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[1]
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Jonas A. Actor, Beatrice Riviere, and D. Fuentes.
Stabilized Image Segmentation in the Presence of Noise, October
2020.
30th Annual Keck Center Research Conference: A Science Odyssey
(2020). Poster Talk.
[ bib ]
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[2]
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E. Gates, A. Celaya, D. Schellingerhout, and D. Fuentes.
Automated Cerebrospinal Fluid ROI Selection on Brain Magnetic
Resonance Images, October 2020.
30th Annual Keck Center Research Conference: A Science Odyssey
(2020). Poster Talk.
[ bib ]
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[3]
|
E. Gates, D. Suki, J. Weinberg, S. Prabhu, D. Fuentes, and
D. Schellingerhout.
Predicting Local Brain Tumor Pathology to Inform Prognosis and
Improve Surgery, October 2020.
30th Annual Keck Center Research Conference: A Science Odyssey
(2020). Plenary Talk.
[ bib ]
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[4]
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K Huang, P Das, L Zhang, M Amirmazaheri, C Nguyen, D Rhee, T Netherton,
S Beddar, T Briere, D Fuentes, E Holliday, L Court, and C Cardenas.
Incorporating GTV Information in a Multi-Stage Process to Improve
Automatically Generated Field Apertures for Rectal Cancer Radiotherapy, July
2020.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Virtual.
[ bib ]
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[5]
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E. Gates, D. Suki, J. Weinberg, S. Prabhu, D. Fuentes, and
D. Schellingerhout.
Prognostic Value of Imaging-Based Estimates of Glioma Pathology Pre-
and Post-Surgery, July 2020.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Virtual.
[ bib ]
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[6]
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E. Gates, D. Suki, J. Weinberg, S. Prabhu, D. Fuentes, and
D. Schellingerhout.
Prognostic Value of Imaging-Based Estimates of Glioma Pathology Pre-
and Post-Surgery, June 2020.
NLM national trainee conference, virtual. Plenary Talk.
[ bib ]
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[7]
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Mohamedi Elbanan, Khaled Elsayes, D. Fuentes, Kareem Elfatairy, Ahmed
Moawad, and Adam Kaye.
Convolutional Neural Networks for Medical Image Analysis, What the
Future Radiologist Needs to Know, May 2020.
American Roentogen Ray Society (ARRS) Annual Meeting.
[ bib ]
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[8]
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E. Gates, J. Weinberg, S. Lin, S. Prabhu, J Hamilton, J. D. Hazle, G. N.
Fuller, V. Baladandayuthapani, D. Fuentes, and D. Schellingerhout.
Combination of MRI Sequences Predicts Cellular Density in Glioma.,
March 2020.
American Medical Informatics Association Summit, Houston, Tx.
[ bib ]
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[9]
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Ahmed Moawad, D. Fuentes, Mohamed Elbanan, Katie Blair, and Khaled
Elsayes.
Artificial intelligence Primer for Beginners; Definitions,
Applications and Simple Explanations in Abdominal imaging, March 2020.
SAR Annual Scientific Meeting.
[ bib ]
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[10]
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Mohab Elmohr, D. Fuentes, Katie Blair, Mohamed Elbanan, Aya Tawfik, and
Khaled Elsayes.
Radiomics and the Adrenal Gland: An Overview, March 2020.
SAR Annual Scientific Meeting.
[ bib ]
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[11]
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Mohab Elmohr, D. Fuentes, Katie Blair, Ahmed Moawad, Jhn Hazle, and
Khaled Elsayes.
Liver Imaging in the Era of Artificial Intelligence: Current
Indications and Future Prespective, March 2020.
SAR Annual Scientific Meeting.
[ bib ]
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[12]
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M. Baqri, S. Kandala, and D. Fuentes.
A preprocessing filter to improve superparamagnetic iron oxide
nanoparticle (SPION) – based early tumor detection, March 2020.
[ bib ]
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[13]
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E. Gates, J. Weinberg, J. Lin, S. Prabhu, D. Fuentes, and
D. Schellingerhout.
Combination of MRI Sequences Predicts Cellular Density in Glioma.,
March 2020.
American Medical Informatics Association Virtual Summit, Houston, TX.
[ bib ]
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[14]
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Jonas A. Actor, Beatrice Riviere, and D. Fuentes.
Stabilizing Deep Convolutional Neural Networks for Image
Segmentation., March 2020.
In Ken Kennedy Institute Rice Oil and Gas High Performance Computing
Conference 2020 (2020). Poster.
[ bib ]
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[15]
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Tracy Liu, S. Gammon, 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 ]
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[16]
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E. Gates, D. Fuentes, and D. Schellingerhout.
Combination of Multicontrast MRI Estimates Local Glioma Malignancy,
November 2019.
Second Annual Meeting, SIAM TX-LA Section, November 1-3, 2019
Southern Methodist University, Dallas, TX, USA .
[ bib ]
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[17]
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M. Baqri, S. Kandala, and D. Fuentes.
A preprocessing filter to improve superparamagnetic iron oxide
nanoparticle (SPION) – based early tumor detection, November 2019.
Applied Nanotechnology and Nanoscience International Conference.
[ bib ]
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[18]
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Aliya Qayyum, Sidra J. Tayyab, D. Fuentesand Rebecca K. Marcus, A. R.
Klekers, Rizwan Aslam, Jia Sun, and Thomas A. Aloia.
Radiomics and Enhancement Ratio in Colorectal Cancer (CRC) Liver
Metastases: Determination of Mutational Status, November 2019.
Educational Exhibit, Radiological Society of North America. 105th
Annual Meeting.
[ bib ]
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[19]
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Mohab Elmohr, D. Fuentes, Sarah M. Palmquist, Ahmed Moawad, John D.
Hazle, and Khaled M. Elsayes.
Artificial Intelligence and Liver Segmentation: Current Applications
and Future Directions, November 2019.
Educational Exhibit, Radiological Society of North America. 105th
Annual Meeting.
[ bib ]
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[20]
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Ahmed Moawad, D. Fuentes, Mohamed Elbanan, Brett W. Carter, Naveen Garg,
Gaiane M. Rauch, David J. Vining, and Khaled M. Elsayes.
Artificial Intelligence in Diagnostic Imaging: Current Applications
and Future Perspective, November 2019.
Educational Exhibit, Radiological Society of North America. 105th
Annual Meeting.
[ bib ]
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[21]
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Ahmed Moawad, D. Fuentes, Mohamed Elbanan, Katherine J. Blair, Layla
Shere, Kundan A. Rao, and Khaled M. Elsayes.
Artificial Intelligence Primer for Beginners: Definitions,
Applications, and Simple Explanations in Radiology, Cum Laude, November
2019.
Educational Exhibit, Radiological Society of North America. 105th
Annual Meeting.
[ bib ]
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[22]
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Mohab Elmohr, D. Fuentes, Katherine J. Blair, Mohamed Elbanan, Ayahallah
Ahmed, and Khaled M. Elsayes.
Adrenal Imaging in the Era of Radiomics: Current Indications and
Future Perspective, November 2019.
Educational Exhibit, Radiological Society of North America. 105th
Annual Meeting.
[ bib ]
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[23]
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Jonas A. Actor, Beatrice Riviere, and D. Fuentes.
Kernel Analysis of a Neural Network for Liver Segmentation.,
November 2019.
29th Annual Keck Center Research Conference : Precision Environmental
Health.
[ bib ]
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[24]
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Jonas A. Actor, Beatrice Riviere, and D. Fuentes.
Efficient and Robust CT Image Segmentation with a Level Set
Network, November 2019.
AMIA Annual Symposium, Washington, D.C.
[ bib ]
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[25]
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Jonas A. Actor, D. Fuentes, and Beatrice Riviere.
Identification of Kernels in a Convolutional Neural Network:
Connections Between Level Set Equation and Deep Learning for Image
Segmentation., October 2019.
Ken Kennedy Institute Rice Data Science Conference.
[ bib ]
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[26]
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E. McCollum, Jonas A. Actor, E Gates, and D. Fuentes.
Opening the Black Box of a Convolutional Neural Network Used for
Brain Tumor Segmentation., August 2019.
CPRIT CURE Summer Undergraduate Research Program.
[ bib ]
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[27]
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E. Gates, D Schellingerhout, and D. Fuentes.
Validation of Gaussian Process Uncertainty Estimates in Registration
of MRI Datasets, July 2019.
Second Workshop for Research Challenges and Opportunities at the
Interface of Machine Learning and Uncertainty Quantification, Los Angeles,
CA.
[ bib ]
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[28]
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Drew Mitchell, Ken Hwang, Jason Stafford, James Bankson, and D. Fuentes.
Conditional Mutual Information to Quantify Information Content of
Sequential Synthetic MRI Acquisitions, July 2019.
15th U.S. National Congress on Computational Mechanics.
[ bib ]
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[29]
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Jonas A. Actor, Beatrice Riviere, and D. Fuentes.
A Comparison of Image Segmentation Methods., July 2019.
SIAM Gene Golub Student Summer School.
[ bib ]
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[30]
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E. D. H. Gates, J. S. Lin, J. S. Weinberg, D. Fuentes, and
D. Schellingerhout.
Guiding the First Biopsy in Glioma Patients Using Estimated Ki-67
Maps Derived from MRI, June 2019.
National Library of Medicine Training Conference, Indianapolis IN.
[ bib ]
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[31]
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D. Fuentes.
Mathematical model developments for thermoembolization, May 2019.
STM 2019 Annual Meeting. St. Pete Beach, Florida.
[ bib ]
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[32]
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Drew Mitchell, D. Fuentes, Jason Stafford, James Bankson, and Ken-Pin
Hwang.
Information Quantification of Subsequent Acquisitions for Minimizing
Synthetic MRI Reconstruction Uncertainty, May 2019.
ISMRM, Montreal, Canada, E-Poster Presentation.
[ bib ]
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[33]
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Ken-Pin Hwang, Marcel Warntjes, Naoyuki Takei, Suchandrima Banerjee, Drew
Mitchell, R. Stafford, Linda Chi, and D. Fuentes.
Effects of acquisition and compressed sensing reconstruction
parameters on 3D-QALAS multi-parameter quantitation and synthetic imaging of
the brain, May 2019.
ISMRM, Montreal, Canada, E-Poster Presentation.
[ bib ]
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[34]
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M.M. Elmohr, D. Fuentes, M.A. Habra, P.R. Bhosale, A.A. Qayyum, E. Gates,
A.I. Morshid, A.W. Moawad, J.D. Hazle, and K.M. Elsayes.
Machine learning-based texture analysis for differentiation of large
adrenal cortical tumors on CT, April 2019.
The annual Global Academic Programs (GAP) Conference 2019; Houston,
TX.
[ bib ]
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[35]
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JI Traylor, E Gates, D. Fuentes, SS Prabhu, and G Rao.
Area under the time-to-Signal intensity curve from dynamic
contrast-enhanced magnetic resonance imaging correlates with
time-to-progression in patients with brain metastases receiving laser
interstitial thermal therapy, April 2019.
AANS Annual Meeting, San Diego, CA.
[ bib ]
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[36]
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Jonas A. Actor, Beatrice Riviere, and D. Fuentes.
A Comparison of Image Segmentation Methods, March 2019.
Rice Oil and Gas High Performance Computing Conference, Houston, TX.
[ bib ]
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[37]
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E.Gates, J. Lin, J. Weinberg, J. Hamilton, S. Prabhu, J. Hazle, G. Fuller, V.
Baladandayuthapani, D. Fuentes, and D. Schellingerhout.
Estimated Maps of Proliferative Activity Present Alternative Targets
For Initial Glioma Biopsy, March 2019.
Intraoperative Imaging Society, Houston, Tx.
[ bib ]
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[38]
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A. Morshid, A.M. Khalaf, J. Yu A. Kaseb, M. Hassal, , D. Fuentes, and
K.M. Elsayes.
Machine Learning Models for Prediction of Hepatocellular Carcinoma
Response to Transcatheter Arterial Chemoembolization, November 2018.
Radiological Society of North America. 104th Annual Meeting.
[ bib ]
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[39]
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J. Gregory Pauloski, Zhao Zhang, William J. Allen, John D. Hazle, and
D. Fuentes.
Optimizing Deep Learning Methods for Image Segmentation with
Distributed Training, October 2018.
2018 TACC Symposium for Texas Researchers (TACCSTER), Austin,
Texas.
[ bib ]
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[40]
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S. Kandala, S.L. Thrower, K. Mathieu, W. Stefan, J. D. Hazle, and D.
Fuentes.
Deep learning based classification method for automatic detection of
bad trials in magnetic relaxometry, October 2018.
World Molecular Imaging Congress (WMIC) 2018, Annual Meeting,
Seattle, WA.
[ bib ]
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[41]
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E. A. B. F. Lima, D. Fuentes, A. I. Morshid, N. M. Rylander, J. T. Oden,
and T. E. Yankeelov.
Development and calibration of patient-specific tumor growth model
for predicting the response of hepatocellular carcinoma, October 2018.
30th Anniversary AACR Special Conference Convergence: Artificial
Intelligence, Big Data, and Prediction in Cancer.
[ bib ]
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[42]
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Jonas A. Actor, Beatrice Riviere, and D. Fuentes.
Liver Segmentation via Unrolled Mumford-Shah Network, October 2018.
Keck Annual Research Conference, Gulf Coast Consortia, Houston, TX.
[ bib ]
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[43]
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S Oedi, M Spors, D Fuentes, and ENK Cressman.
Mathematical Model Development for Thermochemical Ablation, August
2018.
CPRIT Program Trainee Data, MD Anderson, Houston, Texas.
[ bib ]
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[44]
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D. Fuentes.
Therapy Response Prediction in Liver, July 2018.
SIAM Annual Meeting. Portland, Oregon.
[ bib ]
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[45]
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S.L. Thrower, S. Kandala, D. Fuentes, K. Mathieu, W. Stefan, A. Kulp, and
J. D. Hazle.
Comparison of two reconstruction methods for bound-nanoparticle
detection using superparamagnetic relaxometry, July 2018.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Nashville, Tennessee.
[ bib ]
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[46]
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E Gates, J Lin, J Weinberg, J Hamilton, S Prabhu, J Hazle, G Fuller,
V Baladandayuthapani, D Fuentes, and D Schellingerhout.
Quantitative Prediction of Cellular Proliferation Marker Ki67 Using
MR Imaging in Glioma, July 2018.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Nashville, Tennessee.
[ bib ]
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[47]
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C Owens, C Peterson, C Tang, E Koay, W Yu, J Li, M Salehpour, D Fuentes,
L Court, and J Yang.
Convolutional Neural Networks for Fully Automatic Segmentation of
Lung Tumors in CT Images, July 2018.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Nashville, Tennessee.
[ bib ]
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[48]
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Drew Mitchell, Ken-Pin Hwang, Tao Zhang, and D. Fuentes.
Information Theory Quantification of Acquisition Parameter Impact on
Synthetic MRI Reconstruction Uncertainty, June 2018.
ISMRM, Paris, France, E-Poster Presentation.
[ bib ]
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[49]
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Drew Mitchell, Ken-Pin Hwang, Tao Zhang, Linda Chi, Jason Stafford, and
D. Fuentes.
Improved Synthetic MRI Reproducibility through Mutual Information
Optimization of Acquisition Parameters, May 2018.
DI Trainee Research Symposium, Poster Presentation .
[ bib ]
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[50]
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Ahmed M. Khalaf, D. Fuentes, Aliya Qayyum, Janio Szklaruk, Ali Morshid,
Ahmed Kaseb, Manal Hassan, John D. Hazle, and Khaled M. Elsayes.
Quantifying HCC response to TACE using existing criteria and fully
automated volumetric segmentation, April 2018.
American Roentogen Ray Society (ARRS) Annual Meeting.
[ bib ]
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[51]
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Ahmed M. Khalaf, Ali Morshid, D. Fuentes, Asif Rashid, Ahmed Kaseb, Manal
Hassan, Veronica Cox, John D. Hazle, Khaled M. Elsayes, and Aliya Qayyum.
Role of Radiomic Texture Analysis Mapping in Prediction of
Microvascular Invasion in Hepatocellular Carcinoma, April 2018.
American Roentogen Ray Society (ARRS) Annual Meeting.
[ bib ]
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[52]
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Ahmed M. Khalaf, D. Fuentes, Ali Morshid, John D. Hazle, Manal Hassan,
and Khaled M. Elsayes.
Decoding of The Patatin-Like Phospholipase Domain Containing 3
(PNPLA3) Gene Phenotype Using Noninvasive Imaging Quantitative Radiomics
Approach, April 2018.
American Roentogen Ray Society (ARRS) Annual Meeting.
[ bib ]
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[53]
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RM Marcus, D. Fuentes, HA Lillemoe, A Qayyum, and TA Aloia.
Quantitative ct imaging features correlate with colorectal cancer
liver metastases (clm) mutational status.
March 2018.
AHPBA Annual Meeting, Miami, Fl.
[ bib ]
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[54]
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Ahmed M. Khalaf, Ali Morshid, D. Fuentes, Veronica Cox, John D. Hazle,
Khaled M. Elsayes, and Aliya Qayyum.
Quantitative CT Imaging Features Correlate with Colorectal Cancer
Liver Metastases (CLM) Mutational Status, 2018.
AHPBA meeting.
[ bib ]
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[55]
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J. Lin, E. Gates, J. Weinberg, J. Hamilton, S. Prabhu, J. Hazle, G. Fuller,
V. Baladandayuthapani, D. Fuentes, and D. Schellingerhout.
Radiological-Pathological Correlations and Imaging Signatures for
Gliomas, 2018.
56th Annual Meeting of the American Society of Neuroradiology.
[ bib ]
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[56]
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D. Fuentes, N Munoz, K Maldonado, C Kingsley, K Michel, H Taghavi, and
R Avritscher.
Multiparametric imaging correlations with pathology in a HCC rat
model, November 2017.
Radiological Society of North America. 103th Annual Meeting.
[ bib ]
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[57]
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Ahmed M. Khalaf, D. Fuentes, Ahmed O. Kaseb, Manal Hassan, Ali Morshid,
John D. Hazle, and Khaled M. Elsayes.
Hepatocellular Carcinoma (HCC) Response to Transcatheter Arterial
Chemoembolization (TACE) and Its Correlation with HCC Tumor Volumes,
November 2017.
Radiological Society of North America. 103th Annual Meeting.
[ bib ]
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[58]
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A.M. Khalaf, D. Fuentes, A. Kaseb, M. Hassal, A. Morshid, JD Hazle, and
K.M. Elsayes.
Hepatocellular Carcinoma (HCC) Response to Transcatheter Arterial
Chemoembolization (TACE) and Its Correlation with HCC Tumor Volumes,
November 2017.
Radiological Society of North America. 103th Annual Meeting.
[ bib ]
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[59]
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S.L. Thrower, J. Sovizi, W. Stefan, K. Mathieu, J.D. Hazle, and D.
Fuentes.
3D Reconstruction of cancer-bound nanoparticles using
superparamagnetic relaxometry, October 2017.
SIAM Houston Imaging Sciences Symposium.
[ bib ]
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[60]
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S. Loupot, D. Fuentes, W. Stefan, J. Sovizi, K. Mathieu, and J.D. Hazle.
A 3D Reconstruction Algorithm for Superparamagnetic Relaxometry,
July 2017.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Denver, Colorado.
[ bib ]
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[61]
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Drew Mitchell, Samuel Fahrenholtz, Christopher MacLellan, Jason Stafford, John
Hazle, and D. Fuentes.
A Heterogeneous Tissue Model for Treatment Planning in Laser Induced
Thermal Therapy. , May 2017.
14th U.S. National Congress on Computational Mechanics.
[ bib ]
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[62]
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Drew Mitchell, Samuel Fahrenholtz, Christopher MacLellan, Jason Stafford, John
Hazle, and D. Fuentes.
A Heterogeneous Tissue Model for Treatment Planning in Laser Induced
Thermal Therapy. , April 2017.
Society for Thermal Medicine, Poster Presentation.
[ bib ]
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[63]
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Dhiego Bastos, Vivek Beechar, Ganesh Rao, D. Fuentes, Jason Stafford,
Jeffrey Weinberg, and Sujit Siqueira Prabhu.
Predictors of local control of post-stereotactic radiosurgery brain
lesions treated with laser interstitial thermal ablation, April 2017.
AANS Annual Meeting, Los Angeles, CA.
[ bib ]
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[64]
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D. Fuentes, Dhiego Bastos, Ganesh Rao, Jeffrey Weinberg, Sujit Siqueira
Prabhu, and Jason Stafford.
DCE-MRI assessment of MRgLITT Response, April 2017.
AANS Annual Meeting, Los Angeles, CA.
[ bib ]
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[65]
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Drew Mitchell, Reza Madankan, Samuel Fahrenholtz, Christopher MacLellan,
Wolfgang Stefan, Jason Stafford, John Hazle, and D. Fuentes.
Stability of Information Theoretic K-Space Trajectories for
Model-Based MR Thermal Image Reconstruction abstract, March 2017.
Advances in Computational Sciences and Engineering, A conference in
honor of the 80th birthday of Prof. J. Tinsley Oden.
[ bib ]
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[66]
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Dhiego Chaves De Almeida Bastos, Ganesh Rao, Jeffrey S Weinberg,
D. Fuentes, 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 ]
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[67]
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Javad Sovizi, Sara L Thrower, D. Fuentes, Wolfgang Stefan, John D Hazle,
and Kelsey Mathieu.
Binary classification of superparamagnetic relaxometry data for
cancer screening, 2017.
[ bib ]
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[68]
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Sara L Thrower, Kelsey Mathieu, Wolfgang Stefan, R Romero Aburto, Zhen Lu,
Robert C Bast, Javad Sovizi, D. Fuentes, and John D Hazle.
Volumetric reconstruction of targeted nanoparticles for
superparamagnetic relaxometry, 2017.
[ bib ]
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[69]
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Ahmed M Khalaf, D. Fuentes, 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 ]
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[70]
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D. Fuentes, D. Bastos, V. Beechar, D. Mitchell, C. MacLellan, G. Rao, J.
Stafford, S. Prabhu.
MRgLITT Induced Perfusion Changes Assessed by DCE-MRI, Feb 2017.
Intraoperative Imaging Society, Hannover, Germany.
[ bib ]
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[71]
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D. Bastos, V. Beechar, G. Rao, D. Fuentes, J. Stafford, J. Weinberg, and
S. Prabhu.
Predictors of Local Control of Post SRS Brain Metastasis Treated
with LITT. 6th Biennial Meeting of the Intraoperative Imaging Society, Feb
2017.
Intraoperative Imaging Society, Hannover, Germany.
[ bib ]
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[72]
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K Ahmed, D. Fuentes, and K M Elsayes.
Three Dimensional Volumetric Image Segmentation of the Liver; Tips
for Clinical Practice and Future Perspectives., November 2016.
Educational Exhibit, Radiological Society of North America. 102th
Annual Meeting.
[ bib ]
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[73]
|
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 ,
November 2016.
Radiological Society of North America. 102th Annual Meeting.
[ bib ]
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[74]
|
Drew Mitchell, Samuel Fahrenholtz, Reza Madankan, Christopher MacLellan, Jason
Stafford, John Hazle, and D. Fuentes.
A Heterogeneous Tissue Model for Treatment Planning in Laser Induced
Thermal Therapy, October 2016.
11th Interventional MRI Symposium. Baltimore, Maryland.
[ bib ]
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|
[75]
|
SJ Fahrenholtz, RJ Stafford, R Madankan, JD Hazle, and D Fuentes.
Flexible Training of MR-Guided Laser Ablation Models Via Global
Optimization, July 2016.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Washington, DC.
[ bib ]
|
|
[76]
|
R Madankan, C MacLellan, S Fahrenholtz, J Weinberg, G Rao, J Hazle, R Stafford,
and 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 ]
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|
[77]
|
J Lin, D Fuentes, A Chandler, J Hazle, and 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 ]
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[78]
|
CJ MacLellan, D Fuentes, H Espinoza, S Prabhu, G Rao, J Weinberg, and
R Stafford.
Investigation of MRI Derived Thermal Dose Models, July 2016.
American Association of Physicists in Medicine (AAPM): Annual
Meeting, Washington, DC.
[ bib ]
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[79]
|
Sara Loupot, W. Stefan, R. Madankan, K. Mathieu, D. Fuentes, and J.D.
Hazle.
Sparse source reconstruction for nanomagnetic relaxometry , May
2016.
SIAM Conference on Imaging Science.
[ bib ]
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|
[80]
|
Drew Mitchell, Reza Madankan, Samuel Fahrenholtz, Christopher MacLellan,
Wolfgang Stefan, Jason Stafford, John Hazle, and D. Fuentes.
Stability of Information Theoretic K-Space Trajectories for
Model-Based MR Thermal Image Reconstruction, May 2016.
SIAM Conference on Imaging Science.
[ bib ]
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|
[81]
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Reza Madankan, Wolfgang Stefan, Christopher MacLellan, Samuel Fahrenholtz, Drew
Mitchell, R.J. Stafford, John Hazle, and D. Fuentes.
Information Theoretic Approach for Accelerated Magnetic Resonance
Thermometry in the Presence of Uncertainties abstract, May 2016.
SIAM Conference on Imaging Science.
[ bib ]
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[82]
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Wolfgang Stefan, D. Fuentes, Erol Yeniaras, Ken-Pin Hwang, John D. Hazle,
and R. Jason Stafford.
Background Signal Suppression for Magnet Resonance (mr) Phase Images
abstract, May 2016.
SIAM Conference on Imaging Science.
[ bib ]
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[83]
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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 ]
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[84]
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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 ]
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[85]
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C MacLellan, D Fuentes, H Espinoza, S Prabhu, G Rao, J Weinberg, and
J Stafford.
A Patient Derived Logistic Model of Thermal Dose, 2016.
33nd Annual Meeting of the Society for Thermal Medicine, New
Orleans.
[ bib ]
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[86]
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S Fahrenholtz, R Madankan, A Shetty, S Danish, JD Hazle, RJ Stafford, and
D Fuentes.
Laser ablation prediction via global optimization and
cross-validation, 2016.
33nd Annual Meeting of the Society for Thermal Medicine, New
Orleans.
[ bib ]
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[87]
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R Madankan, C MacLellan, S Fahrenholtz, J Hazle, RJ Stafford, and
D Fuentes.
Effect of Different Tissue Segmentation schemes on Precise Treatment
Planning of Laser Induced Thermal Therapy: A Retrospective Study, 2016.
33nd Annual Meeting of the Society for Thermal Medicine, New
Orleans.
[ bib ]
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[88]
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S. Loupot, W. Stefan, R. Madankan, K. Mathieu, D. Fuentes, and J.D.
Hazle.
Sparse source reconstruction for nanomagnetic relaxometry, 2016.
International Workshop on Magnetic Particle Imaging.
[ bib ]
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[89]
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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 ]
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[90]
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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 ]
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[91]
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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 ]
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[92]
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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 ]
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[93]
|
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 ]
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[94]
|
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 ]
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[95]
|
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 ]
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[96]
|
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 ]
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[97]
|
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 ]
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[98]
|
D. Fuentes.
Fast Steady State Solution for Simulating Bioheat Distribution for
Image Guided Laser Ablation, April 2013.
STM 2013 Annual Meeting. Aruba.
[ bib ]
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[99]
|
D. Fuentes.
Planning of MR-Guided Laser Induced Thermal Therapy Using UQ
Methods, February 2013.
SIAM Computational Science and Engineering. Boston, Massachusetts.
[ bib ]
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[100]
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Dmitriy Meshkov, Richard Castillo, Edward Castillo, Min Li, Ngoc Pham, Julianne
Pollard, D. 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 ]
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[101]
|
Dmitriy Meshkov, Richard Castillo, Edward Castillo, D. 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 ]
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[102]
|
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 ]
|
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[103]
|
D. Fuentes.
Kalman Filtered Temperature Imaging for Monitoring MRgLITT
procedures, April 2012.
STM 2012 Annual Meeting. Portland, Oregon.
[ bib ]
|
|
[104]
|
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 ]
|
|
[105]
|
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 ]
|
|
[106]
|
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 ]
|
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[107]
|
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 ]
|
|
[108]
|
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 ]
|
|
[109]
|
D. Fuentes.
Real-Time Bioheat Transfer Models for Computer Driven MR guided
LITT, May 2010.
ISMRM, Stockholm, Sweden, E-Poster Presentation.
[ bib ]
|
|
[110]
|
D. Fuentes.
Thermal Image Reconstruction of In Vivo Nanoparticle
Concentrations for MR-Guided Laser Induced Thermal Therapy Optimization,
February 2010.
NanoEngineering for Medicine and Biology, Houston, Texas, Conference
Presentation.
[ bib ]
|
|
[111]
|
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 ]
|
|
[112]
|
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 ]
|
|
[113]
|
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 ]
|
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[114]
|
K Ahmed, D. Fuentes, J D Hazle, A Qayyum, V L Cox, and K M Elsayes.
Three Dimensional Volumetric Image Segmentation of the Liver Tips
for Clinical Practice and Future Perspectives, November 2015.
Educational Exhibit, Radiological Society of North America. 101th
Annual Meeting.
[ bib ]
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|
[115]
|
R. Madankan and D. Fuentes.
Accelerated Model-based Signal Reconstruction for Magnetic Resonance
Thermometry Data in Presence of Uncertainties, July 2015.
13th United States National Congress on Computational Mechanics
conference, San Diego, California.
[ bib ]
|
|
[116]
|
Florian Maier, Erik N. K. Cressman, Moritz Berger, D. Fuentes, R. Jason
Stafford, Christopher J. MacLellan, Reiner Umathum, and Armin M. Nagel.
Characterization of Thermochemical Ablation Injections Using 23Na
MRI, 2015.
ISMRM, Toronta, Canada, E-Poster Presentation.
[ bib ]
|
|
[117]
|
James Bankson, Christopher M. Walker, Wolfgang Stefan, D. Fuentes,
Matthew E. Merritt, Yunyun Chen, A. Dean Malloy, Craig R.and Sherry, Stephen
Lai, and John Hazle.
Model-Based Reconstruction of Hyperpolarized [1-13C]-Pyruvate,
2015.
ISMRM, Toronta, Canada, Poster Presentation.
[ bib ]
|
|
[118]
|
S. Fahrenholtz, J. Hazle, J. Stafford, A. Shetty, and D. Fuentes.
Results of cross validation from a trained model for laser induced
thermal therapy, 2015.
32nd Annual Meeting of the Society for Thermal Medicine.
[ bib ]
|
|
[119]
|
C MacLellan, MP Melancon, F Salatan, Y Qiao, K Hwang, D. Fuentes, and
RJ Stafford.
Magnetic Resonance Based Quantification of Nanoparticle Distribution
and Heating in Nanoparticle Mediated Laser Interstitial Thermal Therapy
(npLITT)., 2015.
32nd Annual Meeting of the Society for Thermal Medicine.
[ bib ]
|
|
[120]
|
R. Madankan, S. Fahrenholtz, J. Hazle, J. Stafford, A. Shetty, and D.
Fuentes.
Accurate Modeling of Laser Induced Thermal Therapy in Presence of
Heterogeneous Tissue, 2015.
32nd Annual Meeting of the Society for Thermal Medicine.
[ bib ]
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