جست‌وجوی استاد و پژوهشگر

• Cardiac Imaging
• Magnetic Resonance Imaging
• Computed Tomography
• +۶ مورد دیگر

Ronald M Peshock is a Professor of Radiology and Internal Medicine at the University of Texas Southwestern Medical Center (UTSW), with a career spanning over four decades since completing medical school at UT Southwestern in 1976. His academic journey includes Internal Medicine training at Parkland Health & Hospital System (internship 1977, residency 1979) followed by a Cardiology fellowship (1982). Dr. Peshock's research focuses on cardiac imaging, particularly using MRI and CT technologies to assess cardiovascular risk and disease. His work heavily involves the Dallas Heart Study, examining left ventricular hypertrophy, atherosclerosis, and cardiovascular risk factors across diverse populations. In recent years, he has become a prominent researcher in artificial intelligence applications for radiology, with numerous publications on AI implementation in clinical workflows, diagnostic accuracy improvement, and integration of AI into radiology training programs. His publication record shows consistent productivity from 1979 through 2025, with 218 total publications according to available records. Recent work demonstrates particular expertise in cardiac CT, pulmonary embolism imaging, and developing quantitative imaging biomarkers for cardiovascular disease. His Scopus profile indicates an h-index of 67 with 14,914 citations, reflecting significant scholarly impact in his fields. Dr. Peshock maintains active clinical and research affiliations with Parkland Health & Hospital System and serves as a key researcher in the Dallas Heart Study. His work examining ethnic differences in cardiovascular disease, left ventricular hypertrophy patterns, and aortic stiffness has contributed significantly to cardiovascular imaging literature.

Hannah Spitzerمشاهده پروفایل

استادیار

• Computational Biology
• Machine Learning
• Spatial Omics
• +۴ مورد دیگر

Hannah Spitzer is a Research Group Leader at the Institute for Stroke and Dementia Research (ISD) at Ludwig Maximilian University of Munich and an associated Research Group Leader at Helmholtz Munich's Computational Health Center. She leads the Spitzer Lab, focusing on computational analysis of multimodal brain datasets to advance understanding of neurovascular and neurodegenerative diseases. Her educational background includes: PhD in Computer Science from Heinrich-Heine University Düsseldorf and Research Center Jülich (2015-2020) Master's in Computer Science from RWTH Aachen (2013-2015) Bachelor's in Computer Science from RWTH Aachen (2009-2013) Dr. Spitzer's research integrates computational biology and machine learning to decode brain complexity, with emphasis on spatial omics analysis , interpretable image representation learning , and cross-modal data integration . Her group develops tools like squidpy and campa for spatial omics while applying graph neural networks to epilepsy lesion detection through the international MELD project, prioritizing biological interpretability in AI models. Recent publications reveal strong trends in leveraging graph neural networks for subtle brain lesion detection and creating computational frameworks for spatial omics integration. Her work consistently bridges advanced machine learning with clinical neuroscience to uncover disease mechanisms in neurodegeneration and vascular disorders. Dr. Spitzer actively mentors students including current PhD candidate Beatrice Guastella and alumni Deniz Fettahoglu (MSc) and Katia Berr (PhD). Her lab operates through major collaborations including the MELD epilepsy consortium and Helmholtz Imaging Project, with funding supporting computational pipeline development for small-vessel disease prediction and multimodal brain atlasing. The Spitzer Lab comprises postdoc Wasim Aftab and PhD student Beatrice Guastella, working on computational pipelines that integrate histology, spatial omics, and neuroimaging data to decode brain disease mechanisms through interpretable AI approaches.

• Neuroradiology
• Brain Imaging
• Central Nervous System Disorders
• +۳ مورد دیگر

Sandra Abi Fadel, MD serves as an Assistant Professor of Radiology and Biomedical Imaging at Yale School of Medicine and acts as Associate Program Director of the Neuroradiology fellowship at Yale New Haven Hospital. Her clinical work focuses on advanced neuroimaging techniques for diagnosing conditions affecting the brain, spine, spinal cord, neck structures, and salivary glands using multiple modalities including X-ray, CT, and MRI. Dr. Abi Fadel's research interests center on neuroradiology with specific focus areas in brain imaging, central nervous system disorders, head and neck imaging, and neurological diagnostics. She particularly emphasizes correlating patients' neurological symptoms with specific anatomic regions in the brain and abnormalities on brain imaging. Her work demonstrates how advanced imaging techniques can significantly impact the timely and effective management of neurological conditions. Her recent publications reveal a strong trend toward integrating advanced imaging techniques with genetic analysis and addressing representation issues in neurointerventional fields. Her work spans from clinical imaging applications to molecular-level analysis of brain tumors, demonstrating a comprehensive approach to neurological diagnosis and treatment. RSNA fellow research award (2020) Fellow of the year from Yale New Haven Hospital (2020) Dr. Abi Fadel serves as Associate Program Director of the Neuroradiology fellowship, indicating her active role in mentoring and training future neuroradiologists. Her professional service includes participation in the American Society of Spine Radiology (2019-2021) as both an Abstract Grader and Award Committee Member, and the American Society of Neuroradiology (2019-2021) as a Peer Reviewer.

• Medical Imaging
• Cardiovascular Imaging
• Cardiology
• +۳ مورد دیگر

Markus Henningsson serves as an Adjunct University Lecturer at Linköping University's Department of Health, Medicine and Care (HMV) and Department of Diagnostics and Specialist Medicine (DISP). His academic work centers on advanced medical imaging techniques with particular emphasis on cardiovascular applications. His research interests span Medical Imaging , Cardiovascular Imaging , and Artificial Intelligence in Medicine . Dr. Henningsson specializes in developing and applying novel MRI techniques for cardiac analysis, particularly focusing on left atrial structure, epicardial adipose tissue, and fibrosis segmentation. His work bridges engineering methodologies with clinical cardiology applications. Analysis of his recent publications reveals a strong trend toward integrating machine learning with medical imaging, particularly using deep learning architectures like geometric UNet for tissue segmentation. His research consistently addresses clinically relevant questions in atrial fibrillation and cardiovascular disease assessment through advanced imaging biomarkers. While no formal scientific awards are documented in the available information, his work appears in reputable journals including Scientific Reports , Physics in Medicine and Biology , and Magnetic Resonance in Medicine . His collaborative approach is evident through numerous co-authorships with researchers across cardiovascular sciences. Dr. Henningsson's work appears connected to Linköping University's Cardiovascular Sciences Unit, which conducts research in cardiovascular physiology, cardiology, and related surgical disciplines. His contributions support the department's focus on both basic and clinical cardiovascular research with direct patient care applications.

David Carmichaelمشاهده پروفایل

استاد

• Magnetic Resonance Imaging (MRI) Physics
• EEG-fMRI Integration
• Epilepsy Research
• +۳ مورد دیگر

Professor David Carmichael is a distinguished academic at King's College London , affiliated with the School of Biomedical Engineering & Imaging Sciences and the Department of Biomedical Computing . His research focuses on Magnetic Resonance Imaging (MRI) Physics , EEG-fMRI integration , and epilepsy neuroimaging , with a particular emphasis on mapping epileptogenic networks and optimizing MRI safety for concurrent neurophysiological recordings. Education : PhD in Medical Physics (UCL, 2004), MSci in Physics (UCL, 2000). External Role : Honorary Reader at UCL Great Ormond Street Institute of Child Health. His research interests span advanced neuroimaging techniques, including 7T MRI for pediatric epilepsy, quantitative susceptibility mapping to detect cortical abnormalities, simultaneous EEG-fMRI safety protocols, network-guided neuromodulation for treatment optimization. Recent publications highlight his work on ultra-high field MRI in drug-resistant pediatric cohorts, RF-induced heating safety during combined EEG-fMRI, image quality transfer for low-field MRI in developing regions, motion correction strategies in pediatric scans. He leads critical projects such as 7 Tesla Sodium MRI for Epilepsy (MRC-funded) and Minimal Motion MRI Systems (NIHR-funded), while contributing to global epilepsy research through the King’s Epilepsy Research Collective (KERC) .

• Biostatistics
• Neuroscience
• Cancer Research
• +۳ مورد دیگر

Shengxian Ding is a Postdoctoral Associate at the Yale School of Public Health , focusing on Biostatistics, Neuroscience, and Public Health . Her research develops advanced statistical models for biomedical data analysis. Her work includes 2025: Subgroup Mediation Analysis , 2024: Shape Mediation in Alzheimer’s Disease , and 2023: Tumor Growth Quantification via MRI , reflecting expertise in Regression Models, Neuroimaging, and Computational Biology . Contact: naomi.ding@yale.edu

• Biomedical Image Analysis
• Cryogenic Electron Microscopy (Cryo-EM)
• MRI and Ultrasound Image Segmentation
• +۵ مورد دیگر

Dr. Hemant Tagare is a Professor at Yale School of Medicine with primary appointments in the Radiology & Biomedical Imaging department and secondary appointments in Biomedical Engineering and Statistics . He is affiliated with multiple research centers, including the Bioimaging Sciences , Center for Brain & Mind Health , and Yale Biomedical Imaging Institute . Research Interests Cryogenic Electron Microscopy (Cryo-EM) for protein structure reconstruction MRI/ultrasound image segmentation using machine learning Non-rigid registration and shape theory Mathematical foundations of image analysis Articles Trends : Recent publications focus on cryo-EM particle picking consensus algorithms, T2-weighted MRI standardization, and IVIM perfusion measurement in peripheral artery disease. Earlier works emphasize segmentation techniques, non-rigid registration, and phantom validation. Scientific Awards Best Poster Award (2003) for ultrasound segmentation algorithm Francois Erbsmann Award (1993) for medical image processing National Science Talent Search Scholarship (1977) from India

Attila Keresztesمشاهده پروفایل

استادیار

• Cognitive Neuroscience
• Developmental Psychology
• Neuroimaging
• +۴ مورد دیگر

Attila Keresztes is an Assistant Professor in the Department of Cognitive Psychology at Eötvös Loránd University (ELTE), Budapest, Hungary. He serves as Principal Investigator of the Lifespan Memory Development Lab and Head of the Hippocampal Circuit and Code for Cognition Lab (HCCCL), a Max Planck Partner Group. His research focuses on hippocampal structure-function relationships across the lifespan, employing high-resolution MRI and experimental methods to study memory development, stress impacts, and cognitive aging. Keresztes holds a PhD in Cognitive Science from Budapest University of Technology and Economics (2014) and completed postdoctoral training at the Max Planck Institute for Human Development in Berlin. Key interests include hippocampal maturation’s role in memory specificity, longitudinal brain development, and the interplay between stress hormones (e.g., cortisol) and hippocampal morphology. His work has been funded by grants such as FK128648 from Hungary’s National Research, Development, and Innovation Office. In 2022, he received the prestigious Bolyai János Research Scholarship from the Hungarian Academy of Sciences. Current lab activities involve collaborations with the Max Planck Institute and the ELTE Babylab, including the NeMO study on early childhood memory development. The HCCCL team includes PhD students (Alex Ilyés, Zsuzsanna Nemecz, Hunor Kis) and student researchers focusing on neuroimaging, semantic memory, and pattern separation mechanisms. Public engagement initiatives include participation in events like the European Researchers' Night and science outreach for children.

Peter Swobodaمشاهده پروفایل

دانشیار

• Exercise and Cardiac Disease
• Heart Failure Diagnosis/Treatment
• Cardiac Imaging
• +۳ مورد دیگر

Dr. Peter Swoboda is an honorary Associate Professor in Cardiology at the University of Leeds and a consultant cardiologist. His research focuses on the interplay between exercise and cardiac disease, particularly in aging populations, funded by the British Heart Foundation. He also investigates advancements in heart failure diagnosis and treatment. Clinically, he specializes in cardiac imaging, leading the cardiac MRI service at Mid Yorkshire Teaching Hospitals Trust and serving on the British Society for Cardiovascular Magnetic Resonance (BSCMR) board. His research portfolio includes studies on myocardial fibrosis in endurance athletes, arrhythmia risk in veteran athletes, and the diagnostic utility of 4D flow MRI. He leads the CE-MARC 3 trial, evaluating cost-effective approaches to stable chest pain management. Over 15 peer-reviewed articles from 2025–2023 highlight his work in cardiac imaging, ischemia, and post-COVID cardiac involvement. No scientific awards are listed. His clinical roles include overseeing MRI services and contributing to national cardiovascular imaging guidelines. He actively collaborates on studies involving cardiac mechanics, genetics of cardiomyopathy, and exercise physiology impacts on cardiac structure.

Zhengwu Zhangمشاهده پروفایل

دانشیار

• Machine Learning
• Brain Connectomics
• Deep Learning
• +۴ مورد دیگر

Zhengwu Zhang is an Associate Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill. His research focuses on developing statistical and machine learning methods for analyzing high-dimensional neuroimaging data, particularly structural and functional brain connectomics. He leads the UNC Education Program of Intelligence and Connectomics (EPIC), an interdisciplinary initiative training students in brain network analysis. His work addresses challenges in large-scale neuroimaging datasets, including computational efficiency and reproducibility. Zhang completed his Ph.D. in Statistics at Florida State University under Anuj Srivastava. His funding includes NIH grants for CRCNS, structural connectome analysis, and personalized cognitive training. He serves as an Associate Editor for the Journal of the American Statistical Association (Reproducibility). Key contributions include tools like the Surface-Based Connectivity Integration (SBCI) GitHub repository for brain network analysis pipelines. His awards include the 2022 UNC Junior Faculty Development Award and the Oak Ridge Powe Award. Teaching roles include courses on data science, machine learning, and statistical consulting. His research spans brain network dynamics, genetic contributions to connectome structure, and applications of deep learning in neuroscience.

Peter J. Basserمشاهده پروفایل

استاد پژوهشی

• Quantitative Imaging
• Tissue Sciences
• Diffusion MRI
• +۷ مورد دیگر

Peter J. Basser is a leading research scientist at the National Institutes of Health (NIH), specifically within the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD), where he heads the Section on Quantitative Imaging and Tissue Sciences (SQITS). His work bridges physics, engineering, and neuroscience to develop non-invasive MRI methods for probing tissue microstructure, particularly in the brain. His educational background is not explicitly mentioned, but his scientific achievements reflect deep training in biophysics and medical imaging. He earned his Ph.D. and has built a career at NIH as a principal inventor of key neuroimaging technologies. Basser's research focuses on quantitative imaging and tissue sciences , especially using diffusion MRI to study brain structure and function. He pioneered Diffusion Tensor MRI (DTI) , Streamline Tractography , and advanced methods like MAP MRI , CHARMED , and AxCaliber , enabling in vivo measurement of axon diameters and microstructural features previously accessible only through histology. His work aims to translate these tools into clinical use for diagnosing developmental disorders, trauma, and neurodegeneration. The 15 most recent articles reflect a consistent focus on developing novel MRI biomarkers, particularly through diffusion and relaxometry methods. They explore water exchange, restriction, glymphatic clearance, latency connectomes, and cortical microstructure, demonstrating a trajectory toward in vivo MRI histology and precision imaging for pediatric and neurological applications. Scientific Awards: National Academy of Engineering (NAE), Inducted 2020 National Academy of Inventors (NAI) Fellow, 2024 Eduard Rhein Technology Award, 2021 ISMRM Gold Medal, 2008 ISMRM Lauterbur Lecturer, 2020 American Society of Neuroradiology Honorary Member, 2019 Victor M. Haughton Award, 2017 ISMRM Fellow, 2010 AIMBE Fellow Best Paper Award, Frontiers in Physics, 2023 Basser leads a dynamic research group that mentors postdoctoral fellows and trainees, many of whom have received prestigious awards. His lab has secured significant grants from the NIH BRAIN Initiative, NICHD, USUHS, and the Bill & Melinda Gates Foundation. The SQITS lab develops open-source software tools like TORTOISE , dmritool , and HI-SPEED , which are widely used in the neuroimaging community. The lab collaborates with institutions such as Uniformed Services University and participates in major initiatives like the Human Placenta Project and the Human Connectome Project. Basser’s vision is to transform clinical MRI scanners into quantitative scientific instruments for precision medicine and large-scale brain mapping. Labs and Teams: Section on Quantitative Imaging and Tissue Sciences (SQITS), NICHD, NIH Neuropathology-Neuroradiology Integration Core (with USUHS) Advanced Translational Neuroimaging Research & Development Core Diffusion – Data Processing Center (DPC)

Seth Dillardمشاهده پروفایل

عضو هیئت علمی

• Biomedical Engineering
• Mechanical Engineering
• Cardiovascular Biomechanics
• +۵ مورد دیگر

Seth Dillard is an Associate Teaching Professor in the Department of Mechanical Engineering and School of Biomedical Engineering at Colorado State University (CSU). He joined CSU in January 2022, teaching courses such as Introduction to Mechanical Engineering, Finite Element Analysis, Machine Design, and Problem-Based Learning in Biomedical Engineering. He advises Senior Design teams and honors students across both disciplines. In 2023, he introduced new courses including Senior Design and a Berlin-focused Education Abroad program. Education: PhD (2011) and BSE (2004) in Mechanical Engineering from the University of Iowa. He also holds an EMT-P certification (1996) and postdoctoral research experience at the University of Iowa's IIHR from 2011–2014. Research and teaching interests include cardiovascular biomechanics, image-based modeling, energy systems, and applying engineering principles to healthcare. His teaching innovations include courses like Quantitative Systems Physiology and Prosthetics Innovation in Ecuador, blending engineering with global health challenges. Dr. Dillard has received multiple teaching awards, including the College of Engineering Faculty Excellence Award (2020) and four Excellence in Teaching awards from the University of Iowa. He has supervised over 30 students in thesis and design projects and actively participates in academic service roles such as Associate Director of SBME and accreditation committee leadership. His research spans computational fluid dynamics, cardiovascular modeling, and biomedical device design. He has published extensively in journals like Journal of Neurosurgery and Theoretical and Computational Fluid Dynamics, with peer-reviewed conference contributions since 2005.

Yumin Linkمشاهده پروفایل

دانشیار مدعو

• Medical Imaging
• Neurological Disorders
• Ophthalmology
• +۳ مورد دیگر

Yumin Link is an Adjunct Associate Professor at Linköping University's Department of Biomedical and Clinical Sciences (BKV), affiliated with the Faculty of Medicine and Health Sciences. He is part of the Center for Social and Affective Neuroscience (CSAN) and the Division of Cell and Neurobiology (CNB). His research focuses on medical imaging techniques like Optical Coherence Tomography (OCT), neurological disorders, and clinical neuroscience. Key areas include retinal imaging in idiopathic intracranial hypertension, multiple sclerosis progression, and intermachine reproducibility in OCT measurements. Publications (2023–2025) highlight advancements in neuroimaging and diagnostic reliability. No explicit awards or grants are listed, though ongoing collaborations with clinical teams suggest active research participation. Yumin Link advises via two emails: yumin.link@liu.se (academic) and yumin.link@regionostergotland.se (clinical).

• Neuroscience
• Brain Networks
• Functional Connectivity
• +۴ مورد دیگر

Richard F Betzel is an Associate Professor in Neuroscience at University of Minnesota and affiliated with Indiana University and Northwestern University through collaborative research grants. As a leading network neuroscientist, he develops edge-centric approaches for analyzing brain network organization and dynamics. Current affiliations: University of Minnesota (primary), Indiana University (collaboration), Northwestern University (collaboration) Active research grants from: National Science Foundation (NSF), NIH National Institute on Aging, NIH National Institute of Neurological Disorders & Stroke His research focuses on: Understanding brain network reconfiguration during aging and cognitive tasks Developing computational tools for analyzing connectome architecture Exploring relationships between structural and functional connectivity Investigating network mechanisms in neurodegenerative disorders like Parkinson's Advancing mindfulness meditation research through network neuroscience Recent publications emphasize: Edge-centric network analysis methods Connectome organization across species Dynamic network approaches to social cognition Functional MRI analysis of co-fluctuations Network controllability and modular architecture Applications to both healthy aging and pathological conditions His work contributes to UN Sustainable Development Goals including healthy aging (SDG 3) and scientific knowledge advancement. Current projects include NCS-FO for edge-centric brain mapping (NSF-funded), social cognitive aging research (NIH), and TMS therapy network analysis (NIH).

• Biomedical Imaging
• Medical Image Processing
• Neuroimaging
• +۲ مورد دیگر

Dr. Manuel Desco Menéndez is a Full Professor at the Department of Bioengineering , Carlos III University of Madrid , focusing on biomedical imaging and instrumentation. He leads the Biomedical Imaging and Instrumentation Group. Key research domains: Medical Imaging , Neuroimaging , Biomedical Instrumentation Primary publication areas: Computed Tomography , PET Imaging , MRI , Biomaterials His scientific contributions (2022-2025) include: Neuroanatomical changes in pregnancy/postpartum and psychiatric disorders Advanced CT reconstruction algorithms with beam-hardening compensation Innovative biomedical nanoplatforms using milk exosomes Preclinical bone regeneration scaffolds in animal models Current technical developments focus on GPU-accelerated image processing and deep learning applications in tomographic reconstruction.