Find Academic Supervisors

• Quantitative PET/CT and SPECT/CT Imaging
• Deep Learning in Medical Imaging
• Motion Correction Algorithms
• +2 more

Chi Liu is a Professor of Radiology & Biomedical Imaging at Yale School of Medicine . He serves as Associate Director of Biomedical Imaging Technology at the Yale Biomedical Imaging Institute and Director for Research Faculty Affairs in the Radiology & Biomedical Imaging department. Education : PhD from Johns Hopkins University (2008) Postdoctoral Training : University of Washington (2010) Certification : American Board of Science in Nuclear Medicine (Nuclear Medicine Physics and Instrumentation) His research focuses on quantitative cardiac and oncological PET/CT and SPECT/CT imaging , emphasizing deep learning algorithms , reconstruction algorithms , data correction , and dynamic imaging . Key clinical applications include early detection of chemotherapy-induced cardiotoxicity , multimodality imaging of heart failure , and motion variability elimination in therapy response assessment . The 15 most recent publications reveal a strong emphasis on deep learning techniques for low-dose imaging , motion correction , and cross-tracer generalizability in PET/SPECT systems. These works span applications in cardiac imaging , neuroscience , oncology , and theranostics . Scientific Award : Bruce Hasegawa Young Investigator Medical Imaging Science Award (2012) Contact: chi.liu@yale.edu | ORCID 0000-0002-7007-1037

• Medical Imaging
• Molecular Imaging
• Neuroscience
• +3 more

Steven Meikle is a Professor of Medical Imaging Physics and Head of the Imaging Physics Laboratory at the Brain and Mind Centre, University of Sydney. He also serves as Deputy Director (Preclinical) of Sydney Imaging and Deputy Director of the National Imaging Facility's Sydney node. His expertise spans advanced imaging technologies, with a focus on PET/SPECT instrumentation and molecular imaging. He holds a B.App.Sc.(Hons) from the University of Technology Sydney and a PhD from the University of New South Wales. Research focuses include developing novel PET systems like Open-field PET (for freely moving rodents) and Total Body PET, which enhance imaging sensitivity and enable real-time behavioral studies alongside brain function analysis. Collaborations include Tsinghua University (China) and UC Davis (USA). He leads projects on motion correction, quantitative imaging, and AI-driven analysis. Key achievements include over 180 peer-reviewed publications, editorial roles in Physics in Medicine and Biology , and leadership in professional societies. Awards include IEEE Senior Membership and Australian Institute of Physics Fellowship. Current student projects explore Total Body PET applications, motion correction, and radiopharmaceutical evaluation. Teaching roles include medical physics courses in diagnostic radiography and medical physics programs. He advises on imaging ethics, facility implementation, and translational research bridging basic science and clinical applications.

• Medical Image Reconstruction
• Computed Tomography
• Positron Emission Tomography
• +6 more

Professor Jerome Liang is a distinguished faculty member at Stony Brook University's Renaissance School of Medicine, holding professorships in Radiology, Biomedical Engineering, Electrical and Computer Engineering, and Computer Science. He serves as Co-Director of Radiology Research and has established himself as a leading expert in medical imaging reconstruction techniques. Dr. Liang's educational background includes a Ph.D. in Physics from City University of New York, postdoctoral training at Duke University, and fellowship at Albert Einstein College of Medicine. His undergraduate degree in Modern Physics was obtained from Lanzhou University in China. His primary research interests focus on advanced medical imaging techniques, particularly low-dose computed tomography image reconstruction, quantitative SPECT reconstruction, high-resolution PET imaging, tissue segmentation from multi-spectral images, computer-aided diagnosis systems, and virtual colonoscopy development. His work bridges engineering principles with clinical applications to improve diagnostic imaging capabilities while reducing radiation exposure. Analysis of his recent publications reveals a strong focus on machine learning applications in medical imaging, particularly in polyp classification, dual-energy CT spectral analysis, and virtual endoscopy. His research consistently aims to enhance diagnostic accuracy while optimizing radiation dose and improving visualization techniques for various medical conditions. 1981 China-US Physics Examination and Application Program (CUSPEA) Winner (Top 25 among 250,000 candidates) 1990 NIH First Investigator Award 1996 American Heart Association Established Investigator Award 1996 Radiological Society of North America Certificate of Merit Award 2002 SUNY Chancellor's Entrepreneur Award 2007 IEEE Society Fellow 2011-2013 SBU, BNL and CSHL Certificates of Excellence in Research and Invention 2013 Stony Brook School of Medicine Award for Excellence in Translational Research Dr. Liang has secured significant research funding including NIH/NCI R01 grants for "Advanced Virtual Colonoscopy for Early Cancer Screening" and "Radiogenomics of Colorectal Polyps." He currently leads active protocols including IRB 93995-MODCR005 focused on integrating virtual and optical colonoscopies with pathological analysis. His laboratory (IRIS - Imaging Research and Informatics) continues to advance medical imaging technology while mentoring the next generation of researchers in this critical field.

• Biomedical Imaging
• Medical Imaging Physics
• Motion Compensation in Imaging
• +5 more

Associate Professor Andre Kyme is an academic staff member in the School of Biomedical Engineering at The University of Sydney. His research focuses on developing enabling technologies for biomedical imaging, including motion compensation in MRI/PET, robotic platforms for image-guided therapy, and cross-disciplinary applications like plant salt uptake analysis using PET. He collaborates with institutions globally and advises students on projects like lameness detection in horses and AI-based motion correction. Research Interests: Kyme's work spans motion correction in medical imaging modalities, medical robotics integration with imaging systems, and innovative applications of imaging technologies in non-traditional fields. His team emphasizes leveraging advancements in computer vision, machine learning, and instrumentation to improve imaging performance and accessibility. Recent Projects: Current research includes MRI-compatible robotic platforms for therapy applications, AI-driven lameness detection in horses, and pediatric neuroimaging improvements. He leads the BREEZE initiative to enhance MRI accessibility for children with cerebral palsy through eye-gaze communication technology. Publications: His work spans 20+ years with over 50 peer-reviewed publications in journals like Physics in Medicine and Biology and IEEE Transactions. Key areas include PET/SPECT/CT motion correction algorithms, robotic systems for medical imaging, and novel imaging applications in plant science. Teaching: Kyme instructs core biomedical engineering courses including thesis supervision and capstone projects at both undergraduate and postgraduate levels. Labs/Teams: Active in the Brain and Mind Centre and Biomedical Imaging, Visualisation and Information Technologies groups at Sydney. Collaborates with industry partners like TeleMedVet and academic institutions including University of California Davis and Chinese University of Hong Kong.

Rune SundsetView profile

Associate Professor

• Nuclear Medicine
• Radiation Biology
• Radiopharmaceutical Development
• +3 more

Rune Sundset is an Associate Professor at UiT The Arctic University of Norway, affiliated with the Nuclear Medicine and Radiation Biology research group. His work focuses on radiopharmaceutical development, molecular imaging, and liposomal drug delivery systems. Research themes: Radioligand design, PET/SPECT imaging optimization, and nanoparticle-biology interactions Key collaborations: European Journal of Nuclear Medicine and Molecular Imaging, BMC Research Notes, and Acta Physiologica Recent publications highlight his contributions to: Development of Cu-67 and Ga-68 radiotracers for tumor imaging Liposomal formulations for arginase inhibition in cancer therapy Machine learning applications in dynamic PET imaging Sphingolipid effects on liposome uptake by human phagocytes In the SECURE project, he investigates radioligand therapy and imaging biomarkers. His research group (NMRB) spans nuclear medicine, radiation biology, and biomedical engineering, with applications in glioblastoma, prostate cancer, and neurodegenerative diseases.

• Medical Imaging
• Nuclear Medicine Physics
• Biomedical Engineering
• +3 more

Roger Fulton is a Professor of Medical and Preclinical Imaging at the University of Sydney and a Principal Nuclear Medicine Physics Specialist at Westmead Hospital's Department of Medical Physics. He is part of the Brain and Mind Centre and leads the Physics and Biomodelling team. His roles include Conjoint Professor in Medical Radiation Sciences, RHD student supervision, and coordination of the MRTY5134 (Computed Tomography Theory) and MRTY2107 (Imaging Technology 2) units of study. He holds a PhD in Applied Physics. His career has spanned collaborations with institutions like the Research Center Julich (Germany), Massachusetts General Hospital (USA), and the International Atomic Energy Agency (Austria). Professional recognitions include Fellow of ACPSEM, Senior IEEE Membership, and Chair of the Nuclear Medical Imaging Sciences Council (IEEE). Research interests focus on motion tracking/correction for SPECT, PET, and CT imaging, including patent innovations and leadership in radiation dose reduction techniques. He has secured $9.2M in competitive grants since 2004 and contributed to global standards through IAEA initiatives. Grants and advising: Over $9.2M in grants since 2004, advising PhD student Bader ALOUFI on liver imaging kinetics. His work bridges hardware engineering (e.g., Intel RealSense depth cameras) with software solutions (neural networks, Bayesian methods) to enhance imaging accuracy and reduce patient motion artifacts. Scientific Awards: US Patent (2020) ARC College of Experts (2016–2018) ACPSEM Fellowship IEEE Senior Membership IEEE Nuclear and Plasma Sciences Society leadership Labs/Teams: Physics and Biomodelling team at the Brain and Mind Centre, collaborating with international groups on preclinical imaging systems and awake-animal PET methodologies.

Daniel RückertView profile

Professor

• Artificial Intelligence in Medicine
• Biomedical Imaging
• Machine Learning
• +6 more

Prof. Dr. Daniel Rückert is the Alexander von Humboldt Professor for Artificial Intelligence at the Technical University of Munich , where he leads the Lab for AI in Medicine . His work bridges computer science, engineering, and medicine to develop AI and machine learning techniques for biomedical data analysis, with a focus on clinical translation. Research Pillars: Biomedical Imaging, Trustworthy AI, Medical Image Computing Clinical Applications: Dementia, Cardiovascular Disease, Cancer, Traumatic Brain Injury Research Focus : Develops AI methodologies for medical imaging workflows (image reconstruction, registration, segmentation) and clinical translation. Key areas include: AI for early disease detection and diagnosis Personalized therapies using multimodal data Privacy-preserving AI systems Cardiac/Neuroimaging applications Scientific Awards : ERC Synergy Grant (2013) & Advanced Grant (2020) Fellowships: British Computer Society (2010), MICCAI (2014), Royal Academy of Engineering (2015), IEEE (2015), Academy of Medical Sciences (2019), IAMBE (2020) Alexander von Humboldt Professorship (2020) Research Projects : Collaborations with Cornell University and University of Oxford on topics like: Hyperspectral neuronavigation for brain tumor surgery Super-resolution MRI techniques Longitudinal aging pattern analysis Medical report generation from clinical conversations

• Inverse Problems
• Geometry
• Partial Differential Equations (PDEs)
• +7 more

Sean Holman is a Senior Lecturer in Applied Mathematics at The University of Manchester, specializing in inverse problems, geometry, and partial differential equations. His research focuses on advancing theoretical frameworks and computational methods for imaging and material analysis, with applications in medical imaging, geophysics, and engineering. He is affiliated with the Industrial and Applied Mathematics and Inverse Problems research groups, contributing to the Digital Futures research beacon. His work integrates advanced mathematical techniques such as Radon transforms, tensor field analysis, and microlocal analysis to solve challenging inverse problems. Recent collaborations include studies on elastic strain reconstruction, electromagnetic parameter recovery, and agent swarm coordination under communication constraints. Dr. Holman has organized significant academic events like the 'Rich and Nonlinear Tomography' conference (2023) and the 'Microlocal Analysis meets Data Science' workshop (2018), fostering interdisciplinary research. His contributions span theoretical developments and practical applications in imaging modalities like SPECT and proton therapy range verification.

Timothy Garvey TurkingtonView profile

Associate Professor

• PET Imaging Physics
• Instrumentation
• Image Reconstruction
• +5 more

Timothy Garvey Turkington is an Associate Professor in the Department of Radiology at Duke University School of Medicine and a Faculty Network Member of the Duke Institute for Brain Sciences. Holding a Ph.D. from Duke University (1989), his academic career spans over three decades with continuous contributions to nuclear medicine physics. His research focuses on PET imaging physics, including instrumentation development, reconstruction algorithms, and image processing techniques. Key research thrusts include improving quantitative accuracy in PET, reducing scan times and radiation doses in PET/CT systems, developing novel imaging devices for PET and SPECT applications, and expanding PET's clinical utility in oncology, neurology, and cardiology. His work bridges theoretical physics with practical clinical implementation, particularly in breast imaging and quantitative biomarker development. Analysis of his 15 most recent publications reveals a strong emphasis on instrumentation optimization (particularly for breast imaging), quantification accuracy in clinical settings, and the development of standardized protocols for PET/CT systems. His work increasingly focuses on translational applications where physics innovations directly impact clinical decision-making, especially in cancer imaging and therapy response assessment. Dr. Turkington teaches graduate courses including PHYSICS 523: Modern Medical Diagnostic Imaging System, MEDPHY 782: Advanced Practicum for Clinical Development in Medical Physics, and MEDPHY 530: Modern Medical Diagnostic Imaging System, training the next generation of medical physicists. His research has been supported by significant grants including the RSNA 2016 Project (2014-2018), Simultaneous Emission and Transmission Mammotomography (2002-2014), and the Harmonized PET Reconstructions for Cancer Clinical Trails (2013-2014), demonstrating sustained funding for his innovative work in medical imaging physics.

• Medical Imaging
• Nuclear Medicine
• Tomography
• +3 more

Gengsheng L. Zeng, PhD, is an Adjunct Professor in the Department of Radiology & Imaging Sciences at the University of Utah. He holds a PhD in Electrical and Computer Engineering from the University of New Mexico and completed postdoctoral training at the University of Utah Medical Center. His research focuses on advanced imaging techniques in nuclear medicine, particularly SPECT and CT reconstruction algorithms. Education: PhD, University of New Mexico MS, University of New Mexico BS, Xidian University Research Interests: Medical imaging reconstruction algorithms Attenuation and scatter compensation Truncated data handling in tomography Geometric blurring correction Collimator design optimization Dynamic cardiac imaging techniques His work spans three decades, with over 50 peer-reviewed publications and 12 U.S. patents related to SPECT instrumentation and image processing. Zeng's algorithms address challenges in non-uniform attenuation correction , iterative reconstruction , and hybrid tomographic systems . His patents cover innovations like skew-slit collimators, rotating slat-hole detectors, and depth-dependent sensitivity corrections.

Gabriele SANTINView profile

Researcher

• Numerical Analysis
• Kernel Methods
• Approximation Theory
• +5 more

Gabriele Santin is a Researcher at Ca' Foscari University of Venice's Department of Environmental Sciences, Computer Science and Statistics. He holds a PhD in Computational Mathematics from the University of Padua and has held postdoctoral positions at the University of Stuttgart and the Bruno Kessler Foundation. His research focuses on kernel-based approximation methods, numerical analysis, and applications in scientific computing, including partial differential equations and biomedical engineering. Research interests include kernel interpolation, greedy algorithms, convergence analysis, and data-driven modeling. He has contributed to advancing numerical techniques for solving PDEs, optimizing kernel methods, and analyzing stability in non-Lipschitz domains. His work bridges theoretical foundations with practical applications in fields like medical imaging, transportation systems, and epidemic modeling. Publications highlight contributions to kernel-based greedy algorithms, image interpolation, and surrogate modeling. He is affiliated with the Research Institute for Complexity and actively collaborates with institutions like SimTech (University of Stuttgart). His expertise spans numerical methods, machine learning, and interdisciplinary problem-solving.

Cristian Tudorel BadeaView profile

Professor

• Medical Imaging
• Biomedical Engineering
• Radiology
• +5 more

Cristian Tudorel Badea is a Professor in Radiology and Biomedical Engineering at Duke University. He is a member of the Duke Cancer Institute and leads the Quantitative Imaging and Analysis Lab. His research focuses on micro-CT , photon-counting CT , and quantitative imaging biomarkers for preclinical studies. Education: Ph.D. in Electrical Engineering, University of Patras (Greece), 2001 Research Interests: Dr. Badea specializes in advanced imaging technologies, including deep learning-enabled CT reconstruction , nanoparticle contrast agents , and cardiac imaging in murine models. His work bridges preclinical research and clinical translation , particularly in cancer and cardiovascular diseases. Article Trends: His recent publications emphasize photon-counting micro-CT for cardiac perfusion , deep learning denoising algorithms , 5D imaging , and hybrid spectral CT techniques. Key subfields include tumor vasculature analysis , multi-contrast imaging , and neural network applications in preclinical settings. Labs & Collaborations: He collaborates extensively with the Duke Cancer Institute and leads the Quantitative Imaging and Analysis Lab. His work integrates multimodal imaging (CT, MRI, SPECT) and computational tools for co-clinical trials.

Georgios AngelisView profile

Research Fellow

• Medical Imaging
• Biomedical Engineering
• PET/SPECT Image Reconstruction
• +3 more

Dr Georgios Angelis is a National Imaging Facility Fellow at the University of Sydney, specializing in PET/SPECT image reconstruction and pharmacokinetic modeling. His research focuses on developing advanced imaging techniques for neuroscience and oncology applications, including motion correction algorithms, parametric image analysis, and deep learning approaches. He holds a PhD in Biomedical Engineering from the University of Manchester and has held postdoctoral roles at the University of Sydney's Brain and Mind Centre and the Northern Sydney Cancer Centre. His work bridges imaging physics and clinical practice, with contributions to open-field PET systems and tumor tracking for radiotherapy. Key research areas include: medical imaging algorithms, neurochemical PET analysis, machine learning in imaging, and translational imaging tools for freely moving subjects. Recent work emphasizes integration of MRI and PET data for improved spatial-temporal resolution, and development of Bayesian methods for neurotransmitter quantification. His projects frequently involve collaborations in oncology and neuroscience, with applications to both preclinical and clinical settings.

Glenn WellsView profile

Associate Professor

• Nuclear Medicine Imaging
• Cardiac Imaging
• Medical Physics
• +3 more

Dr. Glenn Wells is an Associate Professor at the University of Ottawa and an Adjunct Professor in Physics at Carleton University. He is a research scientist at the University of Ottawa Heart Institute, focusing on multimodality imaging combining nuclear medicine techniques like SPECT and PET with CT. His work addresses challenges in cardiac imaging, including improving accuracy through dynamic SPECT for myocardial blood flow measurement and optimizing novel camera designs. He holds advanced qualifications (PhD, FCCPM) and leads projects on scatter modeling, camera performance, and dose reduction in medical imaging. His research interests span nuclear medicine imaging technologies, particularly advancements in cardiac SPECT systems, scatter correction methods for solid-state detectors, and the development of quantitative imaging protocols. He collaborates with institutions like the University of Ottawa Heart Institute and Carleton University's Physics department to advance clinical applications of imaging technologies. Recent work emphasizes optimizing cardiac SPECT for disease detection and improving image quality through novel reconstruction techniques. His contributions bridge engineering and medicine, aiming to enhance diagnostic precision and reduce patient radiation exposure.

Debasis MitraView profile

Professor

• Machine Learning
• Deep Neural Learning
• Inverse Problems
• +7 more

Debasis Mitra is a Professor in the Department of Electrical Engineering and Computer Science at Florida Institute of Technology (FIT), with an affiliate role in the Biomedical Engineering and Science program within the College of Engineering and Science. He holds a dual Ph.D. in Physics from IIT Kharagpur and Computer Science from the University of Louisiana at Lafayette. His research focuses on inverse problems, medical imaging (e.g., SPECT/PET), machine learning, and spatio-temporal reasoning. He has contributed to projects like nuclear imaging reconstruction algorithms, muon tomography for contraband detection, and computational molecular biology. Education: Ph.D. in Computer Science (University of Louisiana at Lafayette), Ph.D. in Physics (IIT Kharagpur). Awards include the NSF Career Award and Fulbright Scholarship. His DM Lab develops algorithms for dynamic medical image analysis and radiomics. Collaborators include Lawrence Berkeley National Lab and UCSF. He advises graduate students on topics like tumor prognosis and 3D medical image analysis. Teaching includes courses on algorithms, topological data analysis, and medical imaging. Key projects span nuclear imaging reconstruction, muon tomography, and constraint reasoning systems. Labs and teams collaborate on interdisciplinary research blending computer science, physics, and biomedical applications.