جست‌وجوی استاد و پژوهشگر

Isabel Molwitzمشاهده پروفایل

پزشک-پژوهشگر

• Radiology
• Diagnostic Imaging
• Body Composition Analysis
• +۶ مورد دیگر

Dr. Isabel Molwitz serves as a Physician Scientist at the Department of Diagnostic and Interventional Radiology and Nuclear Medicine, University Medical Center Hamburg-Eppendorf, within the Faculty of Medicine. With 47 publications spanning from 2017 to 2025, she has established herself as a leading researcher in quantitative imaging biomarkers and gender studies in radiology. Her research program focuses on advancing body composition analysis through dual-energy CT and MRI techniques, with particular emphasis on muscle quality assessment as a predictor of clinical outcomes. Dr. Molwitz has pioneered the use of CT-derived muscle fat fraction as a survival predictor in critically ill patients and demonstrated that muscle quality—not merely quantity—significantly impacts outcomes in colorectal cancer surgery and liver transplantation. Her work bridges radiology with critical care medicine, oncology, and cardiology through innovative applications of quantitative imaging. Analysis of her publication trends reveals three interconnected research strands: 1) Technical development of dual-energy CT methodologies for precise body composition analysis; 2) Clinical validation of imaging biomarkers across diverse patient populations; and 3) Investigation of gender disparities in radiological practice, academic advancement, and leadership opportunities. Her recent work has expanded to address sustainability in radiology and the impact of organizational structures on both patient care and staff well-being. Dr. Molwitz actively collaborates with multiple research networks at the University Medical Center Hamburg-Eppendorf, including the Cardiovascular Research group, Oncological Research (UCCH), and the Neuro-Immun Network Hamburg. She contributes significantly to the Methodology and Research Working Group of the German Radiological Society, helping shape research standards nationally. Her involvement in developing Germany's first specialist training curriculum for radiology and creating digital teaching tools during the pandemic demonstrates her commitment to advancing radiology education and practice.

Ti Baiمشاهده پروفایل

مدرس

• Radiotherapy
• X-ray CT Imaging
• Medical Image Analysis
• +۳ مورد دیگر

Ti Bai, Ph.D., is a Medical Physics Resident PGY-2 at UT Southwestern Medical Center , affiliated with the Department of Radiation Oncology and its Medical Artificial Intelligence and Automation (MAIA) Lab . He previously served as a postdoctoral researcher (2019–2021) and was promoted to Lecturer in 2021. Dr. Bai’s research focuses on low-dose X-ray CT imaging , medical image analysis , and deep learning algorithms for healthcare applications. His work bridges artificial intelligence and radiation oncology to improve clinical workflows and treatment accuracy. Key publication trends include advancements in CT image denoising , automated segmentation , and real-time tumor localization . His 2023–2022 articles highlight self-supervised learning , multi-spectral CT enhancement , and AI-driven contour editing for radiotherapy. Dr. Bai earned his Ph.D. at Xi'an Jiaotong University (2017), specializing in low-dose CT reconstruction , followed by two years at Baidu Research 's Institute of Deep Learning (2017–2019).

Sokratis Makrogiannisمشاهده پروفایل

استاد

• Medical Image Computing
• Machine Learning
• Biomedical Imaging
• +۳ مورد دیگر

Sokratis Makrogiannis is a Professor in the Division of Physics, Engineering, Mathematics, and Computer Science at Delaware State University, where he directs the Mathematical Imaging and Visual Computing Group. His research focuses on medical image computing, machine learning, and visualization, with applications in biomedical imaging and computer vision. He holds a PhD in Electronics and Informatics from the University of Patras, Greece, and completed postdoctoral fellowships at the University of Pennsylvania and Wright State University. His work bridges industrial collaborations (e.g., GlaxoSmithKline, General Electric) and academic research, emphasizing computational neuroanatomy, image analysis algorithms, and medical imaging techniques. He has secured funding from NIH, NSF, DoD, and the State of Delaware. His teaching spans advanced topics like image processing, pattern recognition, and discrete mathematics. Collaborators include leading institutions such as the University of Delaware, Lund University, and the National Institute on Aging (NIH). Research interests include image analysis, machine learning applications in healthcare, and automated tissue quantification. His publications address cell segmentation, osteoporosis classification, and MRI/CT segmentation techniques. He serves as editor for journals like BioMedical Engineering OnLine and Frontiers in Computer Science, contributing to academic dissemination and peer review. The MIVIC Lab under his leadership develops innovative imaging solutions for clinical and environmental challenges.

Philippe Poncetمشاهده پروفایل

استاد

• Numerical Analysis
• High Performance Computing
• Fluid Dynamics
• +۵ مورد دیگر

Philippe Poncet is a Professor at the Université de Pau et des Pays de l'Adour (UPPA) and Vice-President for Digital Environment. He leads the Numerical Methods and Complex Fluids team at the Laboratory of Mathematics and their Applications (LMAP). His expertise spans particle/vortex methods, high-performance computing, and applications in geoscience, microfluidics, and biomathematics, with a focus on reactive flows and porous media. Poncet's research integrates numerical analysis with practical challenges in environmental and biomedical engineering. Key interests include Lagrangian methods for fluid dynamics, optimization algorithms, pore-scale modeling, and cystic fibrosis biomechanics. His work bridges theoretical mathematics and industrial-scale simulations. Recent publications demonstrate advances in pore-scale dissolution modeling, Bayesian neural networks for inverse problems, and viscous flow simulations. His articles consistently address multi-scale challenges in geosciences and biomedicine using innovative computational frameworks. He has led significant projects including the ANR-funded MucoReaDy (mucus dynamics in respiratory diseases) and MicroMineral (CO₂ mineral storage). These collaborations involve multidisciplinary teams addressing environmental and health-related fluid dynamics.

Yue Zhuoمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Biophotonics
• Machine Learning in Healthcare
• +۳ مورد دیگر

Yue Zhuo is a Research Scientist in Radiology & Biomedical Imaging at Yale School of Medicine. His work focuses on developing advanced imaging technologies using photonic materials, machine learning, and molecular imaging techniques. Key areas include tumor volume delineation for radiotherapy planning, label-free stem cell differentiation imaging, and optimizing MRI/PET modalities for clinical applications. He holds appointments in the PET Core and collaborates extensively with researchers like Thibault Marin and Chao Ma. His lab develops novel biosensors and microscopy techniques, such as Photonic Resonator Outcoupler Microscopy (PROM) and Photonic Crystal Enhanced Microscopy (PCEM). These innovations enable high-resolution, non-invasive monitoring of cellular processes and biomolecular interactions. Zhuo's publications emphasize translating photonic technologies into clinical settings, with recent work addressing inter-observer variability in radiation oncology and improving cardiac imaging accuracy. His research bridges biomedical engineering, data science, and clinical radiology to advance diagnostic precision and therapeutic planning.

Gerhard Adamمشاهده پروفایل

استاد

• Radiology
• Diagnostic Imaging
• Interventional Radiology
• +۵ مورد دیگر

Gerhard Adam is a Professor and Director of the Department of Diagnostic and Interventional Radiology and Nuclear Medicine at the University Medical Center Hamburg-Eppendorf (UKE), which is affiliated with the University of Hamburg's Faculty of Medicine. With extensive expertise in radiology, he leads clinical and research activities in diagnostic and interventional radiology, focusing on advanced imaging techniques including spectral CT, 4D flow MRI, and nuclear medicine applications. His research interests encompass cardiovascular imaging, oncological radiology, liver imaging, and musculoskeletal diagnostics. Professor Adam has made significant contributions to the field of radiology with over 600 publications, particularly in the areas of 4D flow MRI applications, spectral CT techniques, and interventional radiology procedures. His work spans from technical development of imaging modalities to clinical applications across multiple organ systems. Analysis of his recent publications reveals a strong focus on quantitative imaging biomarkers, with particular emphasis on dual-energy CT applications, cardiac MRI in various disease states, and advanced techniques for tissue characterization. His research demonstrates a consistent trajectory toward more precise, quantitative imaging approaches that bridge diagnostic and therapeutic applications. Awards mentioned in his CV (specific details not provided in source material) Professor Adam's work demonstrates significant impact on clinical radiology practice, particularly in developing and validating advanced imaging techniques for cardiovascular, hepatic, and oncological applications. His research collaborations span multiple medical specialties, reflecting the interdisciplinary nature of modern medical imaging. The Department of Diagnostic and Interventional Radiology and Nuclear Medicine under his leadership serves as a major referral center for complex imaging and interventional procedures.

Alexandr Malusekمشاهده پروفایل

مدرس ارشد

• Medical Physics
• Spectral CT Imaging
• Multi-energy CT
• +۷ مورد دیگر

Dr. Alexandr Malusek is a Senior Lecturer at Linköping University's Division of Radiological Sciences, Department of Diagnostics and Specialist Medicine. His work focuses on quantitative tissue classification using spectral and multi-energy CT, and improving radiation dosimetry with KAP-meter calibration. Specialization : Quantitative CT, Internal Dosimetry, Image Quality Optimization Key Tools : DIRA Algorithm, Monte Carlo Simulations, Photon-counting CT Research Interests : Developing advanced CT techniques for tissue characterization and radiation therapy planning. His work addresses challenges in creating mathematical models to fill gaps in spectral CT data and extends dosimetry models to quantify uncertainties in radiation exposure calculations. Publication Trends : Focus on Dual-energy CT , Photon-counting Detectors , Monte Carlo Simulations , and Radiation Safety . Recent work emphasizes automated bone analysis , image reconstruction accuracy , and uncertainty quantification in dosimetry. Labs & Collaborations : Affiliated with the Center for Medical Image Science and Visualization (CMIV) , conducting interdisciplinary research bridging medicine and technology.

• Modeling and characterization of photon-counting detectors
• Dual-energy imaging in material science and non-destructive testing
• Dual-energy and spectral imaging in clinical applications
• +۳ مورد دیگر

Thorsten Sellerer is a Researcher at the Technical University of Munich (TUM) , affiliated with the Department of Physics under the Faculty of Medicine . He is a key contributor to the Chair of Biomedical Physics , led by Prof. Dr. Franz Pfeiffer, and collaborates with the Munich Institute of Biomedical Engineering and Institute for Advanced Study . Research Interests: Modeling and characterization of photon-counting detectors Dual-energy imaging in material science and non-destructive testing Dual-energy and spectral imaging in clinical applications Spectral phase-contrast and darkfield imaging His work focuses on advancing X-ray imaging techniques, including spectral and phase-contrast CT, with applications in biomedical diagnostics and material science . He has published extensively on topics like lung volume estimation , bone density analysis , and photon-counting detector technology , often collaborating with TUM colleagues and international teams. His research integrates deep learning for image analysis and quantitative material decomposition to improve diagnostic accuracy. Thorsten has presented at major conferences, including RSNA (2017, 2019) and SPIE (2017) . He may be contacted via email at thorsten.sellerer@tum.de for collaborations or inquiries.

Henning Richterمشاهده پروفایل

پژوهشگر

• Diagnostic Imaging
• Translational Research
• Veterinary Surgery
• +۵ مورد دیگر

Dr. Henning Richter is a researcher specializing in diagnostic imaging and translational research for veterinary applications. He leads the Diagnostic Imaging Research Unit (DIRU) at the Vetsuisse Faculty, University of Zurich , while also affiliated with the Clinic for Neuroradiology, University Hospital Bonn . His work focuses on clinical imaging modalities, animal model development, and Good Laboratory Practice (GLP) compliance. University of Zurich: Head of DIRU, Clinic for Diagnostic Imaging University Hospital Bonn: Clinic for Neuroradiology DIRU provides advanced imaging services (X-ray, CT, MRI, etc.) for both small/large animals and basic research, emphasizing interdisciplinary collaboration. Dr. Richter holds certification in Laboratory Animal Science (Dipl. SVLAS) and contributes to studies spanning veterinary neurology, pharmacology, and ecological impacts on pollinators. Recent publications highlight his expertise in: Veterinary diagnostic imaging (CT/MRI validation, ADC quantification) Translational pharmacology (gadolinium contrast agents, gastroretentive drug delivery) Neuroscience (epilepsy, CSF hemoglobin toxicity, haptoglobin therapy) Ecological and biomechanical studies (bee overwintering, bone wear analysis)

• Cardiology
• Internal Medicine
• Pulmonary Embolism
• +۳ مورد دیگر

Jesper Kjærgaard serves as a Clinical Associate Professor within the Department of Clinical Medicine at the University of Copenhagen, specializing in Internal Medicine: Cardiology. His clinical and research activities are centered at Rigshospitalet (Blegdamsvej 9, 2100 Copenhagen Ø), affiliated with the university's medical school. His research focuses on critical cardiovascular conditions including pulmonary embolism, cardiac arrest outcomes, and post-resuscitation care. Notable areas include troponin dynamics in pulmonary embolism, brain health trajectories following critical illness, and AI applications in cardiac imaging. His work frequently involves multicenter clinical trials such as the BOX and REVIVAL studies, with strong emphasis on biomarker analysis and therapeutic interventions. Analysis of his recent publications reveals consistent contributions to high-impact cardiology journals, with significant focus on pulmonary embolism diagnostics, cardiac arrest neurology, and diabetes management in cardiac surgery patients. His research integrates clinical cardiology with critical care medicine and advanced imaging techniques. He maintains active collaborations with leading cardiology researchers including Hassager, Køber, and Fosbøl at Rigshospitalet, and participates in international consortia for cardiac arrest research. His clinical work appears embedded within Copenhagen University Hospital's cardiology department, with research outputs spanning clinical trials, cohort studies, and imaging methodology development.

Zacharopoulos Athanasiosمشاهده پروفایل

پژوهشگر

• Biomedical Imaging
• 3D Image Reconstruction
• Inverse Problems
• +۵ مورد دیگر

Dr. Zacharopoulos Athanasios is a Researcher at the Foundation for Research and Technology Hellas (FORTH), where he has been a Research Associate since 2009. He holds a PhD in Computer Science from University College London (UCL), focusing on three-dimensional shape-based reconstructions in medical imaging. His research spans biomedical imaging, 3D reconstruction algorithms, inverse problems, and optical tomography. He has held positions at institutions including UCL’s Centre for Medical Image Computing, Harvard Medical School, and the University of Kuopio. Education: BSc in Mathematics (1999, Aristotle University of Thessaloniki), MSc in Vision Imaging and Virtual Environments, and a PhD from UCL’s Computer Science Department. Research interests include 3D image reconstruction, optical projection tomography, and fluorescence molecular tomography. His work addresses challenges in imaging through scattering layers and improving light propagation models for biomedical applications. Key contributions include phase-retrieved optical projection tomography and advancements in light sheet microscopy for in vivo imaging. Collaborations span academic and clinical institutions globally.

Usman Mahmoodمشاهده پروفایل

استادیار

• Medical Imaging Physics
• Artificial Intelligence in Radiology
• Quantitative Imaging
• +۴ مورد دیگر

Dr. Usman Mahmood serves as Assistant Professor at Weill Cornell Graduate School of Medical Sciences and assistant attending physicist at Memorial Sloan Kettering Cancer Center (MSKCC). He directs the X-ray methods and computed tomography physics course within Weill Cornell Medical College's biomedical imaging graduate program, bridging medical physics, artificial intelligence, and oncological imaging to enhance diagnostic accuracy while prioritizing patient safety and equity. His educational background includes: B.S. in Physics from Stony Brook University (cum laude) M.S. in Medical Physics from Columbia University Ph.D. from Rochester Institute of Technology (completed while working at MSKCC) Dr. Mahmood's research focuses on developing quantitative imaging and AI methodologies for precise tumor characterization in radiological imaging. His lab leverages invisible quantitative features from medical images to enhance cancer diagnosis and treatment, with emphasis on data quality and reliability. Current projects include AI-driven tumor characterization, 3D printing for imaging validation, ethical radiological AI tools in oncology, and quality assurance methodologies. His work addresses AI's potential biases while advancing diagnostic accuracy through rigorous validation frameworks. His publication portfolio demonstrates consistent innovation at the medical physics-AI intersection, with recent work emphasizing practical validation frameworks using 3D-printed phantoms that replicate tumor micro-textures. A recurring theme is establishing tangible benchmarks for AI tools in clinical settings, ensuring algorithmic predictions maintain clinical relevance while mitigating risks associated with radiological AI deployment. Notable distinctions include: 2020 MSK Physics Clinical Service Award for work in CT 2018 President of Radiological and Medical Physics Association of NY 2014 Diplomate, American Board of Radiology - Diagnostic Imaging Physics As a board-certified diagnostic medical physicist, Dr. Mahmood mentors students in developing 3D-printed phantoms that replicate complex anatomical shapes of tumors. His leadership extends to directing educational programs and professional associations. His research utilizes institutional resources at MSKCC to develop large-scale datasets for training AI systems in cancer classification on CT scans, with emphasis on clinical translation and ethical implementation. Dr. Mahmood leads a research group focused on bridging physical and digital realms in medical imaging. His lab develops 3D-printed phantoms providing real-world benchmarks for validating quantitative imaging features, enhancing AI diagnostic reliability through physical counterparts to digital images. This approach ensures technically advanced solutions maintain clinical significance and patient safety as primary considerations.

Hannelore Derluynمشاهده پروفایل

پژوهشگر ارشد

• Geomechanics
• Porous Media
• Salt Crystallization
• +۵ مورد دیگر

Hannelore Derluyn is a CNRS Research Fellow at the Laboratory of Complex Fluids and their Reservoirs (LFCR) within the University of Pau and Pays de l'Adour. Her research focuses on geomechanics of porous media, particularly salt crystallization-induced damage mechanisms in rocks. She received her doctorate from ETH Zurich (2012) and Master's from KU Leuven (2006), with previous positions at Ghent University and ETH Zurich. Research Focus Derluyn's work bridges experimental and computational approaches to understand crystallization damage in porous materials. Using advanced techniques like X-ray tomography, she studies how environmental factors (temperature, humidity, salt concentration) trigger rock degradation. Her research has applications in heritage conservation, CO₂ storage, and geothermal energy. The ERC-funded PRD-Trigger project aims to establish predictive models for salt-induced damage at the pore-network level. Awards and Recognition ERC Starting Grant (2019) for pioneering work on precipitation-triggered rock dynamics ETH Zurich Medal (2013) for outstanding doctoral thesis FWO Research Grant and Fellowship (2013-2016) from Research Foundation Flanders Best Reviewer Award from Materials and Structures (2015) Research Infrastructure At LFCR, Derluyn utilizes synchrotron X-ray microtomography and neutron radiography for in-situ observation of crystallization processes. Her work involves collaborations with European institutes including EMPA Dübendorf and KU Leuven, focusing on multi-scale analysis of geomaterial behavior under environmental stress.

• Computational Mechanics
• Machine Learning
• Inverse Problems
• +۷ مورد دیگر

Assad Anshuman Oberai is a Professor at the University of Southern California's Viterbi School of Engineering . His research spans computational mechanics, inverse problems, and machine learning applications in nuclear engineering and biomedical imaging. Key Research Areas: Multi-fidelity modeling, Bayesian inference in mechanics, conditional GANs for posterior estimation, ultrasonic sensing of nuclear fuel canisters, mechano-microscopy for tumor elasticity imaging Methodological Contributions: Novel operator network architectures, dimension-reduced Bayesian frameworks, active noise cancellation techniques, and error estimation in variational multiscale methods Recent work focuses on non-invasive nuclear fuel inspection using ultrasonic sensing and machine learning, achieving significant signal-to-noise ratio improvements with active noise cancellation. In biomedical domains, his team develops cGAN-enhanced elasticity imaging for cancer differentiation, outperforming traditional algebraic methods. Methodologically, he pioneered VarMiON (Variationally Mimetic Operator Networks) with rigorous error analysis, demonstrating superior performance over DeepONet in partial differential equation approximations. His publications address high-dimensional inverse problems through generative models, with applications in epidemic modeling (capturing algebraic decay via behavioral feedback), 3D traction microscopy accounting for cell-induced matrix degradation, and compressible phase change simulations using discontinuous finite element methods. Co-authors include researchers from mechanical engineering, biomedical informatics, and nuclear safety domains.

Ram Kumar Selvarajuمشاهده پروفایل

پژوهشگر

• Molecular Imaging
• Positron Emission Tomography (PET)
• Drug Development
• +۵ مورد دیگر

Ram Kumar Selvaraju is a Researcher at Uppsala University , affiliated with the Department of Medicinal Chemistry and the Preclinical PET-MRI Platform . With a background in biotechnology and nuclear medicine, his work focuses on molecular imaging techniques for diabetes and cancer research. Education : PhD in Pharmacy (Uppsala University, 2015), MSc in Medical Nuclide Techniques (Uppsala University, 2011), BTech in Biotechnology (VIT University, 2009) Research Areas : Development of PET/SPECT/CT tracers for beta cell imaging , diabetes , and neuroendocrine cancer . Techniques include autoradiography, small animal modeling, and image analysis with PMOD/AMIRA. Publication Trends : Recent work emphasizes theranostic nanoprobes , dual-nuclide radiopharmaceuticals , and beta cell mass quantification using [11C]5-HTP and [68Ga]Exendin-4. Collaborations span oncology, diabetes, and imaging technology development.