Craig H. Meyer is a Professor in Biomedical Engineering and Radiology & Medical Imaging at the University of Virginia. He holds a Ph.D. from Stanford University and leads the Rapid MRI Research Group, focusing on developing advanced MRI techniques for cardiovascular disease, neural disorders, and pediatrics. His work integrates physics, signal processing, and machine learning to improve MRI acquisition and processing speed. Education: Ph.D. in Biomedical Engineering, Stanford University. Research Interests: Medical and Molecular Imaging, Signal and Image Processing, Biomedical Data Sciences, Biomechanics, and Cardiovascular Engineering. His innovations include fast spiral imaging, conjugate phase reconstruction, and machine learning-enhanced MRI denoising. Awards: Notably includes the Dean’s Award for Excellence in Team Science (2014), Fellowships from NAI (2021), AIMBE (2015), and ISMRM (2013). He also authored two landmark MRI papers recognized as pivotal in the field. Teaching: Courses include BME 6310 (Computation and Modeling in Biomedical Engineering) and BME 8782 (Magnetic Resonance Imaging). He emphasizes translational research, with applications in clinical MRI advancements and collaborative interdisciplinary projects. Labs/Groups: Rapid MRI Research Group focuses on cutting-edge MRI technologies, including real-time cardiac imaging and artifact reduction through deep learning.
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
Xenophon Papademetris is a Professor of Biomedical Informatics & Data Science and Radiology & Biomedical Imaging at Yale School of Medicine. He serves as Associate Director of Biomedical Imaging Data Sciences at Yale Biomedical Imaging Institute and directs the Medical Software and Medical Artificial Intelligence Certificate Program. PhD in Electrical and Information Sciences from Yale University (2000) BA from Cambridge University (1994) Postdoctoral Fellowship at Yale University (2002) His research focuses on medical image analysis, machine learning, and biomedical software development. He has developed tools like BioImage Suite Web and contributed to standards committees at the Association for the Advancement of Medical Instrumentation (AAMI). His work spans modalities including MRI, CT, PET, and optical imaging. Recent publications emphasize neuroimaging analysis, explainable AI in healthcare, and multimodal data integration across species. He leads NIH-funded research under the BRAIN Initiative (R24 MH114805) and has authored a textbook on Medical Software published by Cambridge University Press. IEEE Senior Member Yale Brown-Coxe Postdoctoral Fellowship Harding Bliss Prize for Excellence in Engineering He directs the BioImage Suite Project, creating web-based image analysis tools using JavaScript and WebAssembly. His teaching includes both academic courses and a Coursera program on Medical Software with over 14,000 enrollments.
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.
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.
James C. Gee is a Professor of Radiologic Science in Radiology at the University of Pennsylvania's Perelman School of Medicine. He serves as Director of the Penn Image Computing and Science Laboratory and Co-Director of the Translational Biomedical Imaging Center , with affiliations in Bioengineering and Applied Mathematics graduate groups. His research focuses on biomedical image analysis, specialization in segmentation, registration, and morphometry applied to neurodegenerative diseases and multi-organ systems. Education : B.S. in Computer Science/Electrical Engineering (University of Washington, 1987), Ph.D. in Computer and Information Science (University of Pennsylvania, 1996) Research : Quantitative medical imaging methods, brain connectomics, neurodegeneration mapping, and translational imaging technologies Publications : 15+ recent works on AI-driven image analysis for Alzheimer's disease, cardiac amyloidosis, and radiomics applications Leadership : Directs MSE-DS Online Degree Program, co-chairs Radiology DCOAP Committee, and founded RISE (Radiology Initiative to Support Inclusive Excellence) His laboratory develops advanced computational tools like ITK-SNAP for biomedical imaging, with applications in both in vivo clinical imaging and ex vivo histology . The work spans cross-disciplinary collaborations in computer science, neuroscience, and clinical medicine.
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.
Prof. Freek J. Beekman is a Full Professor and head of the Biomedical Imaging section within the Department of Radiation Science & Technology at Delft University of Technology (TU Delft), Faculty of Applied Sciences. He is a leading figure in biomedical imaging, with extensive contributions to nuclear imaging technologies, including SPECT, PET, and CT. His research spans detector development, image reconstruction algorithms, hybrid photonic imaging, and the application of artificial intelligence in medical imaging. Research Interests: His work focuses on advancing imaging modalities through innovations in hardware (e.g., multi-pinhole collimators) and software (e.g., deep learning for attenuation correction). He has pioneered ultra-high-resolution imaging systems, particularly for preclinical and clinical SPECT, and has developed integrated platforms like U-SPECT-BioFluo. His recent research explores glymphatic delivery of nanoparticles, infection imaging, and AI-driven reconstruction techniques, reflecting a strong translational focus. Publication Trends: His most recent publications (2021–2023) emphasize deep learning in SPECT, multi-isotope imaging, high-resolution ex vivo systems, and applications in neuroimaging and oncology. The articles demonstrate a consistent focus on improving image quality, resolution, and clinical utility through physics-informed and AI-enhanced methods. Scientific Awards: NWO Physics Valorization Prize Innovation of the Year Award by the World Molecular Imaging Society (2015, 2018) Edward Hoffman Memorial Award (2017) Bruce Hasegawa Memorial Award (2021) FOM Valorization Award (2013) TU Delft Entrepreneurial Award (2010) Advising and Grants: While specific student names are not listed, his leadership in large collaborative projects and supervision of numerous publications suggests active mentoring. He has secured significant funding through national and international grants, evidenced by his invention of over 20 patent families and successful technology transfer. His founding and leadership of MILabs BV (sold to Rigaku) highlights his impact on commercialization and industry-academia collaboration. Labs and Teams: He leads the Biomedical Imaging research group at TU Delft, which develops cutting-edge imaging systems such as VECTor (SPECT-PET) and EXIRAD-HE. His teams have produced technologies used globally in academic and pharmaceutical research, contributing to tracer development and therapeutic innovation.
Dale Bailey is a Professor in the Faculty of Medicine & Health at the University of Sydney, with clinical appointments at Royal North Shore Hospital's Department of Nuclear Medicine. He previously directed the Sydney Vital Northern Translational Cancer Research Centre and is a member of the Centre for Drug Discovery Innovation, the University of Sydney Nano Institute, and the Charles Perkins Centre. Nuclear Medicine Physicist Theranostics Researcher Clinical Imaging Expert His research focuses on quantitative functional imaging using SPECT and PET, hybrid imaging modalities, radiation dosimetry for personalized cancer therapy, and the biological effects of radiation. He pioneered combined structure/function imaging and advanced radionuclide therapy applications. Recent publications emphasize PET/CT with novel radionuclides, metabolic tumor volume as a prognostic biomarker, and dosimetry validation methods . Key themes include cancer, nuclear medicine, and translational research. PhD in Physics Fellow of the Institute of Physics and Engineering in Medicine (FIPEM) Chartered Scientist (CSci) in the UK International Consensus Guidelines Contributor He supervises research students in radiotheranostics and collaborates on clinical trials for neuroendocrine tumors and glioblastoma. Grants and projects involve hybrid imaging optimization, radiation safety, and personalized treatment planning.
Professor Mark Lythgoe is a distinguished academic at University College London (UCL), where he serves as Professor of Biomedical Imaging in the Department of Imaging within the Faculty of Medical Sciences. He is the Founder and Director of the Centre for Advanced Biomedical Imaging (CABI) at UCL, a multidisciplinary research center hosting 12 state-of-the-art imaging modalities and 50 researchers. Additionally, he is Co-Director of the UCL Department of Imaging and Director of Biomedical Imaging Research at the Francis Crick Institute. Mark Lythgoe earned his Doctor of Philosophy from University College London in 1999 and his Master of Science from the University of Surrey in 1993. Professor Lythgoe has a long-standing track record in the development and application of biomedical imaging techniques, with research spanning from fundamental imaging science to clinical applications. His work focuses on advancing imaging technologies for biomedical research and clinical practice, with particular emphasis on neuroimaging, molecular imaging, and the application of imaging to understand neurological disorders and cancer. He has translated his research findings into clinical radiological practice and established training programs in biomedical imaging. His extensive publication record demonstrates consistent innovation across imaging modalities, with recent work increasingly focused on the glymphatic system in neurological disorders, advanced tumor imaging techniques, and the development of novel imaging technologies for both preclinical and clinical applications. His research bridges engineering, physics, biology, and clinical medicine to solve complex biomedical challenges. Professor Lythgoe's significant contributions to the field have been recognized with numerous prestigious awards: IET Achievement Medal (2023) - for major and distinguished contribution in Medical Imaging Royal Society of Medicine Ellison-Cliffe Award (2021) - for contribution of fundamental science to the advancement of medicine Davies Medal from the Royal Photographic Society (2013) - for significant contribution to imaging science Alumni Achievement Award from the University of Salford Neuroscience Prize for Public Understanding from the British Neuroscience Association Dorothy Hodgkin Award Biosciences Federation Science Communication Award Fellow of the British Science Association Professor Lythgoe has secured substantial research funding, with £45 million awarded for his collaborative imaging research program. He is deeply committed to training the next generation of imaging scientists, serving as Co-Director of the MSc in Advanced Biomedical Imaging and Co-Founder of the UCL Centre for Doctoral Training in Medical Imaging. His public engagement efforts include directing the Cheltenham Science Festival, which has become one of the largest science festivals in the world. As Director of the Centre for Advanced Biomedical Imaging, Professor Lythgoe leads a vibrant research team of 50 researchers working across 12 state-of-the-art imaging modalities. His center fosters interdisciplinary collaboration between engineers, physicists, biologists, and clinicians to develop and apply cutting-edge imaging technologies. He has published over 300 papers including publications in Nature, Nature Photonics, Nature Medicine and The Lancet, demonstrating the high impact of his research across multiple disciplines.
Daniel Appelbaum, MD, is a Professor of Radiology at the University of Chicago, specializing in nuclear medicine and molecular imaging. He serves as a radiologist and nuclear medicine physician at the University of Chicago Medicine, where he leads a section involved in multiple oncology PET/CT clinical trials. Dr. Appelbaum chairs the University of Chicago Radioactive Drug Research Advisory Committee (RADRAC) and is an active member of the national Society of Nuclear Medicine Committee on Education. Mallinckrodt Institute of Radiology at Washington University, St. Louis, MO (2000) - Fellowship in Nuclear Medicine University of Chicago, Chicago, IL (1999) - Residency in Diagnostic Radiology Mount Sinai School of Medicine, New York, NY (1995) - MD Cornell University, Ithaca, NY (1991) - BA in English Literature American Board of Radiology Certification (1999) American Board of Nuclear Medicine Certification (2000) Dr. Appelbaum's research focuses on nuclear and molecular imaging in both basic science and clinical settings. His current work includes developing a novel brain imaging agent for multiple sclerosis and a metabolic catalyst to enhance tumor detection with PET imaging. He has pioneered new strategies for computer-aided diagnosis (CAD) in nuclear medicine, for which he holds an AI patent. His research spans oncologic, neurologic, and cardiac applications of PET and PET/CT imaging, with special emphasis on metabolic tumor volume analysis, molecular targeted therapies, and quantitative imaging biomarkers. His laboratory investigates applications of artificial intelligence in radiographic image interpretation, particularly for cancer detection and monitoring treatment response. Analysis of Dr. Appelbaum's recent publications (2015-2025) reveals a strong focus on quantitative nuclear medicine imaging, particularly PET/CT applications in oncology. His work consistently explores metabolic tumor volume as a prognostic marker across various cancers including lung cancer, lymphoma, and neuroendocrine tumors. There is a clear trajectory toward integrating artificial intelligence with nuclear medicine imaging, as evidenced by his research on computer-aided diagnosis systems and quantitative image analysis. His publications span multiple disciplines including oncology, neurology, cardiology, and hematology, demonstrating the broad applicability of nuclear imaging techniques across medical specialties. America's Top Doctor (Castle Connolly, 2014-present) Chicago's Top Doctor (Chicago Magazine, 2012-present) Marc Tetalman Teacher of the Year Award (University of Chicago, 2006-2007) As an educator, Dr. Appelbaum has made significant contributions to nuclear medicine education, serving on the national Society of Nuclear Medicine Committee on Education for many years. He authored the textbook Nuclear Medicine RadCases as a comprehensive review resource for the field. He frequently accepts lecture invitations on nuclear and PET imaging at medical institutions and conferences worldwide. His research program has secured multiple clinical trials funding, particularly in oncology PET/CT applications for monitoring therapy response. Dr. Appelbaum's AI-related patent in computer-aided diagnosis represents a significant translational research achievement with potential clinical impact. Dr. Appelbaum leads a research section at the University of Chicago focused on nuclear and molecular imaging. His team collaborates extensively with oncologists, neurologists, and cardiologists on clinical trials and translational research projects. The group is particularly active in developing novel imaging biomarkers and quantitative analysis methods for cancer imaging. They maintain strong collaborations with computer science researchers for AI development in medical imaging. The section participates in multiple multi-center clinical trials evaluating PET/CT for cancer staging, treatment response assessment, and prognostication across various malignancies.
Anas Alani, MD is an Assistant Professor in both Medicine and Radiology at the School of Medicine, University of California, Los Angeles. His academic career spans cardiovascular medicine with a strong emphasis on advanced imaging techniques and coronary pathophysiology. Dr. Alani's research interests focus on: Cardiovascular imaging using computed tomography Coronary artery disease and atherosclerosis Diabetes-related cardiovascular complications Valvular heart disease and interventions Cardiovascular risk assessment in special populations Extra-coronary calcification patterns His scholarly work demonstrates expertise in applying advanced imaging modalities to understand cardiovascular pathophysiology and improve diagnostic accuracy. Dr. Alani has contributed significantly to the field of coronary plaque characterization, positive remodeling in smokers, and the application of CT angiography in various clinical scenarios including diabetes, HIV, and metabolic syndrome. Dr. Alani's publication record shows consistent productivity since 2013, with 24 publications through 2024. His work frequently appears in high-impact cardiology and radiology journals including the Journal of the American College of Cardiology, American Journal of Cardiology, and JACC: Cardiovascular Interventions. His research demonstrates methodological rigor with extensive use of CT angiography, intravascular ultrasound, and other advanced imaging techniques to quantify coronary plaque characteristics. Dr. Alani is currently serving as Principal Investigator for a clinical trial titled "Effect of Tirzepatide on progression of coronary atherosclerosis using MDCT" sponsored by Eli Lilly & Co. (2025-2026), indicating ongoing active research in diabetes and cardiovascular disease. His work frequently collaborates with Dr. Matthew Budoff and other cardiovascular imaging experts, contributing to multicenter studies including the DCCT/EDIC Research Group and Multicenter AIDS Cohort Study.
Walter Noordzij is an Assistant Professor at the University of Groningen within the Faculty of Medical Sciences , affiliated with the Basic and Translational Research and Imaging Methodology Development in Groningen (BRIDGE) group. His research focuses on nuclear medicine imaging technologies , particularly PET/CT applications in oncology, cardiology, and healthcare economics. He has contributed to studies on cardiac sympathetic innervation in amyloidosis, financial literacy of medical residents regarding imaging costs, and prostate cancer management using PSMA PET/CT. Recent work also addresses physician workload trends in Dutch nuclear medicine and innovative parametric imaging techniques. Research interests include positron emission tomography-computed tomography (PET/CT) , fluorodeoxyglucose (FDG) imaging , and multidisciplinary approaches to prostate cancer diagnosis . He actively collaborates internationally, evidenced by his participation in the 19th International Conference on Radiation Therapy and the EANM Focus Meeting on Prostate Cancer . No awards are explicitly listed, but his involvement in high-impact studies suggests active recognition in the field. He has supervised 3 students but their names are not disclosed. His work spans clinical imaging methodology, translational research, and healthcare system analysis, reflecting a commitment to bridging basic science and clinical practice.
Jovan Brankov is a Professor of Electrical and Computer Engineering and Biomedical Engineering at the Illinois Institute of Technology (IIT), within the Armour College of Engineering. He joined IIT in 2002 as a researcher, became Research Assistant Professor in 2004, and was promoted to Assistant Professor in 2008. His research focuses on medical imaging, image sequence processing, pattern recognition, and data mining, with emphasis on biomedical applications such as tumor detection, motion tracking, and tomographic reconstruction methods. Education: Ph.D. in Electrical Engineering from IIT (2002), M.Sc. from IIT (1999), and Dipl. Ing. from the University of Belgrade (1996). Research interests include medical image quality assessment, multiple-image radiography, 4D/5D tomographic reconstruction, and the ADEPT cancer imager. He leads the Advanced X-ray Imaging Laboratory (AXIL) at IIT, which develops phase-sensitive x-ray imaging technologies. Key awards include the IOP Select Award (2006), AIP Editor's Pick (2015), and Nayar Prize phases I (2015) and II (2016). He serves as an associate editor for IEEE Transactions on Medical Imaging, Medical Physics, and SPIE's Journal of Medical Imaging. His work spans over 150 publications, including peer-reviewed articles and a book chapter. Ongoing projects involve deep learning applications in SPECT imaging, fluorescence-guided surgery, and paired-agent imaging for cancer detection.
Weihua Zhou is a Tenured Associate Professor at Michigan Technological University in the College of Computing, with affiliations in Applied Computing, Biomedical Engineering, Computer Science, Electrical and Computer Engineering, and Mathematical Sciences. He holds a PhD from Southern Illinois University Carbondale, an MS and B.Eng. from Wuhan University, and completed postdoctoral research at Emory University. Academic Positions : Tenured Associate Professor (2025–present), Tenure-Track Assistant Professor (2019–2025) at Michigan Tech; Nina Bell Suggs Endowed Professorship (2015–2019) at the University of Southern Mississippi. Research Interests : Focuses on medical imaging and health informatics , particularly machine learning applications in cardiovascular diagnosis , osteoporosis risk stratification , and senile dementia early detection . Additional work includes radiomics for COVID-19 severity assessment , federated learning in medical segmentation , and deep learning for proximal femur strength prediction . Scientific Awards : USM Nina Bell Suggs Endowed Professorship, USM College of Arts and Sciences Scholarly Research Award, American Heart Association Research Leaders Academy (2017, 2018), USM Butch Oustalet Distinguished Professorship Research Award. Lab & Tools : Leads the Medical Imaging & Informatics Lab (MIILab-MTU) and contributes to the NSF/MRI GPU Cluster. Open-sourced tools include KD4COVID19 for radiomics analysis and ECGTools for ECG classification.
