib | .pdf ]
[36] D. Fuentes. Development of a Computational Paradigm for Laser Treatment of cancer, July 2006. World Congress on Computational Mechanics. Los Angeles, California, Conference Presentation. [ bib | .pdf ]
[37] D. Fuentes. An Application of Goal-oriented Error Estimation to Shock Loaded Elastomeric Materials, July 2005. National Congress on Computational Mechanics. Austin, Texas, Conference Presentation. [ bib | .pdf ]
[38] Maier, Florian and Cressman, Erik N. K. and Berger, Moritz and Fuentes, David and Stafford, R. Jason and MacLellan, Christopher J. and Umathum, Reiner and Nagel, Armin M. . Characterion of Thermochemical Ablation Injections Using 23Na MRI, 2015. ISMRM, Toronta, Canada, E-Poster Presentation. [ bib ]
[39] Bankson, James and Walker, Christopher M. and Stefan, Wolfgang and Fuentes, David and Merritt, Matthew E. and Chen, Yunyun and Malloy, Craig R.and Sherry, A. Dean and Lai, Stephen and Hazle, John . Model-Based Reconstruction of Hyperpolarized [1-13C]-Pyruvate, 2015. ISMRM, Toronta, Canada, Poster Presentation. [ bib ]
[40] D. Fuentes. Image Segmentation for Monitoring Treatment Response, 2015. Radiomics Meeting, Oct 2015, Clearwater, Fl. [ bib ]
[41] R. Madankan and D. Fuentes. Accelerated Model-based Signal Reconstruction for Magnetic Resonance Thermometry Data in Presence of Uncertainties. 13th United States National Congress on Computational Mechanics conference, July 26-30, 2015, San Diego, California. [ bib ]
[42] S. Fahrenholtz and J. Hazle and J. Stafford and A. Shetty and D. Fuentes. Results of cross validation from a trained model for laser induced thermal therapy. 32nd Annual Meeting of the Society for Thermal Medicine, 2015. [ bib ]
[43] K Ahmed and D. Fuentes and J D Hazle and A Qayyum and V L Cox and K M Elsayes. Three Dimensional Volumetric Image Segmentation of the Liver Tips for Clinical Practice and Future Perspectives. Educational Exhibit, Radiological Society of North America. 101th Annual Meeting, November 2015. [ bib ]
[44] MacLellan C and Melancon MP and Salatan F and Qiao Y and Hwang K and D. Fuentes and Stafford RJ. . Magnetic Resonance Based Quantification of Nanoparticle Distribution and Heating in Nanoparticle Mediated Laser Interstitial Thermal Therapy (npLITT). [ bib ]
[45] R. Madankan and S. Fahrenholtz and J. Hazle and J. Stafford and A. Shetty and D. Fuentes. Accurate Modeling of Laser Induced Thermal Therapy in Presence of Heterogeneous Tissue. 32nd Annual Meeting of the Society for Thermal Medicine, 2015. [ bib ]
[46] K Ahmed and D. Fuentes and K M Elsayes. Three Dimensional Volumetric Image Segmentation of the Liver; Tips for Clinical Practice and Future Perspectives. Educational Exhibit, Radiological Society of North America. 102th Annual Meeting, November 2016. [ bib ]
[47] C MacLellan and D Fuentes and H Espinoza and S Prabhu and G Rao and J Weinberg and J Stafford. A Patient Derived Logistic Model of Thermal Dose. 33nd Annual Meeting of the Society for Thermal Medicine, New Orleans 2016. [ bib ]
[48] S Fahrenholtz and R Madankan and A Shetty and S Danish and JD Hazle and RJ Stafford and D Fuentes . Laser ablation prediction via global optimion and cross-validation. 33nd Annual Meeting of the Society for Thermal Medicine, New Orleans 2016. [ bib ]
[49] R Madankan and C MacLellan and S Fahrenholtz and J Hazle and RJ Stafford and D Fuentes . Effect of Different Tissue Segmentation schemes on Precise Treatment Planning of Laser Induced Thermal Therapy: A Retrospective Study. 33nd Annual Meeting of the Society for Thermal Medicine, New Orleans 2016. [ bib ]
[50]  B. W. Carter, P. R. Bhosale, M. G. Elbanan, A. Duran, S. Rao, E. Unlu, J. Sun, W. Wei, D. Fuentes, and W. T.Yang. RECIST 1.1 Criteria and Quantitative CT Image Features to Predict Pathologic Response to Immunotherapy in Melanoma: Preliminary Findings . Radiological Society of North America. 102th Annual Meeting, November 2016. [ bib ]
[51] Sparse source reconstruction for nanomagnetic relaxometry . Loupot, S., Stefan, W., Madankan, R., Mathieu, K., Fuentes, D., Hazle, J.D. Hazle . SIAM Conference on Imaging Science 2016. [ bib ]
[52] Loupot, S., Stefan, W., Medankan, R., Mathieu, K., Fuentes, D., Hazle, J.D. Hazle. Sparse source reconstruction for nanomagnetic relaxometry. International Workshop on Magnetic Particle Imaging, 2016. [ bib ]
[53] Drew Mitchell, Reza Madankan, Samuel Fahrenholtz, Christopher MacLellan, Wolfgang Stefan, Jason Stafford, John Hazle, and David Fuentes. Stability of Information Theoretic K-Space Trajectories for Model-Based MR Thermal Image Reconstruction abstract. SIAM Conference on Imaging Science 2016. [ bib ]
[54] Reza Madankan, Wolfgang Stefan, Christopher MacLellan, Samuel Fahrenholtz, Drew Mitchell, R.J. Stafford, John Hazle, and David Fuentes. Information Theoretic Approach for Accelerated Magnetic Resonance Thermometry in the Presence of Uncertainties abstract. SIAM Conference on Imaging Science 2016. [ bib ]
[55] Wolfgang Stefan and David Fuentes, Erol Yeniaras, Ken-Pin Hwang, John D. Hazle, and R. Jason Stafford. Background Signal Suppression for Magnet Resonance (mr) Phase Images abstract. SIAM Conference on Imaging Science 2016. [ bib ]
[56] Drew Mitchell, Samuel Fahrenholtz, Reza Madankan, Christopher MacLellan, Jason Stafford, John Hazle, and David Fuentes. A Heterogeneous Tissue Model for Treatment Planning in Laser Induced Thermal Therapy. 11th Interventional MRI Symposium. Baltimore, Maryland, 2016 . [ bib ]
[57] Drew Mitchell, Reza Madankan, Samuel Fahrenholtz, Christopher MacLellan, Wolfgang Stefan, Jason Stafford, John Hazle, and David Fuentes. Stability of Information Theoretic K-Space Trajectories for Model-Based MR Thermal Image Reconstruction abstract. Advances in Computational Sciences and Engineering, A conference in honor of the 80th birthday of Prof. J. Tinsley Oden, March 2017. [ bib ]
[58] D. Fuentes, houston, TX; N Munoz; K Maldonado; C Kingsley; K Michel; H Taghavi; R Avritscher. Multiparametric imaging correlations with pathology in a HCC rat model. Radiological Society of North America. 103th Annual Meeting, November 2017. [ bib ]
[59] Ahmed M. Khalaf; David Fuentes; Ahmed O. Kaseb; Manal Hassan; Ali Morshid; John D. Hazle; Khaled M. Elsayes. Hepatocellular Carcinoma (HCC) Response to Transcatheter Arterial Chemoembolion (TACE) and Its Correlation with HCC Tumor Volumes. Radiological Society of North America. 103th Annual Meeting, November 2017. [ bib ]
[60] Ahmed M. Khalaf; Ali Morshid; David Fuentes; Veronica Cox; John D. Hazle; Khaled M. Elsayes; Aliya Qayyum. QUANTITATIVE CT IMAGING FEATURES CORRELATE WITH COLORECTAL CANCER LIVER METASTASES (CLM) MUTATIONAL STATUS. AHPBA 2018 meeting. [ bib ]
[61] D. Fuentes. Prediction of TACE Treatment Response of HCC Patients. Radiomics Meeting, Oct 2017, Clearwater, Fl. [ bib ]
[62] Ahmed M. Khalaf; David Fuentes; Aliya Qayyum; Janio Sruk; Ali Morshid; Ahmed Kaseb; Manal Hassan; John D. Hazle; Khaled M. Elsayes. Quantifying HCC response to TACE using existing criteria and fully automated volumetric segmentation. 2018 American Roentogen Ray Society (ARRS) Annual Meeting. [ bib ]
[63] Ahmed M. Khalaf; Ali Morshid; David Fuentes; Asif Rashid; Ahmed Kaseb; Manal Hassan; Veronica Cox; John D. Hazle; Khaled M. Elsayes; Aliya Qayyum. Role of Radiomic Texture Analysis Mapping in Prediction of Microvascular Invasion in Hepatocellular Carcinoma. 2018 American Roentogen Ray Society (ARRS) Annual Meeting. [ bib ]
[64] Ahmed M. Khalaf; David Fuentes; Ali Morshid; John D. Hazle; Manal Hassan; Khaled M. Elsayes . Decoding of The Patatin-Like Phospholipase Domain Containing 3 (PNPLA3) Gene Phenotype Using Noninvasive Imaging Quantitative Radiomics Approach. 2018 American Roentogen Ray Society (ARRS) Annual Meeting. [ bib ]
[65]  A.M. Khalaf, D. Fuentes, A. Kaseb, M. Hassal, A. Morshid, JD Hazle, and K.M. Elsayes. Hepatocellular Carcinoma (HCC) Response to Transcatheter Arterial Chemoembolion (TACE) and Its Correlation with HCC Tumor Volumes. Radiological Society of North America. 103th Annual Meeting, November 2017. [ bib ]
[66] S.L. Thrower, D. Fuentes, W. Stefan, J. Sovizi, K. Mathieu, J.D. Hazle. Sensitivity and specificity of a sparse reconstruction algorithm for superparamagnetic relaxometry. SPIE Physics of Medical Imaging, 2018. [ bib ]
[67] S.L. Thrower, J. Sovizi, W. Stefan, K. Mathieu, J.D. Hazle, D. Fuentes. 3D Reconstruction of cancer-bound nanoparticles using superparamagnetic relaxometry. SIAM Houston Imaging Sciences Symposium, 2017. [ bib ]
[68] S. Loupot, D. Fuentes, W. Stefan, J. Sovizi, K. Mathieu, J.D. Hazle . A 3D Reconstruction Algorithm for Superparamagnetic Relaxometry. American Association of Physicists in Medicine (AAPM): 2017 Annual Meeting, Denver, Colorado. [ bib ]
[69] D. Fuentes, D. Bastos, V. Beechar, D. Mitchell, C. MacLellan, G. Rao, J. Stafford, S. Prabhu. MRgLITT Induced Perfusion Changes Assessed by DCE-MRI. Intraoperative Imaging Society, Hannover, Germany, Feb. 2017. [ bib ]
[70] Drew Mitchell, Samuel Fahrenholtz, Christopher MacLellan, Jason Stafford, John Hazle, and David Fuentes. A Heterogeneous Tissue Model for Treatment Planning in Laser Induced Thermal Therapy. . Society for Thermal Medicine, Poster Presentation. April 2017. [ bib ]
[71] Drew Mitchell, Samuel Fahrenholtz, Christopher MacLellan, Jason Stafford, John Hazle, and David Fuentes. A Heterogeneous Tissue Model for Treatment Planning in Laser Induced Thermal Therapy. . 14th U.S. National Congress on Computational Mechanics. May 2017. [ bib ]
[72] Drew Mitchell, David Fuentes, Tao Zhang, and Ken-Pin Hwang . Information Theory Optimion of Acquisition Parameters for Improved Synthetic MRI Reconstruction. . The International Society for Optics and Photonics, Medical Imaging. February 2018. [ bib ]

Other Manuscripts
[1] F. Maier, C. J. MacLellan, D. Fuentes, E.N.K Cressman, K. Hwang, J. D. Hazle, and R. J. Stafford. A Multi-Parametric Pulse Sequence for Characterion of Thermochemical Ablation Injections. International Journal of Hyperthermia, 2015. submitted. [ bib ]
[2] R. Madankan, W. Stefan, S. Fahrenholtz, C. MacLellan, J. Hazle, J. Stafford, J. S. 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, J. D. Hazle, and R. J. 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. International Journal of Hyperthermia, 2014. submitted. [ bib ]
[5] D. Fuentes, JT Oden, KR Diller, J Yung, Y Feng, JD Hazle, A Shetty, and RJ Stafford. Computational and MR-guided Patient-Specific Laser Induced Thermal Therapy of Cancer. ICES REPORT 13-33, 2013. [ bib ]
[6] E. Castillo Jr., 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 ]