Diffusion Tensor Imaging Professors and Supervisors in Denmark

Mojtaba Zareiمشاهده پروفایل

پژوهشگر

University of Southern DenmarkDenmark

Mojtaba Zarei is a researcher at the Department of Clinical Research, Faculty of Health Sciences, University of Southern Denmark, with additional affiliations at Odense University Hospital (OUH) and Karolinska Institutet (KI). His primary research unit is the Neurology Research Unit in Odense, focusing on advanced neuroimaging techniques and their applications in neurological and sleep disorders. Dr. Zarei's research spans multiple domains within neuroscience, with particular expertise in Positron Emission Tomography (PET), Diffusion Tensor Imaging (DTI), and cognitive function assessment. His work frequently addresses Alzheimer's Disease, Parkinson's Disease, and insomnia disorders, utilizing both clinical and computational approaches. His fingerprint analysis shows strong activity in neuroscience (100% for PET), diffusion tensor imaging (66%), cognitive function (45%), and Alzheimer's Disease (40%). His recent publications reveal a clear trajectory toward integrating multimodal imaging techniques with machine learning approaches for improved diagnosis and understanding of neurological conditions. The work on OPETIA (Odense-Oxford PET Image Analysis) demonstrates his contribution to developing standardized tools for neuroimaging analysis. His research increasingly bridges computational methods with clinical neuroscience, as evidenced by his work on image stitching algorithms and machine learning applications for insomnia classification. Dr. Zarei actively collaborates with researchers across multiple institutions, with notable external collaborations visible on the international network map. His work has been mentioned by peer review sites, picked up by news outlets, and shared across social media platforms, indicating growing impact in his field. Within his research unit of Neurology in Odense, Dr. Zarei appears to be part of a multidisciplinary team working at the intersection of clinical neurology, advanced imaging, and computational analysis, contributing to both methodological development and clinical applications of neuroimaging techniques.

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Anders Bjorholm Dahlمشاهده پروفایل

استاد

Technical University of DenmarkDenmark

Anders Bjorholm Dahl is a Professor at the Department of Applied Mathematics and Computer Science , DTU Compute , Technical University of Denmark (DTU). His research focuses on medical imaging, computer vision, and biomedical engineering. He leads projects in ultrasound imaging, AI-driven medical diagnostics, and advanced imaging technologies for healthcare applications. Education: Ph.D. in Computer Science (Image Analysis and Computer Vision), DTU (2005–2009) Forestry, Royal Veterinary and Agricultural University (1997–2004) Research Interests: Combines machine learning and advanced imaging techniques to address challenges in medical diagnostics, including ultrasound super-resolution, stenosis detection in coronary angiographies, and material anisotropy analysis. His work bridges anatomy and histology using X-ray tomography and explores AI applications in healthcare. Key Projects: Crowd Counting through Remote Sensing (2025–2027) AI for Extreme Super-Resolution CT (2024–2026) Fighting Cancer with Generative AI (2024–2027) Labs/Teams: Leads the UltraSound and Biomechanics Visual Computing Center for Fast Ultrasound Imaging , focusing on real-time medical imaging solutions.

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Marco Pizzolatoمشاهده پروفایل

دانشیار

Technical University of DenmarkDenmark

Marco Pizzolato is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), specializing in Visual Computing with a focus on Magnetic Resonance Imaging (MRI), particularly diffusion MRI and biophysical modeling. He is also affiliated with the inter-departmental Microstructure & Plasticity (MAP) research group and has held visiting positions at the University of Verona, EPFL, and DRCMR. His educational background includes a PhD in Signal and Image Processing from INRIA Sophia Antipolis, a Master’s in Bioengineering from the University of Padua, and a Bachelor’s in Biomedical Engineering from the same institution. He previously served as an Assistant Professor at DTU and was a postdoctoral researcher under the Marie Curie COFUND Eurotech programme. Dr. Pizzolato's research centers on image and signal denoising, inverse problems, optimization, diffusion MRI, tractography, and Monte Carlo simulations. He actively contributes to the development of microstructural models for brain imaging, with applications in neurodegenerative diseases and brain connectivity. His work aligns with UN Sustainable Development Goals, particularly in advancing education and health through imaging technology. The recent publications reflect a strong trend in advancing diffusion MRI techniques, including ACID imaging, microscopic propagator modeling, myelin integrity mapping, and multi-scale white matter organization. These works emphasize biophysical accuracy, model validation, and integration across imaging modalities and species. Magna Cum Laude , ISMRM 2020 Magna Cum Laude , ISMRM 2022 First Place , Macaque Validation Challenge at ISBI 2018 First Place (Overall and HCP) , IronTrack Challenge 2019 (MICCAI) MICCAI Student Travel Award 2015 He has supervised PhD students such as Thøgersen, T. L. and Corral Bolaños, M. in projects related to microstructure MR imaging and myelin mapping. He has also been involved in significant grants and collaborative projects, including the Multimodal Microstructure-Informed Connectivity (MMINCARAV) initiative between Inria and EPFL, and the Sinergia consortium for Brain Communication Pathways . He co-organized multiple international events, including the MICCAI CDMRI workshops and challenges (2019–2021), and the ESMRMB Leaps in Microstructure Imaging workshop (2024). Dr. Pizzolato is an active member of the scientific community, serving as an editor for MICCAI workshop proceedings, a reviewer for major journals and conferences, and an invited speaker at ISMRM 2025. He leads and participates in several ongoing research projects at DTU focused on quantitative imaging, myelin mapping, and MRI-based connectivity, demonstrating sustained research leadership and external funding success.

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Markus Kiderlenمشاهده پروفایل

دانشیار

Aarhus UniversityDenmark

Markus Kiderlen is an Associate Professor at the Department of Mathematics, Aarhus University, specializing in the intersection of geometry, probability theory, and statistics. His research spans integral geometry, convex geometry, stochastic geometry, geometric tomography, and spatial statistics. Primary affiliation: Department of Mathematics, Aarhus University Academic rank: Associate Professor His work in stochastic geometry involves integral geometric relations, Minkowski tensors for quantifying random particles, and uniqueness/inference problems in geometric tomography (e.g., solving Hammer's X-ray problem). Another key focus is spatial sampling methods, including Wicksell's corpuscle problem and Cavalieri volume estimation improvements. Recent research trends include geometric reconstruction algorithms, stereological techniques for nonconvex particles, and rotational integral formulae for convex bodies. He has also explored polarizations in rearrangement processes and their applications. Markus Kiderlen has presented at international conferences such as MCQMC (2022), Stochastic Geometry Workshops (2019-2022), and the WIASABISS workshop (2018). His publications reflect collaborations with researchers like M. Stehr, R. Eriksen, and D. Hug.

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Kenny Erlebenمشاهده پروفایل

استاد

University of CopenhagenDenmark

Kenny Erleben is a Professor and Head of the IMAGE research section at the Department of Computer Science, University of Copenhagen . His work integrates physics-based simulation with data-driven approaches, advancing fields like robotics, machine learning, and computer graphics. He leads a team of 10 within IMAGE, a section housing 10 faculty, 2 postdocs, and 20 PhDs, with research foci in computer vision, medical image analysis, and numerical methods. Education: PhD in Computer Science, University of Copenhagen (2005) Erleben’s research interests span physics-based modeling and simulation , robotics automation , data-driven modeling , and digital twins . His team develops numerical methods for interactive simulation, bridging real-world data with synthetic models for applications in surgical simulators and computer games. Scientific production trends include differentiable simulation pipelines, collision detection algorithms, and friction modeling. Key applications involve NVIDIA’s PhysX/Isaac engines, surgical robotics, and medical imaging. His awards include four best paper prizes, reflecting his impact on simulation and robotics. Supervision: 15 PhDs, 8 Postdocs, and 170+ theses Grants: HORIZON-RIA (€6.19M), EU MSCA-ITN RAINBOW (€4M+), and other Innovationsfond/DFF awards Erleben’s community leadership includes coordinating the Danish Academy of Digital Interactive Entertainment (DADIU) and reviewing for 300+ scientific papers. He has held roles like Deputy Head of Department (2012-2015) and Scientific Advisor at Alexandra Institute.

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Peter Aggerمشاهده پروفایل

دانشیار

Aarhus UniversityDenmark

Peter Agger is an Associate Professor at the Department of Clinical Medicine, Aarhus University. A specialist paediatrician by training, he bridges clinical practice, research, and teaching, focusing on structural heart disease and congenital heart defects through advanced imaging techniques. Education: DMSc, PhD, MD in cardiac structure/function and paediatrics External Role: Speciallæge (Paediatric Specialist) at Regional Hospital of Randers since 2025 His research spans cardiac morphology, metabolism, and imaging innovations, with a focus on congenital heart disease , myocardial remodeling , and 3D visualization . Collaborations include EU-funded projects and international exchanges. Scientific awards include multiple Børnehjertefondens Forskningslegat grants (2013, 2015, 2021) and a PhD stipend from Aarhus University (2013). He teaches anatomy, physiology, and research theory, supervising students at all academic levels.

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Sami Brandtمشاهده پروفایل

استاد

IT University of CopenhagenDenmark

Sami Brandt is a Professor in Data Science and Machine Learning at the IT University of Copenhagen. Specializing in Audio-Visual Computing, they lead a research group focused on cutting-edge applications in computer vision, neural networks, and extended reality (XR). Their work bridges mathematical imaging with practical technological solutions. Research Interests: Latent Space Analysis in Diffusion Models Non-rigid Structure-from-Motion Techniques Human Motion Prediction and Smoothing Algorithms Tensor-based Methods for Emotion Recognition Metaverse Applications for Parkinson's Disease Therapies Scientific Awards: Best Student Paper Award (2022), 11th International Conference on Pattern Recognition Applications and Methods Projects: MotiVerseP (2024-2025): Motivation for movement therapies in a metaverse for Parkinson's disease XTREME (2024-2026): Extended Reality Environment for Immersive Experience of Art and Music

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Yousef Yavarianمشاهده پروفایل

دانشیار بالینی

Aalborg UniversityDenmark

Yousef Yavarian is a Clinical Associate Professor and Ledende overlæge at Aalborg University Hospital's Department of Radiology, affiliated with the Faculty of Medicine. His primary role involves clinical and academic work in radiology with a focus on neuroimaging applications. Research Interests include neuroimaging techniques like Diffusion Tensor Imaging, brain microstructure analysis in chronic conditions (e.g., diabetes, multiple sclerosis), stroke recurrence prediction through white matter hyperintensity studies, and rare neurological disorders like neurosarcoidosis. Notable Publications focus on clinical neurology applications of imaging technologies, including BPPV treatment innovations and case studies on atypical neurological manifestations. His work bridges clinical practice and advanced imaging diagnostics. Grants & Collaborations involve multi-institutional research (e.g., collaboration with Prof. Lip on stroke studies) and data sharing via platforms like Figshare. He has supervised at least one PhD student but no named advisees are listed. Labs/Teams include involvement with radiology departments and interdisciplinary teams focusing on neuroimaging applications in clinical settings.

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Siavash Arjomand Bigdeliمشاهده پروفایل

دانشیار

Technical University of DenmarkDenmark

Siavash Arjomand Bigdeli is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he conducts research in Visual Computing. His work spans computer vision, machine learning, and AI applications in medical imaging and food science. He is actively involved in interdisciplinary research and supervises multiple PhD projects. Research Interests: Dr. Bigdeli's research focuses on deep learning for visual data, particularly diffusion models, image synthesis, and privacy-preserving AI. He investigates how AI models can be made more robust, explainable, and fair, especially in sensitive domains like healthcare. His work also extends to texture analysis, scale-space theory, and structural modeling in both biological and food materials. The recent publications reveal a strong trend toward diffusion models , membership inference attacks , and counterfactual generation , indicating expertise in both generative modeling and AI security. These works are applied in domains such as medical imaging and computer vision, showing a balance between theoretical innovation and practical impact. Scientific Supervision and Projects: Supervisor in Human-XAI Collaboration for Improved Fetal Ultrasound Imaging (2025–2028) Supervisor in Bias and Fairness in Medical Imaging (2023–2026) Project Participant in EXCHEQUER: Examining Cheese Quality Elements and Reactions (2021–2025) He collaborates closely with researchers such as Aasa Feragen, Anders B. Dahl, and Jeppe R. Frisvad. His lab is part of DTU Compute’s Visual Computing section, contributing to both fundamental algorithms and real-world applications in AI-driven imaging.

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Simon Lysdahlgaardمشاهده پروفایل

پژوهشگر

University of Southern DenmarkDenmark

Simon Lysdahlgaard is a Visiting Researcher at the Department of Regional Health Research within IRS - Esbjerg and Grindsted Hospital's Research Unit of Radiology and Nuclear Medicine at the University of Southern Denmark's Faculty of Health Sciences. His work bridges clinical radiology, artificial intelligence applications, and evidence-based medical research methodologies. His primary research domains include: Artificial intelligence integration in radiography practice Radiomics and advanced MRI techniques for neurological conditions Systematic review and meta-analysis methodologies Diagnostic accuracy assessment in medical imaging Clinical applications in stroke treatment and bone density management Lysdahlgaard's publication pattern reveals growing influence in AI applications for medical imaging, with notable work on Nordic radiographer perspectives regarding AI adoption and technical studies on MRI analysis for brain tumors. His recent publications demonstrate strong interdisciplinary collaboration across European institutions. His significant recognition includes: Paul Dudley White International Scholar of the American Heart Association (awarded February 5, 2025) Lysdahlgaard actively contributes to academic discourse through conference participation including the ISRRT World Congress (2024 program committee) and European Congress of Radiology (2023), while maintaining teaching commitments focused on AI applications in radiography since 2022. His work shows substantial engagement with both technical imaging challenges and professional implementation considerations.

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Francois Bernard Lauzeمشاهده پروفایل

دانشیار

University of CopenhagenDenmark

Francois Bernard Lauze is an Associate Professor at the Department of Computer Science, University of Copenhagen, specializing in the Image Analysis, Computational Modelling and Geometry section. His research bridges mathematical image analysis and geometric methods for shape analysis in medical imaging. His primary research interests include variational methods, partial differential equations, and Riemannian geometry applied to image inpainting, motion estimation, segmentation, and medical imaging. Recent work focuses on physics-integrated deformable registration, SE(3) group equivariant neural networks, and multi-view 3D reconstruction techniques. The trends in his publications highlight interdisciplinary approaches combining geometry, computer vision, and deep learning. Notable examples include locally orderless networks, SE(3) group convolutions for DWI segmentation, and RNb-NeuS for 3D reconstruction. He is affiliated with the Pioneer AI section at DIKU, which anchors foundational research in artificial intelligence, machine learning, and medical imaging. The section hosts the Applied Geometry lab and participates in major international conferences like MICCAI and CVPR.

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