David Fuentes

Associate 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 diagnostics and treatment modalities. My current research focuses on exploiting the predictive abilities of sophisticated numerical algorithms for treatment planning, real-time monitoring, and real-time feedback control of image guided therapies. The effort to provide accurate patient specific predictions are based on numerical techniques that span the fields of: Finite Elements, Uncertainty Quantification, Optimion, Control Theory, Parallel Computing, Image Processing, Machine Learning, and Continuum Mechanics.

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]	J. A. Actor, B. Riviere, K. Elsayes, and D. Fuentes. Effects of CT Scanner Type on Deep Learning Segmentation Algorithms. 2021. submitted. [ bib ]
[2]	J. A. Actor, D. Fuentes, and B. Riviere. Robust Regularized Networks in the Presence of Noise. Medical Image Analysis, 2020. in revision. [ bib ]
[3]	Millicent Roach, Newsha Nikzad, Tasadduk Chowdhury, Laura Beretta, David Thomas Alfonso Fuentes, and Eugene Jon Koay. Enhancement pattern mapping for detection of hepatocellular carcinoma in cirrhotic patients: A Feasibility Study. Journal of Hepatocellular Carcinoma, 2020. in preparation. [ bib ]
[4]	A.W. Moawad, A. Ahmed, D. Fuentes, K.J. Blair, J.D. Hazle, M.A. Habra, and K.M. Elsayes. Machine learning-based texture analysis for differentiation of radiologically indeterminate small adrenal tumors on contrast-enhanced CT scans. Frontiers in Oncology, 2020. in review. [ bib ]
[5]	Ahmed W. Moawad, D. Fuentes, Mohamed G. Elbanan, Katherine J Blair, and Khaled M. Elsayes. The Future is Now: Principles and Current and Future Applications of Deep Learning in Medical Imaging. Frontiers in Oncology, 2020. in review. [ bib ]

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

[1] EDH Gates, JS Weinberg, SS Prabhu, JS Lin, J Hamilton, JD Hazle, GN Fuller, V Baladandayuthapani, D. Fuentes, and D Schellingerhout. Estimating local cellular density in glioma using mr imaging data. American Journal of Neuroradiology, 2020. [ bib ]

[2] D. Fuentes, Emily Thompson, Megan Jacobsen, Anna Crouch, Rick R Layman, Beatrice Rivière, and Erik Cressman. Imaging-based characterion of convective tissue properties. International Journal of Hyperthermia, accepted, 2020. [ bib ]

[3] D. Fuentes, Samuel J Fahrenholtz, Chunxiao Guo, Christopher J MacLellan, Rick R Layman, Beatrice Rivière, R Jason Stafford, and Erik Cressman. Mathematical modeling of mass and energy transport for thermoembolion. International Journal of Hyperthermia, 37(1):356--365, 2020. [ bib ]

[4] Ahmed W Moawad, D. Fuentes, Ahmed M Khalaf, Katherine J Blair, Janio Sruk, Aliya Qayyum, John D Hazle, and Khaled M Elsayes. Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolion. Frontiers in Oncology, 10:572, 2020. [ bib ]

[5] Drew Mitchell, Ken-Pin Hwang, James A Bankson, R Jason Stafford, Suchandrima Banerjee, Naoyuki Takei, and D. Fuentes. An information theory model for optimi quantitative magnetic resonance imaging acquisitions. Physics in Medicine & Biology, 2020. [ bib ]

[6] Tracy W Liu, Seth T Gammon, Ping Yang, D. Fuentes, and David Piwnica-Worms. Myeloperoxidase-produced HOCl is a paracrine effector linking myeloid cells to NF-κB signaling in melanoma, mediating anti-tumor responses during early melanoma progression, 2020. [ bib ]

[7] Dhiego Chaves de Almeida Bastos, D. Fuentes, Jeffrey Traylor, Jeffrey Weinberg, Vinodh A Kumar, Jason Stafford, Jing Li, Ganesh Rao, and Sujit S Prabhu. The use of laser interstitial thermal therapy in the treatment of brain metastases: a literature review. International Journal of Hyperthermia, 37(2):53--60, 2020. [ bib ]

[8] Dhiego Chaves de Almeida Bastos, Jeffrey Weinberg, Vinodh A Kumar, D. Fuentes, Jason Stafford, Jing Li, Ganesh Rao, and Sujit S Prabhu. Laser Interstitial Thermal Therapy in the treatment of brain metastases and radiation necrosis. Cancer Letters, 2020. [ bib ]

[9] Dhiego Chaves de Almeida Bastos, Ganesh Rao, Isabella Claudia Glitza Oliva, Jonathan M Loree, D. Fuentes, R Jason Stafford, Vivek B Beechar, Jeffrey S Weinberg, Komal Shah, Vinodh A Kumar, et al. Predictors of local control of brain metastasis treated with laser interstitial thermal therapy. Neurosurgery, 87(1):112--122, 2020. [ bib ]

[10] Costas Papadopoulos, Eleni K Efthimiadou, Michael Pissas, D. Fuentes, Nikolaos Boukos, Vassilis Psycharis, George Kordas, Vassilios C Loukopoulos, and George C Kagadis. Magnetic fluid hyperthermia simulations in evaluation of SAR calculation methods. Physica Medica, 71:39--52, 2020. [ bib ]

[11] Rajarajeswari Muthusivarajan, William J Allen, Ashok D Pehere, Konstantin V Sokolov, and D. Fuentes. Role of alkylated residues in the tetrapeptide self-assembly—A molecular dynamics study. Journal of Computational Chemistry, 41(31):2634--2640, 2020. [ bib ]

[12] EDH Gates, JS Lin, JS Weinberg, SS Prabhu, J Hamilton, JD Hazle, GN Fuller, V Baladandayuthapani, D. Fuentes, and D Schellingerhout. Imaging-Based Algorithm for the Local Grading of Glioma. American Journal of Neuroradiology, 41(3):400--407, 2020. [ bib ]

[13] 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. IEEE Transactions on Biomedical Engineering, 2019. [ bib ]

[14] SL Thrower, SK Kandala, D. Fuentes, W Stefan, N Sowko, MX Huang, K Mathieu, and JD Hazle. A compressed sensing approach to immobilized nanoparticle localion for superparamagnetic relaxometry. Physics in Medicine & Biology, 64(19):194001, 2019. [ bib ]

[15] Evan DH Gates, Jie Yang, Kaka Fukumura, Jonathan S Lin, Jeffrey S Weinberg, Sujit S Prabhu, Lihong Long, D. Fuentes, Erik P Sulman, Jason T Huse, et al. Spatial distance correlates with genetic distance in diffuse glioma. Frontiers in oncology, 9, 2019. [ bib ]

[16] A. Morshid, K. Elsayes A.M. Khalaf, J. Yu, A. Kaseb, M. Hassan, A. Mahvash, Z. Wang, J.D. Hazle, and D. Fuentes. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolion. Radiology: Artificial Intelligence, 1(5), 2019. Cover Page. [ bib ]

[17] J. Traylor, D. Bastos, D. Fuentes, M. Muir, R. Patel, VA. Kumar, RJ. Stafford, G. Rao, and SS Prabhu. Dynamic contrast-enhanced MRI in patients with brain metastases receiving laser interstitial thermal therapy. American Journal of Neuroradiology, 40(9):1451--1457, 2019. [ bib ]

[18] Dhiego Bastos, Ganesh Rao, Isabella Claudia Glitza, D. Fuentes, Jason Stafford, Jeffrey Weinberg, Komal Shah, Vinodh A Kumar, and Sujit S Prabhu. Predictors of Local Control of Brain Metastasis Treated with Laser Interstitial Thermal Therapy. J. of Neurosurgery, 2019. [ bib ]

[19] M. Elmohr, D. Fuentes, A. Morshid, A. Kaseb, M. Hassan, E Gates, J. Sruk, J.D. Hazle, and K. Elsayes. Machine learning-based texture analysis for differentiation of large adrenal cortical tumors on computed tomography. Clinical radiology, 74(10):818, 2019. [ bib ]

[20] A. Khalaf, D. Fuentes, A. Morshid, M. Elmohr, A. Kaseb, M. Hassan, J. Sruk, J.D. Hazle, and K. Elsayes. Hepatocellular Carcinoma (HCC) Response to Transcatheter Arterial Chemoembolion (TACE) Using Automatically Generated Pre-Therapeutic Tumor Volumes by a Random Forest-Based Segmentation Protocol. Clinical radiology, 74(12):974--e13, 2019. [ bib ]

[21] C Walker, D. Fuentes, Peder Larson, Vikas Kundra, Daniel Vigneron, and James Bankson. Effects of Excitation Angle Strategy on Quantitative Analysis of Hyperpolarized Pyruvate. Magnetic resonance in medicine, 2019. [ bib ]

[22] Evan DH Gates, Jonathan S Lin, Jeffrey S Weinberg, Jackson Hamilton, Sujit S Prabhu, John D Hazle, Gregory N Fuller, Veera Baladandayuthapani, D. Fuentes, and Dawid Schellingerhout. Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging. Neuro-Oncology, 21(4):527--536, 2019. [ bib ]

[23] 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. J. of Computer Assisted Tomography, 43(3):499--506, 2019. [ bib ]

[24] 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, 3:1--10, 2019. [ bib ]

[25] 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, 13(10):e0205003, 2018. [ bib ]

[26] 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 ]

[27] D. Fuentes, Nina M Muñoz, Chunxiao Guo, Ura Polak, Adeeb A Minhaj, William J Allen, Michael C Gustin, and Erik NK Cressman. A molecular dynamics approach towards evaluating osmotic and thermal stress in the extracellular environment. International Journal of Hyperthermia, pages 1--9, 2018. [ bib ]

[28] 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, 34(7):943--952, 2018. [ bib ]

[29] 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, 137(1):57--65, 2018. [ bib ]

[30] 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 ]

[31] 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 ]

[32] 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 ]

[33] 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 ]

[34] 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 ]

[35] 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 ]

[36] 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 ]

[37] 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 ]

[38] 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 ]

[39] 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 ]

[40] 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 ]

[41] 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 ]

[42] 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 ]

[43] 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 ]

[44] 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 ]

[45] 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 ]

[46] 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 ]

[47] 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 ]

[48] 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 ]

[49] 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 ]

[50] 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 ]

[51] 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 ]

[52] 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 ]

[53] 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 ]

[54] 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 ]

[55] 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 ]

[56] 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 ]

[57] 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 ]

[58] 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 ]

[59] 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 ]

[60] 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 ]

[61] 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] J. A. Actor, D. Fuentes, and B. Riviere. Identification of Kernels in a Convolutional Neural Network: Connections Between Level Set Equation and Deep Learning for Image Segmentation. In Medical Imaging 2020: Computer-Aided Diagnosis. International Society for Optics and Photonics, 2020. [ bib ]

[2] E. Gates, J. Lin, J. Weinberg, S. Prabhu, J. Hamilton, J. Hazle, G. Fuller, V. Baladandayuthapani, D. Fuentes, and D. Schellingerhout. Advanced magnetic resonance imaging based algorithm for local grading of glioma. In Medical Imaging 2020: Computer-Aided Diagnosis, page 11314. International Society for Optics and Photonics, 2020. [ bib ]

[3] E Castillo and D Fuentes. Data driven deformable image registration for extreme deformations. In MEDICAL PHYSICS, volume 46, pages E572--E572. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]

[4] D Mitchell, K Hwang, R Stafford, J Bankson, and D Fuentes. Calibration of synthetic mri acquisition parameters through information theory modeling. In MEDICAL PHYSICS, volume 46, pages E128--E128. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]

[5] C Owens, D Rhee, D Fuentes, C Peterson, J Li, M Salehpour, L. Court, and J Yang. Automated detection and segmentation of lung tumors using deep learning. In MEDICAL PHYSICS, volume 46, pages E447--E448. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]

[6] C Papadopoulos, G Loudos, D. Fuentes, and GC Kagadis. Accuracy optimion in magnetic fluid hyperthermia (mfh) simulations. In MEDICAL PHYSICS, volume 46, pages E436--E436. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]

[7] Medhi Baqri, Sri Kandala, and D. Fuentes. A pre-processing filter to improve superparamagnetic iron oxide nanoparticle (SPION)-based early tumor detection. Journal of Physics: Conference Series (IOP), 2019. [ bib ]

[8] Evan Gates, J. Gregory Pauloski, Dawid Schellingerhout, and D. Fuentes. Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction. 2018 International MICCAI BraTS Challenge, 2018. [ bib ]

[9] 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 ]

[10] 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 ]

[11] 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 ]

[12] 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 ]

[13] Dhiego Chaves De Almeida Bastos, Ganesh Rao, Jeffrey S Weinberg, David Thomas Alfonso Fuentes, Jason Stafford, and Sujit S Prabhu. Predictors of local control of post srs brain metastasis treated with litt. Neuro-Oncology, 19(Suppl 6):vi241, 2017. [ bib ]

[14] R Madankan, C MacLellan, S Fahrenholtz, J Weinberg, G Rao, J Hazle, R Stafford, and D. Fuentes. Su-f-j-03: Treatment planning for laser ablation therapy in presence of heterogeneous tissue: A retrospective study. Medical physics, 43(6Part8):3406--3406, 2016. [ bib ]

[15] 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 ]

[16] 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 ]

[17] 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 ]

[18] 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 ]

[19] 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 ]

[20] 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 ]

[21] 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 ]

[22] 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 ]

[23] J. T. Oden, D. Fuentes, J. Bass, and Y. Feng. Dynamic-Data-Driven Systems Aid Patient-Specific Cancer Therapy. spie.org, 2008. [ bib | DOI ]

[24] 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 ]

[25] 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] Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Roi, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629, 2018. [ bib ]

[2] Javad Sovizi, Sara L Thrower, Kelsey B Mathieu, Wolfgang Stefan, John D Hazle, and D. Fuentes. Data-driven Image Reconstruction in Superparamagnetic Relaxometry. 2017. [ bib ]

[3] 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 ]

[4] 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 ]

[5] 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 ]

[6] 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 ]

[7] 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 ]

[8] 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 ]

[1]	EDH Gates, JS Weinberg, SS Prabhu, JS Lin, J Hamilton, JD Hazle, GN Fuller, V Baladandayuthapani, D. Fuentes, and D Schellingerhout. Estimating local cellular density in glioma using mr imaging data. American Journal of Neuroradiology, 2020. [ bib ]
[2]	D. Fuentes, Emily Thompson, Megan Jacobsen, Anna Crouch, Rick R Layman, Beatrice Rivière, and Erik Cressman. Imaging-based characterion of convective tissue properties. International Journal of Hyperthermia, accepted, 2020. [ bib ]
[3]	D. Fuentes, Samuel J Fahrenholtz, Chunxiao Guo, Christopher J MacLellan, Rick R Layman, Beatrice Rivière, R Jason Stafford, and Erik Cressman. Mathematical modeling of mass and energy transport for thermoembolion. International Journal of Hyperthermia, 37(1):356--365, 2020. [ bib ]
[4]	Ahmed W Moawad, D. Fuentes, Ahmed M Khalaf, Katherine J Blair, Janio Sruk, Aliya Qayyum, John D Hazle, and Khaled M Elsayes. Feasibility of Automated Volumetric Assessment of Large Hepatocellular Carcinomas' Responses to Transarterial Chemoembolion. Frontiers in Oncology, 10:572, 2020. [ bib ]
[5]	Drew Mitchell, Ken-Pin Hwang, James A Bankson, R Jason Stafford, Suchandrima Banerjee, Naoyuki Takei, and D. Fuentes. An information theory model for optimi quantitative magnetic resonance imaging acquisitions. Physics in Medicine & Biology, 2020. [ bib ]
[6]	Tracy W Liu, Seth T Gammon, Ping Yang, D. Fuentes, and David Piwnica-Worms. Myeloperoxidase-produced HOCl is a paracrine effector linking myeloid cells to NF-κB signaling in melanoma, mediating anti-tumor responses during early melanoma progression, 2020. [ bib ]
[7]	Dhiego Chaves de Almeida Bastos, D. Fuentes, Jeffrey Traylor, Jeffrey Weinberg, Vinodh A Kumar, Jason Stafford, Jing Li, Ganesh Rao, and Sujit S Prabhu. The use of laser interstitial thermal therapy in the treatment of brain metastases: a literature review. International Journal of Hyperthermia, 37(2):53--60, 2020. [ bib ]
[8]	Dhiego Chaves de Almeida Bastos, Jeffrey Weinberg, Vinodh A Kumar, D. Fuentes, Jason Stafford, Jing Li, Ganesh Rao, and Sujit S Prabhu. Laser Interstitial Thermal Therapy in the treatment of brain metastases and radiation necrosis. Cancer Letters, 2020. [ bib ]
[9]	Dhiego Chaves de Almeida Bastos, Ganesh Rao, Isabella Claudia Glitza Oliva, Jonathan M Loree, D. Fuentes, R Jason Stafford, Vivek B Beechar, Jeffrey S Weinberg, Komal Shah, Vinodh A Kumar, et al. Predictors of local control of brain metastasis treated with laser interstitial thermal therapy. Neurosurgery, 87(1):112--122, 2020. [ bib ]
[10]	Costas Papadopoulos, Eleni K Efthimiadou, Michael Pissas, D. Fuentes, Nikolaos Boukos, Vassilis Psycharis, George Kordas, Vassilios C Loukopoulos, and George C Kagadis. Magnetic fluid hyperthermia simulations in evaluation of SAR calculation methods. Physica Medica, 71:39--52, 2020. [ bib ]
[11]	Rajarajeswari Muthusivarajan, William J Allen, Ashok D Pehere, Konstantin V Sokolov, and D. Fuentes. Role of alkylated residues in the tetrapeptide self-assembly—A molecular dynamics study. Journal of Computational Chemistry, 41(31):2634--2640, 2020. [ bib ]
[12]	EDH Gates, JS Lin, JS Weinberg, SS Prabhu, J Hamilton, JD Hazle, GN Fuller, V Baladandayuthapani, D. Fuentes, and D Schellingerhout. Imaging-Based Algorithm for the Local Grading of Glioma. American Journal of Neuroradiology, 41(3):400--407, 2020. [ bib ]
[13]	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. IEEE Transactions on Biomedical Engineering, 2019. [ bib ]
[14]	SL Thrower, SK Kandala, D. Fuentes, W Stefan, N Sowko, MX Huang, K Mathieu, and JD Hazle. A compressed sensing approach to immobilized nanoparticle localion for superparamagnetic relaxometry. Physics in Medicine & Biology, 64(19):194001, 2019. [ bib ]
[15]	Evan DH Gates, Jie Yang, Kaka Fukumura, Jonathan S Lin, Jeffrey S Weinberg, Sujit S Prabhu, Lihong Long, D. Fuentes, Erik P Sulman, Jason T Huse, et al. Spatial distance correlates with genetic distance in diffuse glioma. Frontiers in oncology, 9, 2019. [ bib ]
[16]	A. Morshid, K. Elsayes A.M. Khalaf, J. Yu, A. Kaseb, M. Hassan, A. Mahvash, Z. Wang, J.D. Hazle, and D. Fuentes. A machine learning model to predict hepatocellular carcinoma response to transcatheter arterial chemoembolion. Radiology: Artificial Intelligence, 1(5), 2019. Cover Page. [ bib ]
[17]	J. Traylor, D. Bastos, D. Fuentes, M. Muir, R. Patel, VA. Kumar, RJ. Stafford, G. Rao, and SS Prabhu. Dynamic contrast-enhanced MRI in patients with brain metastases receiving laser interstitial thermal therapy. American Journal of Neuroradiology, 40(9):1451--1457, 2019. [ bib ]
[18]	Dhiego Bastos, Ganesh Rao, Isabella Claudia Glitza, D. Fuentes, Jason Stafford, Jeffrey Weinberg, Komal Shah, Vinodh A Kumar, and Sujit S Prabhu. Predictors of Local Control of Brain Metastasis Treated with Laser Interstitial Thermal Therapy. J. of Neurosurgery, 2019. [ bib ]
[19]	M. Elmohr, D. Fuentes, A. Morshid, A. Kaseb, M. Hassan, E Gates, J. Sruk, J.D. Hazle, and K. Elsayes. Machine learning-based texture analysis for differentiation of large adrenal cortical tumors on computed tomography. Clinical radiology, 74(10):818, 2019. [ bib ]
[20]	A. Khalaf, D. Fuentes, A. Morshid, M. Elmohr, A. Kaseb, M. Hassan, J. Sruk, J.D. Hazle, and K. Elsayes. Hepatocellular Carcinoma (HCC) Response to Transcatheter Arterial Chemoembolion (TACE) Using Automatically Generated Pre-Therapeutic Tumor Volumes by a Random Forest-Based Segmentation Protocol. Clinical radiology, 74(12):974--e13, 2019. [ bib ]
[21]	C Walker, D. Fuentes, Peder Larson, Vikas Kundra, Daniel Vigneron, and James Bankson. Effects of Excitation Angle Strategy on Quantitative Analysis of Hyperpolarized Pyruvate. Magnetic resonance in medicine, 2019. [ bib ]
[22]	Evan DH Gates, Jonathan S Lin, Jeffrey S Weinberg, Jackson Hamilton, Sujit S Prabhu, John D Hazle, Gregory N Fuller, Veera Baladandayuthapani, D. Fuentes, and Dawid Schellingerhout. Guiding the first biopsy in glioma patients using estimated Ki-67 maps derived from MRI: conventional versus advanced imaging. Neuro-Oncology, 21(4):527--536, 2019. [ bib ]
[23]	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. J. of Computer Assisted Tomography, 43(3):499--506, 2019. [ bib ]
[24]	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, 3:1--10, 2019. [ bib ]
[25]	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, 13(10):e0205003, 2018. [ bib ]
[26]	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 ]
[27]	D. Fuentes, Nina M Muñoz, Chunxiao Guo, Ura Polak, Adeeb A Minhaj, William J Allen, Michael C Gustin, and Erik NK Cressman. A molecular dynamics approach towards evaluating osmotic and thermal stress in the extracellular environment. International Journal of Hyperthermia, pages 1--9, 2018. [ bib ]
[28]	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, 34(7):943--952, 2018. [ bib ]
[29]	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, 137(1):57--65, 2018. [ bib ]
[30]	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 ]
[31]	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 ]
[32]	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 ]
[33]	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 ]
[34]	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 ]
[35]	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 ]
[36]	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 ]
[37]	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 ]
[38]	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 ]
[39]	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 ]
[40]	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 ]
[41]	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 ]
[42]	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 ]
[43]	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 ]
[44]	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 ]
[45]	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 ]
[46]	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 ]
[47]	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 ]
[48]	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 ]
[49]	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 ]
[50]	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 ]
[51]	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 ]
[52]	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 ]
[53]	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 ]
[54]	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 ]
[55]	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 ]
[56]	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 ]
[57]	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 ]
[58]	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 ]
[59]	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 ]
[60]	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 ]
[61]	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 ]

[1]	J. A. Actor, D. Fuentes, and B. Riviere. Identification of Kernels in a Convolutional Neural Network: Connections Between Level Set Equation and Deep Learning for Image Segmentation. In Medical Imaging 2020: Computer-Aided Diagnosis. International Society for Optics and Photonics, 2020. [ bib ]
[2]	E. Gates, J. Lin, J. Weinberg, S. Prabhu, J. Hamilton, J. Hazle, G. Fuller, V. Baladandayuthapani, D. Fuentes, and D. Schellingerhout. Advanced magnetic resonance imaging based algorithm for local grading of glioma. In Medical Imaging 2020: Computer-Aided Diagnosis, page 11314. International Society for Optics and Photonics, 2020. [ bib ]
[3]	E Castillo and D Fuentes. Data driven deformable image registration for extreme deformations. In MEDICAL PHYSICS, volume 46, pages E572--E572. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]
[4]	D Mitchell, K Hwang, R Stafford, J Bankson, and D Fuentes. Calibration of synthetic mri acquisition parameters through information theory modeling. In MEDICAL PHYSICS, volume 46, pages E128--E128. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]
[5]	C Owens, D Rhee, D Fuentes, C Peterson, J Li, M Salehpour, L. Court, and J Yang. Automated detection and segmentation of lung tumors using deep learning. In MEDICAL PHYSICS, volume 46, pages E447--E448. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]
[6]	C Papadopoulos, G Loudos, D. Fuentes, and GC Kagadis. Accuracy optimion in magnetic fluid hyperthermia (mfh) simulations. In MEDICAL PHYSICS, volume 46, pages E436--E436. WILEY 111 RIVER ST, HOBOKEN 07030-5774, NJ USA, 2019. [ bib ]
[7]	Medhi Baqri, Sri Kandala, and D. Fuentes. A pre-processing filter to improve superparamagnetic iron oxide nanoparticle (SPION)-based early tumor detection. Journal of Physics: Conference Series (IOP), 2019. [ bib ]
[8]	Evan Gates, J. Gregory Pauloski, Dawid Schellingerhout, and D. Fuentes. Glioma Segmentation and a Simple Accurate Model for Overall Survival Prediction. 2018 International MICCAI BraTS Challenge, 2018. [ bib ]
[9]	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 ]
[10]	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 ]
[11]	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 ]
[12]	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 ]
[13]	Dhiego Chaves De Almeida Bastos, Ganesh Rao, Jeffrey S Weinberg, David Thomas Alfonso Fuentes, Jason Stafford, and Sujit S Prabhu. Predictors of local control of post srs brain metastasis treated with litt. Neuro-Oncology, 19(Suppl 6):vi241, 2017. [ bib ]
[14]	R Madankan, C MacLellan, S Fahrenholtz, J Weinberg, G Rao, J Hazle, R Stafford, and D. Fuentes. Su-f-j-03: Treatment planning for laser ablation therapy in presence of heterogeneous tissue: A retrospective study. Medical physics, 43(6Part8):3406--3406, 2016. [ bib ]
[15]	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 ]
[16]	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 ]
[17]	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 ]
[18]	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 ]
[19]	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 ]
[20]	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 ]
[21]	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 ]
[22]	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 ]
[23]	J. T. Oden, D. Fuentes, J. Bass, and Y. Feng. Dynamic-Data-Driven Systems Aid Patient-Specific Cancer Therapy. spie.org, 2008. [ bib \| DOI ]
[24]	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 ]
[25]	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 ]

[1]	Spyridon Bakas, Mauricio Reyes, Andras Jakab, Stefan Bauer, Markus Rempfler, Alessandro Crimi, Russell Takeshi Shinohara, Christoph Berger, Sung Min Ha, Martin Roi, et al. Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the brats challenge. arXiv preprint arXiv:1811.02629, 2018. [ bib ]
[2]	Javad Sovizi, Sara L Thrower, Kelsey B Mathieu, Wolfgang Stefan, John D Hazle, and D. Fuentes. Data-driven Image Reconstruction in Superparamagnetic Relaxometry. 2017. [ bib ]
[3]	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 ]
[4]	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 ]
[5]	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 ]
[6]	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 ]
[7]	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 ]
[8]	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 ]

[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 ]

David Fuentes

Associate 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

Associate 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