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[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 optimization 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 optimization 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 Optimization 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 ]
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