جست‌وجوی استاد و پژوهشگر

Stephanie Dixonمشاهده پروفایل

استادیار مدعو

• Biostatistics
• Cluster Randomization Trials
• Epidemiology
• +۶ مورد دیگر

Stephanie Dixon, PhD, MSc is an Adjunct Assistant Professor in the Department of Epidemiology and Biostatistics at the Schulich School of Medicine & Dentistry, Western University. She serves as Program Manager for the Kidney, Dialysis and Transplantation Program at the Institute for Clinical Evaluative Sciences and works as a Biostatistician at the London Health Sciences Centre. Her academic appointments include being an Associated Graduate Faculty member with the Department of Mathematics and Statistics at the University of Guelph. Dr. Dixon earned her Post-Doctorate in Biostatistics from Western University (2009-2011), PhD in Statistics from the University of Guelph (2004-2009), MSc in Statistics from the University of Guelph, and Honours BSc in Mathematics and Statistics from McMaster University. Her research spans biostatistics methodology and clinical applications with particular expertise in kidney disease research. Dr. Dixon specializes in cluster randomization trials, survival analysis, health services research, and statistical methods for correlated observations. Her work frequently involves population-based research related to kidney disease and clinical trials, applying rigorous statistical methodologies to address important clinical questions in nephrology. Dr. Dixon's recent publications demonstrate a strong emphasis on applying biostatistical methods to real-world healthcare problems, particularly in nephrology. Her research portfolio includes medication safety studies in kidney disease patients, comparative effectiveness research on dialysis modalities, and methodological improvements in clinical trial design. She has made significant contributions to understanding drug safety profiles, organ allocation systems, and statistical methodology for cluster randomized trials. As an active member of the Biostatistics Research Cluster at Western University, Dr. Dixon collaborates across disciplines to advance statistical methodology and its application to health research questions. Her work bridges theoretical statistics and practical healthcare applications, contributing substantially to evidence-based medicine through rigorous methodological approaches applied to clinically important questions.

Christopher Kurzمشاهده پروفایل

پژوهشگر

• Medical Physics
• Radiation Oncology
• MR-guided Radiation Therapy (MRgRT)
• +۱۲ مورد دیگر

Christopher Kurz is a dedicated researcher and group leader in the Department of Radiation Oncology at LMU University Hospital, affiliated with the Faculty of Medicine at LMU Munich. His work bridges medical physics and clinical oncology, focusing on advancing MRI-guided radiation therapy through artificial intelligence. Education: PhD in Medical Physics, 2014, LMU Munich Diploma in Physics, 2011, Heidelberg University Dr. Kurz's research is centered on integrating artificial intelligence into radiation oncology, with a strong emphasis on improving imaging, targeting, and treatment adaptation. His work spans deep learning applications for auto-segmentation, real-time tumor tracking, synthetic CT generation, motion modeling, and dose calculation in MRI-guided and CBCT-based radiotherapy. He has made significant contributions to the development of patient-specific AI models and methods for online adaptive therapy, particularly for abdominal and lung cancers. His research also explores low-field MR-linac applications and the clinical implementation of quantitative imaging. The recent publications highlight a strong trend toward leveraging foundation models, generative AI, and patient-specific deep learning frameworks to enhance precision, reduce imaging doses, and enable real-time adaptive radiotherapy. His work increasingly focuses on creating robust, clinically viable AI tools that can be integrated into routine workflows. Scientific Awards: Mildred Scheel Postdoctoral Scholarship, German Cancer Aid Dr. Kurz leads a research group, implying active mentorship and guidance of junior scientists and students in the fields of medical physics and AI in oncology. His research is supported by competitive funding, including the German Research Foundation (DFG), as evidenced by the DFG-funded project AIM-CBCT-ART, which focuses on uncertainty calibration for deep learning in adaptive radiotherapy. His leadership in creating challenge datasets (SynthRAD2025, TrackRAD2025) also indicates a strong collaborative and community-engaged research profile. Dr. Kurz leads the research group on MR-guided Radiation Therapy within the Department of Radiation Oncology. His team focuses on developing and validating AI-driven solutions for real-time imaging, target localization, and adaptive treatment planning. The group's work involves close collaboration between physicists, clinicians, and data scientists, aiming to translate advanced computational methods into clinical practice for improved cancer care.

Danny Lathouwersمشاهده پروفایل

دانشیار

• Nuclear Engineering
• Computational Physics
• Proton Therapy
• +۴ مورد دیگر

Danny Lathouwers is an Associate Professor at the Delft University of Technology within the Faculty of Applied Sciences and the Department of Radiation Science and Technology . His research focuses on computational techniques for nuclear reactor physics and proton therapy, particularly using adaptive numerical methods to simulate reactor systems and charged particle transport for high-quality dose distribution in patient geometries. Academic Affiliation : Delft University of Technology, Faculty of Applied Sciences, Department of Radiation Science and Technology Contact : Mekelweg 15, 2629JB Delft, The Netherlands | +31 15 278 3148 | d.lathouwers@tudelft.nl His research areas include: Nuclear Reactor Physics : Numerical methods, modal analysis of neutron transport, multi-physics simulations for Generation-IV reactors Proton Therapy : Deterministic dose calculation, uncertainty quantification, robust treatment planning Adaptive Radiation Transport : Spatial/angular refinement techniques, discontinuous Galerkin methods Recent publications highlight his expertise in: Proton therapy range verification systems Deterministic low-rank transport methods Deep learning applications for anatomical variability Multiphysics reactor modeling He actively supervises MSc and BSc student projects in computational reactor physics and proton therapy.

Dennis R. Schaartمشاهده پروفایل

استاد

• Medical Physics
• Radiation Oncology
• Medical Imaging
• +۱۳ مورد دیگر

Dennis R. Schaart is a Professor and head of the Medical Physics & Technology section at the Department of Radiation Science & Technology, Faculty of Applied Sciences, Delft University of Technology (TU Delft). He is also a member of the R&D Program Board of the Holland Proton Therapy Centre (HollandPTC), highlighting his significant role in advancing clinical radiation technologies. His work bridges fundamental physics with medical applications, particularly in imaging and therapy. His primary research interests include Medical Physics, Radiation Oncology, Medical Imaging, Radiation Detection, Dosimetry, and Biomedical Engineering . He specializes in positron emission tomography (PET), time-of-flight methods, proton therapy, and scintillation detector development. His expertise in Monte Carlo simulation and experimental physics enables rigorous evaluation and innovation in detector systems and imaging protocols. The recent publications (2025–2021) reveal a strong trend toward photon-counting X-ray and PET detectors , proton therapy optimization , and novel scintillator materials . There is a clear emphasis on improving spatial, temporal, and energy resolution in imaging, with applications in both diagnostics and treatment planning. The integration of machine learning and Monte Carlo simulations further enhances the predictive and analytical power of his research. Scientific Awards: SNMMI 2015 International Best Abstracts Award Awarded for the highest number of citations for an article published over 2004–2008 Most cited paper in preceding five years (GATE V6 paper) Recognition at Trace 'n Treat conference for radionuclide state determination Dennis Schaart has (co-)authored over 100 journal papers and is a frequently invited speaker, indicating strong leadership and influence in the medical physics community. While no direct mention of students is found, his leadership role and extensive publication record suggest active supervision and mentorship. He is involved in national advisory roles, including serving on committees for the Ministry of Economic Affairs, reflecting broader impact beyond academia. His work is supported by collaborations with institutions like Philips, CERN, and various medical centers. Laboratories and Research Groups: He leads the Medical Physics & Technology research group within the Radiation Science & Technology department at TU Delft. The group focuses on developing and evaluating novel detector systems for medical imaging and therapy, using both experimental and computational approaches. The lab is equipped for scintillator characterization, detector prototyping, and advanced simulations, particularly using the GATE platform.

Tom Braeckeveltمشاهده پروفایل

پژوهشگر ارشد

• Materials Science
• Perovskite Solar Cells
• Computational Chemistry
• +۳ مورد دیگر

Tom Braeckevelt is a Research Fellow at Ghent University, Belgium, working within the computational materials science group led by Prof. Veronique Van Speybroeck. Based at Tech Lane Ghent Science Park (Technologiepark 46, Zwijnaarde), he collaborates extensively with experimental teams including Prof. Johan Hofkens (photophysics) and Prof. Sara Bals (electron microscopy), bridging theoretical modeling with advanced characterization techniques to solve stability challenges in next-generation photovoltaics. Education: PhD in Materials Science, Ghent University (2018). Dissertation: Designing 2D hybrid organic-inorganic perovskites for game-changing photovoltaics , supervised by Prof. Veronique Van Speybroeck and Dr. Kurt Lejaeghere. His research centers on perovskite stability mechanisms through multiscale computational modeling (DFT, machine learning potentials, molecular dynamics) integrated with experimental validation (TEM, GIWAXS, spectroscopy). Key focus areas include phase transition kinetics, strain engineering, doping strategies, and interfacial design for cesium lead halide perovskites. This work addresses critical barriers to commercial solar cell deployment, particularly ambient-condition stability and efficiency retention. From 2019-2025, Dr. Braeckevelt co-authored 13 high-impact publications including Science (2019), Nature Communications (2022), and ACS Nano (2025), demonstrating progression from fundamental phase transition studies to machine learning-enhanced stability solutions. Recent work expands into covalent organic frameworks and rare-event sampling algorithms, reflecting methodological diversification while maintaining photovoltaic applications as the core driver. No scientific awards or fellowships were documented in the source materials. Supported by institutional research grants at Ghent University, Dr. Braeckevelt has presented findings at 8+ international conferences including PSCO19 (Lausanne), ICAMM (Rennes), and DFT2022 (Brussels). His invited talk at the 2025 Eindhoven Psiflow workshop highlights growing recognition in machine learning for perovskites. No student supervision roles were indicated in current position. He operates within Ghent's integrated materials research ecosystem, contributing to cross-disciplinary projects that combine computational prediction with nanoscale characterization to accelerate renewable energy technology development.

• Radiation Oncology Physics
• Stereotactic Body Radiotherapy (SBRT)
• Lung Cancer Radiotherapy
• +۵ مورد دیگر

Marcin Wierzbicki serves as an Assistant Professor in Interdisciplinary Science at McMaster University, with primary appointments in the Department of Medical Physics and Radiation Sciences within the Faculty of Science. His research bridges medical physics and radiation oncology, focusing on optimizing radiotherapy treatments for cancer patients, particularly those with lung cancer. Dr. Wierzbicki plays a key role in major clinical trials including the LUSTRE trial (comparing SBRT versus conventionally hypofractionated radiotherapy) and the OCOG-ALMERA trial (investigating metformin as a radiosensitizer). Dr. Wierzbicki's scholarly work demonstrates a progression from earlier contributions in cardiac modeling and image-guided surgery toward his current specialization in radiation oncology physics. His research addresses critical challenges in treatment planning accuracy, quality assurance protocols, and toxicity management for stereotactic body radiotherapy (SBRT), particularly for challenging tumor locations like ultracentral lung tumors. His publications reveal a strong emphasis on clinical trial methodology, credentialing standards for multi-institutional studies, and the application of advanced computational techniques to improve treatment precision. Analysis of his 15 most recent publications shows consistent focus on optimizing lung cancer radiotherapy, with particular attention to planning target volume margins, dose reconstruction methods, and the relationship between treatment parameters and clinical outcomes. His work on the LUSTRE trial credentialing process has established important standards for SBRT delivery across multiple institutions, while his recent exploration of AI-based dose prediction systems represents cutting-edge integration of machine learning in radiation oncology. Growth differentiation factor 15 (GDF15) as a biomarker for treatment response prediction (2025) LUSTRE Phase 3 trial comparing SBRT versus conventional radiotherapy (2024) Long-term toxicity analysis for ultracentral lung tumors (2023-2024) AI-based dose prediction systems for adaptive radiotherapy (2023) Dr. Wierzbicki contributes significantly to graduate education through courses including 'Anatomy for Medical Physicists' (MEDPHYS 783) and 'Radiation Oncology Physics I' (MEDPHYS 778), demonstrating his commitment to training the next generation of medical physicists. His consistent teaching record since 2018 and active research program position him as an important contributor to translational research in cancer treatment.

Sara Ghorbani Gorjiمشاهده پروفایل

پژوهشگر ارشد

• Environmental Health Sciences
• Chemical Exposure Science
• Per- and Polyfluoroalkyl Substances (PFAS)
• +۳ مورد دیگر

Dr. Sara Ghorbani Gorji is a Research Fellow at the Queensland Alliance for Environmental Health Sciences (QAEHS), The University of Queensland, specializing in environmental health and chemical exposure science. Her work focuses on per- and polyfluoroalkyl substances (PFAS) , passive sampling methodologies , and advanced analytical techniques for contaminant detection. Education: PhD in Food Science (2018), The University of Queensland Research interests include environmental contaminant mapping , chemical risk assessment , and metabolomics applications in food and environmental systems. She develops targeted and non-target mass spectrometry strategies to analyze persistent pollutants across water, soil, and consumer products. Recent publications highlight PFAS in compostable packaging , neonicotinoid ecotoxicity , and passive sampling innovations . Her work has informed policy guidelines for chemical regulation and industry standards for sustainable product development. Grants: Advance Queensland Women's Research Assistance Program (2021-2023) FOGO Contamination Study (2022-2024) Seafood PFAS Assessment (2023-2025) As Associate Advisor for multiple PhD candidates, she guides research on glyphosate exposure reduction , neonicotinoid fate , and FOGO material contaminants . Collaborations span government agencies, industry partners, and international institutions.

Jean-Baptiste Woillardمشاهده پروفایل

استاد

• Pharmacometrics Modeling
• Artificial Intelligence
• Personalized Medicine
• +۵ مورد دیگر

Jean-Baptiste Woillard is a Professor of Medical Pharmacology at the Faculty of Medicine in Limoges and serves as the head of the "Pharmacometrics and Artificial Intelligence" functional unit within the Department of Pharmacology, Toxicology, and Pharmacovigilance at the University Hospital of Limoges. He also functions as deputy director at Inserm U1248 "Pharmacology & Transplantation" and is actively involved with the International Association of Therapeutic Drug Monitoring and Clinical Toxicology (IATDMCT), where he previously served as president of the Pharmacometrics Committee. His professional affiliations include membership in the European Association for Clinical Pharmacology and Therapeutics (EACPT) and the French Society of Pharmacology and Therapeutics (SFPT). Dr. Woillard obtained his PharmD from the University of Toulouse in 2004, followed by a Master's degree in Pharmacology in 2007, and completed his Ph.D. in Pharmacogenetics and Pharmacokinetics in 2011. His academic progression includes positions as Assistant Professor (2010-2014), Associate Professor (2014-2023), and Professor (since 2023) within the Pharmacology, Toxicology Department at the University Hospital of Limoges/INSERM U1248. His research focuses on pharmacometrics modeling, artificial intelligence applications in personalized medicine, and pharmacogenetics. Dr. Woillard's work centers on the personalization of treatments, particularly concerning immunosuppressants in organ transplantation. His research encompasses pharmacogenetic and pharmacodynamic studies, development of pharmacokinetic models, statistical modeling, and the application of machine learning methods to therapeutic drug monitoring. Since 2019, his research has increasingly incorporated AI methodologies to optimize drug dosing and treatment outcomes. Analysis of Dr. Woillard's recent publications reveals a strong emphasis on applying machine learning to pharmacokinetic modeling and therapeutic drug monitoring across various medications including immunosuppressants, antibiotics, and antivirals. His work demonstrates a consistent trajectory toward developing sophisticated algorithms for dose individualization, with particular focus on mycophenolate, tacrolimus, ganciclovir, and daptomycin. The research shows increasing integration of AI techniques with traditional pharmacometric approaches to create more precise dosing strategies. IFCC-Gérard Siest Young Scientist Award (2020) for Distinguished Contributions in Pharmacogenetics Recipient of 1.8 million euros funding through the French PEPR framework for developing multi-scale pharmacological digital twins Dr. Woillard has published over 130 articles in peer-reviewed international scientific journals, delivered more than 30 presentations at national and international conferences, and has been invited to give over 40 academic and industry talks. He leads a significant research consortium focused on developing multi-scale pharmacological digital twins, demonstrating his leadership in securing substantial research funding. His work bridges clinical practice with advanced computational methodologies to improve patient outcomes through personalized medicine approaches. As head of the "Pharmacometrics and Artificial Intelligence" functional unit at the Department of Pharmacology, Toxicology, and Pharmacovigilance (CHU of Limoges), Dr. Woillard directs research efforts focused on treatment personalization, particularly for immunosuppressants in organ transplantation. His team collaborates extensively within the Inserm U1248 unit "Pharmacology & Transplantation," working on both fundamental pharmacological research and clinical applications of their findings. The research group maintains active collaborations with international organizations including the IATDMCT and EACPT.

Lazar Krsticمشاهده پروفایل

عضو هیئت علمی

• Artificial Intelligence
• Machine Learning
• Informatics
• +۳ مورد دیگر

Lazar Krstic is an Assistant at the Institute of Mathematics and Informatics within the Faculty of Science and Mathematics at the University of Kragujevac. His research focuses on Artificial Intelligence and Machine Learning, operating in the Informatics field. He completed his Master's degree in 2017 and earned his Doctorate in 2019. His work integrates advanced computational techniques with applications in medical physics, education, and environmental modeling. Research Trends: Recent publications highlight expertise in radiation therapy optimization, physics-informed neural networks, and evolutionary algorithms. Articles demonstrate interdisciplinary applications spanning healthcare, education, and computational hydrology.

Mike Wellsمشاهده پروفایل

استادیار

• Emergency Weight Estimation
• Drug Dosing
• Geriatric Emergency Medicine
• +۴ مورد دیگر

Mike Wells is a Research Assistant Professor in the Department of Emergency Medicine at Florida Atlantic University's Charles E. Schmidt College of Medicine and an Honorary Professor at the University of the Witwatersrand, Johannesburg. He leads research in emergency weight estimation and drug dosing with emphasis on obese patients while supporting geriatric emergency medicine initiatives. His educational credentials include a Ph.D. (2018) and MSc(Med) (2008) in Emergency Medicine, MBBCh medical degree (1992), and intercalated BSc(Med) (1987) and BSc.Hons (1988) from the University of the Witwatersrand, plus a Diploma in Transthoracic Echocardiography (2013) from the Medical University of Vienna. Dr. Wells' research spans pediatric and adult emergency weight estimation systems, most notably the internationally endorsed PAWPER tape. His work integrates patient safety, metacognition, point-of-care ultrasound, prehospital care, and geriatric emergency protocols. He has pioneered methodologies accounting for body habitus in weight estimation and drug dosing calculations. His 2024-2025 publications reveal dual research trajectories: geriatric head trauma investigations focusing on anticoagulation risks, dementia complications, and fall-related injuries alongside technological innovations in weight estimation using 3D imaging and systematic validation of dosing protocols. This work bridges clinical emergency care with engineering solutions for vulnerable populations. Recognition includes: Order of the Emergency Medicine Society of South Africa (2016) Fellow of the College of Emergency Medicine (FCEM(SA)) Fellow of the Emergency Medicine Society of South Africa (FEMSSA) With over 20 Master's candidates supervised to completion, Dr. Wells maintains active editorial leadership as Deputy Editor-in-Chief of the African Journal of Emergency Medicine and Associate Editor for JACEP Open. His research is supported by ongoing collaborations with South African institutions and international emergency medicine societies. At Florida Atlantic University, he is building a dedicated research team focused on refining weight estimation tools for diverse body types and expanding evidence-based protocols for geriatric emergency care through interdisciplinary partnerships.

• Machine Learning
• Deep Learning for Medical Applications
• Medical Imaging
• +۳ مورد دیگر

Chenyang Shen is an Assistant Professor in the Department of Radiation Oncology at the University of Texas Southwestern Medical Center. He earned his B.S. from Yangzhou University and his M.S. and Ph.D. from Hong Kong Baptist University, followed by a medical physics residency at UT Southwestern. His clinical focus includes RefleXion and EROC TPOD, while his research integrates machine learning and deep learning into medical imaging, image processing, and intelligent treatment planning for radiotherapy. Education: B.S. (Yangzhou University), M.S. and Ph.D. (Hong Kong Baptist University) Dr. Shen’s research spans medical physics , AI-driven treatment optimization , and image-guided radiation therapy . His work emphasizes deep learning for dose calculation, real-time adaptive planning, and synthetic CT generation from CBCT/MRI data. Key publications highlight innovations in PET-guided liver motion tracking and AI for brachytherapy applicator digitization . He has received the ASTRO Basic/Translational Science Award for junior investigators and AAPM Best in Physics (Therapy) . His grants and collaborations focus on improving radiation dose accuracy and enabling biology-guided therapy via PET-Linac systems. Labs and teams he works with include the Division of Medical Physics and Engineering at UT Southwestern.

• Radiation Oncology
• Medical Physics
• Computational Physics
• +۵ مورد دیگر

Dr. Yulong Yan is a Professor in the Department of Radiation Oncology at UT Southwestern Medical Center, where he serves as Director of Computational Physics and faculty in the Division of Medical Physics & Engineering. His career spans academic, clinical, and technical domains in radiation oncology and biomedical engineering. Bachelor's & Master's: Nanjing University of Aeronautics and Astronautics Ph.D.: Biomedical Engineering, Southeast University, Nanjing Fellowship/Residency: Stanford University School of Medicine Dr. Yan's research focuses on Medical Physics , Adaptive Radiotherapy , and Deep Learning applications in clinical oncology. Key areas include: Functional lung avoidance techniques (FWAS, virtual bronchoscopy) Deep learning for synthetic CT generation and tumor segmentation Dose verification platforms (ART2Dose) Radiation toxicity modeling for brain and lung treatments Bluetooth-based localization systems for clinical applications MR-only radiotherapy workflows His publications demonstrate expertise in Image-Guided Radiotherapy , Treatment Planning Optimization , and Radiation Dose Modeling , with a 2013–2024 publication record showing sustained contributions to Medical Physics , Radiotherapy and Oncology , and PLOS One . Dr. Yan has served as Associate Editor for Medical Physics and Journal of Clinical Medical Physics , while chairing the Southwest Chapter AAPM Communications and IT Committee since 2014. As an educator since 2013, he mentors medical physics residents and postdoctoral fellows while delivering lectures in the Medical Physics Certificate Program.

Tingliang Zhuangمشاهده پروفایل

دانشیار

• Adaptive Radiotherapy
• Cone-Beam CT Image Reconstruction
• Image-Guided Radiation Therapy (IGRT)
• +۳ مورد دیگر

Tingliang Zhuang, Ph.D. is an Associate Professor in the Department of Radiation Oncology at UT Southwestern Medical Center , where he contributes to both research and teaching. His academic journey began with a bachelor's degree in Electrical Engineering from Nanjing University , followed by a Ph.D. in Medical Physics at the University of Wisconsin-Madison . Education: B.S. in Electrical Engineering, Nanjing University Ph.D. in Medical Physics, University of Wisconsin-Madison Dr. Zhuang’s research interests span adaptive radiotherapy, cone-beam CT (CBCT) image reconstruction, image-guided radiation therapy (IGRT), treatment planning, and dosimetry. A significant focus of his work involves leveraging artificial intelligence to enhance therapeutic precision. His research has led to over 30 publications in journals like Medical Physics and International Journal of Radiation Oncology Biology Physics , addressing topics such as dose calculation algorithms, stereotactic body radiation therapy (SBRT), and adaptive treatment strategies for lung, spine, and prostate cancers. The 15 most recent articles in his publication record emphasize advancements in adaptive radiotherapy, CBCT-guided dose optimization, and AI-driven outcome prediction. These works often compare traditional methods (e.g., pencil beam vs. Monte Carlo simulations) and explore dosimetric impacts of tumor localization, organ motion, and delivery accuracy. In teaching , Dr. Zhuang has co-lectured medical physics residents at UT Southwestern, covering imaging in medicine and clinical rotation courses. His collaborations with institutions like Varian Medical Systems and UT Southwestern Medical Center highlight his translational approach to medical physics.

• Operations Research
• Radiation Oncology
• Medical Data Analytics
• +۳ مورد دیگر

Ali Ajdari is an Instructor in Radiation Oncology at the Massachusetts General Hospital. With a Ph.D. in Optimization from the University of Washington and postdoctoral training in Radiation Therapy Optimization at Harvard Medical School, he applies operations research methodologies to healthcare, particularly oncology. His work bridges mathematical optimization, biological data, and clinical practice to advance personalized treatment plans. Education: B.Sc. and M.Sc. in Industrial & Systems Engineering, Ph.D. in Optimization Research Interests focus on integrating optimization techniques (convex, robust, dynamic, stochastic) with medical data (imaging, genetic, bio-physiological) to enhance radiation therapy. His recent publications highlight applications in: Pediatric traumatic brain injury simulation Diffusion tensor-based target volume personalization Machine learning toxicity models in radiotherapy Mid-treatment adaptive planning using biomarkers Publications Analysis reveals a strong emphasis on computational approaches in radiation oncology, with key contributions in: Anisotropic tumor volume modeling Online adaptive proton therapy Toxicity prediction frameworks PET/CT-driven treatment adaptation

Sunil Sharmaمشاهده پروفایل

دانشیار بالینی

• Radiation Physics
• Radiation Dosimetry
• Radiation Oncology
• +۷ مورد دیگر

Sunil Sharma, PhD is a Clinical Associate Professor in the Department of Radiation Oncology at East Carolina University's Brody School of Medicine. He is board certified by the American Board of Radiology in Therapeutic Radiological Physics and has extensive expertise in radiation therapy systems including treatment planning (ADAC Pinnacle, XIO, VARIAN Eclipse), teletherapy (Varian Clinac IX, TrueBeam, TOMOTHERAPY), Cyber Knife, Gamma Knife, and brachytherapy techniques. Dr. Sharma's research focuses on radiation physics, dosimetry, and radiation-induced heart disease. His work spans preclinical small animal irradiation research and clinical radiation therapy applications. He has developed image-guided techniques for local heart irradiation in rat models and has investigated the effects of radiation on cardiac mitochondria, oxidative stress pathways, and inflammatory responses. His recent publications (2017-2023) demonstrate continued research in stereotactic radiosurgery, fiducial tracking for lung tumors, image quality assessment in small animal irradiators, and advanced radiation delivery techniques including GRID therapy. His work shows an evolving focus from fundamental radiation physics to translational applications in cancer treatment. Dr. Sharma has held academic positions at East Carolina University since 2015 and previously at the University of Arkansas for Medical Sciences. He has collaborated extensively with researchers in radiation oncology, medical physics, and cardiovascular biology, resulting in 53 publications with approximately 725 citations. His laboratory work centers around the Small Animal Conformal Radiation Therapy Device (SACRTD), which integrates 3D-conformal radiation therapy delivery with CBCT imaging capabilities using a robotic arm system. This platform enables precise radiation delivery for preclinical research with direct clinical translation potential.