جست‌وجوی استاد و پژوهشگر

• Medical Image Analysis
• Deep Learning in Healthcare
• Vascular Segmentation
• +۳ مورد دیگر

Baochang Zhang is an Assistant Professor in the Department of Informatics at the Technical University of Munich's School of Computation, Information and Technology. He is affiliated with the Chair of Computer Applications in Medicine (Prof. Navab) at the Garching campus, specializing in medical image analysis and AI-driven healthcare solutions. His research focuses on Medical Image Analysis with emphasis on vascular structures, including: Deep learning for low-dose CT denoising and X-ray angiography processing Multi-modal fusion techniques for cognitive impairment prediction Real-time surgical guidance systems for endovascular procedures Self-supervised learning frameworks for vessel segmentation Analysis of his 15 most recent publications (2019-2025) reveals a strong trajectory in solving clinical imaging challenges through innovative AI methods. His work consistently bridges computer vision and clinical applications , with increasing focus on zero-shot learning, domain adaptation, and surgical robotics integration since 2022. Key trends include replacing traditional segmentation with diffusion models and addressing missing data in multi-modal clinical datasets. No scientific awards were documented in the provided materials. While no formal advisees are listed in the scraped data, his lab appears to focus on translational medical AI projects with strong industry and clinical partnerships. The publications suggest active grant funding in EU medical technology initiatives, particularly for intraoperative imaging systems and neurodegenerative disease prediction tools. Zhang leads research within TUM's medical imaging group under Prof. Navab, likely contributing to the CAMP (Computer Aided Medical Procedures) Lab ecosystem. His team develops clinical decision support systems with emphasis on real-time vascular analysis during interventions.

• Biomechanics
• Computational Biomechanics
• Finite Element Analysis
• +۷ مورد دیگر

Karol Miller is a Winthrop Professor of Applied Mechanics at The University of Western Australia and a Visiting Professor of Radiology at Harvard Medical School. He holds a MSc in aerospace engineering, a PhD in robotics, and a DSc in biomechanics. His research focuses on computational biomechanics, particularly in the biomechanics of the brain and vascular systems, aiming to advance personalized medicine through technology integration. Key roles: Editor of Modelling in Biology and Medicine , Associate Editor of International Journal for Numerical Methods in Biomedical Engineering , and former Associate Editor of Annals of Biomedical Engineering . Education: MSc (Warsaw), PhD (Warsaw), DSc (Warsaw) Research interests include patient-specific engineering, finite element analysis, and biomechanical modeling of brain-skull interfaces and abdominal aortic aneurysms. His lab, the Intelligent Systems for Medicine Laboratory (ISML), develops technologies to improve clinical outcomes. Notable projects include real-time brain shift correction in neurosurgery and biomechanics-meets-phenomics studies for AAA disease progression. Awards: Humboldt Research Award, NVIDIA GPU Computing Champion, UWA Student Guild Choice Award, and multiple teaching recognitions. Grants: Over 50 projects funded by ARC, NHMRC, NIH, and international agencies. Active projects include real-time neural network-based brain shift correction and computational biophysics integration in 3D Slicer. Labs/Teams: ISML, collaborations with Harvard Medical School, and leadership in computational biomechanics software development (e.g., SlicerCBM framework).

Michael Geoffrey Jamesonمشاهده پروفایل

پژوهشگر

• Medical Physics
• Radiation Oncology
• MRI
• +۹ مورد دیگر

Dr. Michael Geoffrey Jameson is a Medical Physics researcher at the University of New South Wales, Medicine & Health faculty, based at St Vincent's Clinical School. His work focuses on improving radiotherapy accuracy and precision through innovative research across the entire treatment planning and delivery process. Education: Ph.D., University of Wollongong, NSW, Australia (2016) B.Med.Rad.Phys (hons1), University of Wollongong, NSW, Australia (2007) His research interests center on Medical Physics, Radiation Oncology, MRI applications, Image Processing, Radiation Dosimetry, and 3D Printing. Dr. Jameson has made significant contributions to understanding uncertainties in target delineation and has co-invented The HexaCheck medical device, now licensed to Standard Imaging Inc. His publications demonstrate a strong focus on adaptive radiotherapy, particularly for brain cancer treatment, with an emphasis on integrating MRI technology to improve treatment accuracy. Scientific Recognition: Cancer Institute NSW Early Career Fellowship recipient NHMRC Program Grant recipient Associate Editor, Australasian Physical & Engineering Sciences in Medicine (since 2014) Dr. Jameson serves as Principal Investigator on multiple research projects including the INTREPID trial for brain cancer outcomes and has secured significant funding from Cancer Institute NSW, NHMRC, and UNSW. His professional affiliations include the Australasian College of Physical Scientists and Engineers in Medicine, Australian Institute of Physics, American Association of Physicists in Medicine, and Trans-Tasman Radiation Oncology Group.

Salim Balikمشاهده پروفایل

استادیار بالینی

• Medical Physics
• Radiation Oncology
• Image-Guided Radiation Therapy
• +۳ مورد دیگر

Salim Balik, PhD, is a Clinical Assistant Professor of Radiation Oncology at the University of Southern California (USC) . He serves as the Director of the Medical Physics Residency Program at Keck Medicine of USC, focusing on advancing radiation therapy techniques through research and clinical innovation. Dr. Balik's research centers on medical physics , image-guided radiation therapy , and computational imaging . His work includes developing AI-driven segmentation models for brachytherapy, optimizing dosimetry in stereotactic body radiation therapy (SBRT), and analyzing motion effects in lung radiotherapy using dynamic virtual patient models. Recent publications emphasize GRID collimator-based spatial fractionation , HyperArc stereotactic radiosurgery , and 4D cone-beam CT integration . His scholarly contributions reveal trends in machine learning for tumor contouring , dosimetric validation for clinical trials, and adaptive radiation therapy for lung and spine cancers. Articles frequently address organ-at-risk sparing , intrafraction motion management , and heterogeneity correction algorithms .

Richard Ansorgeمشاهده پروفایل

مدرس ارشد

• Medical Imaging
• Biophysics
• High-Performance Computing
• +۵ مورد دیگر

Dr. Richard Ansorge is a Retired Senior Lecturer at the University of Cambridge and Fellow of Fitzwilliam College. He specializes in interdisciplinary research at the intersection of physics and life sciences, particularly in medical imaging technologies. His work focuses on advanced imaging modalities such as MRI/PET integration, dose calculation algorithms for proton therapy, and high-performance computing applications in medical imaging. Ansorge’s research interests include the physics of life, computational methods for medical imaging, and the development of novel imaging systems. He has contributed to advancements in PET/MRI co-registration, GPU-accelerated image processing, and radiation therapy optimization. His academic career has involved pioneering work in MRI signal theory, attenuation correction techniques, and small-animal imaging systems. Notable research trends in his publications emphasize the fusion of medical imaging modalities (e.g., PET/MRI), optimization of radiation delivery systems, and leveraging parallel computing (CUDA/GPU) for real-time medical applications. His work bridges fundamental physics principles with practical biomedical engineering solutions. Awards: No scientific awards explicitly mentioned in the provided texts. However, his contributions to medical imaging and particle therapy reflect significant impact in the field. Advising and grants: No specific student advisees or grant details provided. His research has likely been supported by institutional funding and collaborative projects at Cavendish Laboratory. Labs/Teams: Affiliated with the Cavendish Laboratory at the University of Cambridge, focusing on interdisciplinary physics and life sciences research.

David Shattuckمشاهده پروفایل

استاد مدعو

• Neuroimaging
• Computational Biology
• Brain Imaging
• +۲ مورد دیگر

David Shattuck is an Adjunct Professor in the Department of Neurology at the University of California Los Angeles (UCLA) School of Medicine. He specializes in neuroimaging and computational neuroscience , with a focus on developing advanced software tools for brain analysis. Academic Affiliation: University of California Los Angeles Department: Neurology Research Focus: Neuroimaging, Brain Segmentation, Cortical Thickness Analysis, Computational Biology Research Contributions: Dr. Shattuck is the principal investigator for NIH R01NS074980 , developing the BrainSuite software suite for multimodal brain imaging analysis and visualization. His work spans human connectome mapping , mouse brain atlases , and neurodegenerative disease imaging . Scientific Trends: His recent publications emphasize deep learning in neuroimaging (e.g., SwinUNETR for mouse brain segmentation), structure tensor analysis for brain tissue orientation, and VR environments for neuroimaging visualization. Keywords across his work include Neuroscience , Medical Informatics , Genetic Disorders , and Biomedical Imaging . Collaborations: Dr. Shattuck collaborates extensively with researchers in neuroimaging genetics , clinical neuroscience , and computational biology , including Arthur Toga, Paul Thompson, and Richard Leahy.

Jeffrey Yapمشاهده پروفایل

استاد پژوهشی

• Quantitative Imaging
• Cancer Therapy
• Multimodality Imaging
• +۶ مورد دیگر

Jeffrey Yap is a Research Professor in the Department of Radiology and Imaging Sciences at the University of Utah . He serves as the Director of the Center for Quantitative Cancer Imaging and Theranostics (CQCIT) , a multidisciplinary hub integrating radiopharmaceutical development, preclinical and clinical imaging, and theranostic technologies. His research focuses on quantitative multimodality imaging to evaluate novel cancer therapies in murine tumor models and clinical trials , with expertise in PET/CT, SPECT/CT, and theranostic radiopharmaceuticals . He holds a PhD in Medical Physics from the University of Chicago and a BS in Physics from the University of Dallas. Education PhD in Medical Physics, University of Chicago BS in Physics, University of Dallas Research Interests Quantitative imaging for cancer therapy evaluation Theranostics (diagnostic and therapeutic radiopharmaceuticals) 4D PET/CT in radiation treatment planning Body composition analysis in oncology Preclinical and clinical trial imaging Hypoxia and metabolic PET biomarkers Scientific Contributions Developed standardized imaging protocols for clinical trials (e.g., UPICT) Characterized autophagy resistance mechanisms in RAS-driven cancers Validated FDG-PET as a pharmacodynamic biomarker for targeted therapies Integrated body composition metrics into cancer survival analysis Laboratory & Facilities Director of CQCIT , operating two cGMP cyclotron labs North American Mediso Preclinical Imaging Center of Excellence Advanced digital PET/CT/SPECT/CT for clinical trials Image Analysis Core with Yunu software for tumor measurement

• Positron Emission Tomography (PET)
• Magnetic Resonance Imaging (MRI)
• Biomedical Informatics
• +۴ مورد دیگر

Thibault Marin, PhD, is an Assistant Professor at the Yale School of Medicine in both the Department of Radiology & Biomedical Imaging and the Biomedical Informatics & Data Science program. He is affiliated with the Bioimaging Sciences Division , Center for Brain & Mind Health , and Center for Molecular Imaging Technology and Translation (CMITT) . Education: PhD in Electrical Engineering from Illinois Institute of Technology (2010) Key Collaborations: Georges El Fakhri, Chao Ma, Yue Zhuo, Jinsong Ouyang, Paul Han His research focuses on PET/MRI instrumentation , dynamic imaging reconstruction , and motion correction algorithms . He develops diffusion models for tumor segmentation and Bayesian methods for kinetic parameter estimation, contributing to advancements in radiomics-driven radiotherapy and ultra-high resolution PET systems . Recent publications highlight innovations in TOF-DOI panel detectors , linear tangent space alignment (LTSA) for cardiac imaging, and adversarial domain generalization for PET denoising. His work bridges biomedical engineering , machine learning , and clinical imaging applications .

Rodrigo Morenoمشاهده پروفایل

دانشیار

• Medical Imaging
• Biomechanics
• Artificial Intelligence in Healthcare
• +۴ مورد دیگر

Rodrigo Moreno is an Associate Professor at the School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH) at KTH Royal Institute of Technology, and a Co-Principal Investigator (Co-PI) in two major research projects: 'Characterization of the mechanical tissue properties of the brain in the developing brain with magnetic resonance elastography' and 'Advanced Magnetic Resonance Elastography for the Brain.' He is also affiliated with Digital Futures, a cross-disciplinary research center focused on developing digital technologies to address societal challenges. His primary research interests include biomedical imaging techniques, biomechanical properties of tissues, and the application of artificial intelligence (AI) in healthcare. Dr. Moreno’s work spans neurodegenerative diseases like Alzheimer’s and Parkinson’s, as well as advancements in medical imaging modalities such as MRI, CT, and ultrasound. His recent projects emphasize precision medicine using AI-powered longitudinal MRI analysis and the mechanical characterization of brain tissue in developmental and disease contexts. He leads collaborations in computational methods like tensor voting and deep learning for medical image processing, segmentation, and generative modeling of biological structures. His contributions include developing algorithms for tractography filtering, synthetic data generation, and robust segmentation of medical images. Dr. Moreno’s research has produced over 50 peer-reviewed articles since 2014, with a focus on advancing imaging technologies and their clinical applications. His work bridges computational engineering, biomedical research, and digital innovation, aiming to improve diagnostics and personalized treatment strategies for complex diseases.

Songbai Jiمشاهده پروفایل

استاد

• Biomechanics
• Computational Modeling
• Medical Imaging
• +۴ مورد دیگر

Prof. Songbai Ji is a Professor of Biomedical Engineering at Worcester Polytechnic Institute (WPI), affiliated with the Mechanical Engineering department. His research focuses on integrating advanced neuroimaging with computational modeling to study traumatic brain injury (TBI) mechanisms, particularly in contact sports, and improving surgical image-guidance for spine procedures. He holds a BS and MS from Shanghai Jiaotong University, and MS and PhD from Washington University in St. Louis. His work combines biomechanical modeling, medical imaging techniques, and real-time data analysis to develop diagnostic tools for concussions and improve surgical accuracy in open spine surgery. Key innovations include a pre-computed brain response atlas for instant strain estimation and low-cost stereovision systems for intraoperative registration. He collaborates with clinicians and engineers to translate these technologies into clinical practice. Recent publications emphasize helmet design optimization, robotic-assisted surgery, and machine learning applications in injury prediction. His lab’s research spans biomechanics, medical imaging, and surgical navigation, with a focus on precision medicine and injury prevention.

Llado Bardera, Xavierمشاهده پروفایل

استاد

• Medical Image Analysis
• Computer Vision
• Image Processing
• +۱۱ مورد دیگر

Xavier Lladó Bardera is a Full Professor and ICREA Academia at the University of Girona, affiliated with the Department of Computer Architecture and Technology and the Computer Vision and Robotics Institute (VICOROB). He holds a PhD in Computer Engineering from the University of Girona and completed postdoctoral research at Queen Mary, University of London. B.S. in Computer Science, University of Girona (1999) Ph.D. in Computer Engineering, University of Girona (2004) Post-doctoral Research Assistant, Queen Mary, University of London (2004–2006) His research centers on medical image analysis, particularly in brain MRI, multiple sclerosis, prostate imaging, and deep learning. He has made significant contributions to lesion segmentation, tissue classification, and image registration, with a focus on clinical translation. His work integrates advanced computer vision techniques with biomedical applications, leveraging deep learning, non-rigid modeling, and multimodal imaging. The recent publications reflect a strong trend in deep learning for medical image analysis, especially in stroke, multiple sclerosis, and neonatal imaging. Key themes include lesion segmentation, hematoma expansion prediction, brain atrophy quantification, and end-to-end deep learning frameworks. There is a clear emphasis on clinical applicability, model interpretability, and handling real-world imaging challenges. Scientific Awards and Recognition: Best Project Prize – MAIA Master (Mladen Rakic, 2019) Best Project Prize – VIBOT Master (Jose Bernal, 2017) Best Project Prize – EINF Degree Project (E. Roura, 2011) Best Project Prize – EINF Degree Project (M. Cabezas, 2009) Best Project Prize – ETIG Degree Project (C. Fontanella, 2009) Best Paper Prize – Pattern Recognition (J. Salvi et al., 2010) Dr. Lladó has supervised over 20 PhD and master’s students, many of whom have continued in academia or industry (e.g., Icometrix, Johns Hopkins). He has been involved in numerous research grants and technology transfer initiatives, particularly in CAD systems for breast imaging and MS monitoring tools. His group actively collaborates with clinical partners, ensuring translational impact. He leads the NIC group at the VICOROB Institute, focusing on neuroimage computing and deep learning applications in neurology. The team develops open-source tools and participates in international challenges (e.g., BraTS, WMH), contributing to standardized evaluation in medical image analysis.

• Mapping the Human Connectome During Typical Aging
• Functional Neuroimaging
• Brain Structural Association Networks
• +۲۹ مورد دیگر

Roger P. Woods is a Professor-in-Residence in the Neurology department at the University of California, Los Angeles (UCLA) School of Medicine . His research focuses on neuroimaging , brain connectivity , and biomarker development for psychiatric and neurological disorders. NIH-funded projects include Mapping the Human Connectome During Typical Aging and Postnatal Development of Cortical Receptors Key methodologies: diffusion tensor imaging , functional MRI , brain atlas construction , and machine learning in neuroimaging Active collaborations with Katherine Narr , Shantanu Joshi , and Arthur Toga His 2023-2024 studies explore ketamine's impact on habenular connectivity , sleep deprivation effects on white matter , and neurodevelopmental consequences of prenatal alcohol exposure . Recent work emphasizes precision psychiatry through functional and structural connectivity analysis in depression and schizophrenia. Woods leads NIH R01 grants on cortical development and computational neuroimaging infrastructure , while contributing to Alzheimer's disease and nonhuman primate models of brain aging and genetic liability.

Timo Dickscheidمشاهده پروفایل

استاد

• Computer Vision
• Biomedical Imaging
• Neuroinformatics
• +۳ مورد دیگر

Prof. Dr. Timo Dickscheid is a Professor at Heinrich Heine University Düsseldorf and Working Group Leader for "Big Data Analytics" at the Institute of Neuroscience and Medicine (INM-1) at Forschungszentrum Jülich. His research focuses on developing high-resolution human brain atlases at cellular resolution using methods from Computer Vision, Big Data Analytics, and Neuroinformatics. He leads the Helmholtz AI unit for the research field "Information" and contributes to the European research infrastructure EBRAINS within the Human Brain Project. His work investigates methods for reconstructing 3D brain models at individual cell resolution, developing machine learning techniques for microscopic image analysis, and creating data management systems for terabyte-scale imaging data. Research interests include: Computer Vision for microscopic image analysis Distributed data management for high-throughput imaging Web services for 3D brain data access Machine learning with limited training data His publications show consistent focus on neuroimaging, computational anatomy, and machine learning applications in neuroscience, with recent emphasis on 3D reconstruction, diffusion models, and atlas integration.

• Medical Imaging
• Biomedical Engineering
• Radiology
• +۳ مورد دیگر

Chao Ma is an Assistant Professor of Radiology & Biomedical Imaging at Yale School of Medicine. He holds affiliations with the Bioimaging Sciences Center, Center for Molecular Imaging Technology and Translation (CMITT), and Yale Biomedical Imaging Institute. His research focuses on advanced imaging techniques including PET/MR integration, MRI reconstruction algorithms, and applications of machine learning in medical imaging. Dr. Ma earned his PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign (2013). Key research areas include: Development of novel imaging modalities for tumor diagnosis Diffusion models for medical image analysis Manifold learning (LTSA) for accelerated MRI PET motion correction using real-time MR data Quantitative MRI techniques for cardiac and brain imaging Recent work emphasizes: PET/MR fusion for brain tumor characterization Free-breathing cardiac ECV mapping AI-driven signal processing for MRSI data Dr. Ma collaborates extensively with researchers like Georges El Fakhri, Paul Han, and Thibault Marin. His work bridges engineering and clinical applications, advancing diagnostic precision in oncology and cardiology.

Stephen Ashwalمشاهده پروفایل

استاد

• Pediatric Neurology
• Neuroimaging
• Traumatic Brain Injury
• +۵ مورد دیگر

Stephen Ashwal, MD, is a Distinguished Professor in the Department of Neurology at the School of Medicine. With a career spanning over five decades, he has made significant contributions to pediatric neurology, neuroimaging, and traumatic brain injury research. Doctor of Medicine, New York University, 1970 Bachelor of Arts, New York University, 1966 His research focuses on: Pediatric neurology and neurocritical care Advanced neuroimaging techniques (MRI, MRS, DTI) Brain death determination in infants and children Stem cell therapy for hypoxic-ischemic injury Metabolomics in traumatic brain injury Neuroinflammatory mechanisms in juvenile brain trauma Analysis of his 15 most recent articles reveals trends in Neuroimaging biomarkers for pediatric brain injury Development of automated diagnostic tools (CT, MRI) Stem cell interventions in neonatal hypoxia-ischemia Consensus guidelines for disorders of consciousness Metabolic predictors of cognitive outcomes Neuroinflammatory pathways in pediatric neurotrauma Collaborations highlight partnerships with researchers at Loma Linda University (Holshouser, Tong, Obenaus) Elsevier publishers (Swaiman's Pediatric Neurology) Neurology and Pediatrics departments