Editorial Board
- Editor-in-Chief
- Interim Managing Editors
- Interim Senior Media Editor
- Interim Senior Area Editors
- Newly Inducted Associate Editors
- Associate Editors
- Guest Associate Editors
- Interim Scientific Advisory Committee
Editor-in-Chief

Wang, Ge
Rensselaer Polytechnic Institute, USA
Ge Wang (Fellow, IEEE, SPIE, AAPM, OSA, AIMBE, AAAS, and NAI) is the Clark & Crossan Endowed Chair Professor and Director of the Biomedical Imaging Center at Rensselaer Polytechnic Institute (Troy, New York, USA). He pioneered the cone-beam spiral CT method in 1991 and published the first perspective on AI-based tomographic imaging in 2016. His other notable contributions, in collaboration with his peers, include interior tomography, bioluminescence tomography, and innovative photon-counting CT algorithms. Dr. Wang's interests encompass AI-based imaging, teaching, and publishing. His recent honors include the 2021 IEEE EMBS Career Achievement Award, 2022 SPIE Meinel Technology Award, 2022 Sigma Xi Chubb Award for Innovation, 2023 RPI Wiley Distinguished Faculty Award, 2023 IEEE R1 Outstanding Teaching Award, 2023 IEEE NPSS/NMISC Hoffman Medical Imaging Scientist Award, and 2024 IEEE TRPMS Best Paper Award. A dedicated supporter of IEEE TMI throughout his career, Dr. Wang, as the Editor-in-Chief, is committed to upholding TMI's tradition of excellence and advancing the AI4TMI initiative in collaboration with the global medical imaging community.Interim Managing Editors

Kruger, Uwe
Rensselaer Polytechnic Institute, USA
Uwe Kruger (Senior Member, IEEE) is a Professor of Practice in Biomedical Engineering at Rensselaer Polytechnic Institute (Troy, New York, USA). From the beginning his research career in 1996, he has been developing innovative methods related to multivariate statistics and machine learning for data mining of operational data from complex processes. His most notable contributions include the development of monitoring statistics for serially correlated data and creating new univariate statistics to enhance sensitivity to anomalous signal contributions for detection and diagnosis. Currently, he coordinates the Biomedical Data Science M.Eng. program and is a passionate educator in probability and statistics, data science, machine learning, and artificial intelligence. Based on his research and teaching experience, he is the lead author of the monograph “Statistical Monitoring of Complex Multivariate Processes”, published in 2012, the lead author of the textbook “Modeling and Analysis of Uncertainty”, 2nd edition, published in 2023, and a co-author of the monograph “Foundations of Modern Artificial Intelligence - from Regression and Classification to Deep Learning and Beyond”, expected to be published in 2025. Since 2014, he has served as an associate editor for several major specialty journals. He is enthusiastic about leveraging his rich experience to support and advance TMI’s excellence including the AI initiative, AI4TMI.
Shan, Hongming
Fudan University, China
Hongming Shan (Senior Member, IEEE) is a Professor at the Institute of Science and Technology for Brain-inspired Intelligence, Fudan University (Shanghai, China). Before joining Fudan University, he served as a Research Scientist under the mentorship of Prof. Ge Wang at the Biomedical Imaging Center, Rensselaer Polytechnic Institute (Troy, New York, USA). His research focuses on developing artificial intelligence techniques for medical image reconstruction and analysis, with a recent emphasis on multi-modal foundation models for medical imaging. He was honored with the Youth Outstanding Paper Award at the World Artificial Intelligence Conference in 2021. He is dedicated to advancing TMI’s management and impact through the AI4TMI initiative.Interim Senior Media Editor

Petitjean, Caroline
Université de Rouen Normandie, France
Caroline Petitjean is a Professor at Université de Rouen Normandie, France. Her research focuses on deep learning models for medical image analysis and segmentation, with an emphasis on AI explainability and hybridizing deep models and variational models. She also has an interest in medical image datasets and has organized several international challenges. Notably, she served as the Challenge Chair for the IEEE ISBI 2021 and as Chair of the Medical Imaging with Deep Learning (MIDL) Conference in 2024. Since 2020, she has been actively contributing as an Associate Editor and Guest Editor for several leading journals. She has over 15 years of experience leveraging social media platforms such as X (Twitter), LinkedIn, and Instagram for staying updated on research and engaging with the community. She has successfully managed social media accounts for her lab and a master’s program. She is committed to enhancing the journal’s online presence through established channels and innovative strategies.Interim Senior Area Editors

Colliot, Olivier
French National Center for Scientific Research (CNRS), France
Bio: Olivier Colliot (Senior Member IEEE, Member SPIE, Member MICCAI, Member OHBM) is Research Director (equivalent to Full Professor) at CNRS (French National Center for Scientific Research) and the co-head of the ARAMIS Lab affiliated with the Paris Brain Institute, CNRS, Sorbonne Université, Inria (French National Institute for Computer Science) and Inserm (French National Institute of Health). His research focuses on machine learning for medical imaging with main application in neurological disorders. He is an Associate Editor of IEEE Transactions on Medical Imaging, an Associate Editor of Medical Image Analysis and an Associate Editor of the Journal of Medical Imaging. He has acted as Conference Co-Chair at SPIE Medical Imaging, as Area Chair at the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) and at MICCAI (Medical Image Computing and Computer Assisted Intervention) where he received the Outstanding Area Chair Award. He teaches machine learning and medical imaging at the graduate level at University of Paris-Saclay, CentraleSupelec, University Paris-Cité and Mines ParisTech.
Expertise: Olivier Colliot has over 20 years of experience in medical image processing, in particular using various types of machine learning techniques (from statistical learning to deep learning). He has expertise on a wide range of topics including classification, segmentation, generative modeling, image synthesis, time-to-event/survival analysis, disease progression modeling, shape analysis, explainability, integration of multimodal data such as imaging and non-imaging data (e.g. genetic, omics, text, clinical data…), longitudinal data, quality control, validation and benchmarking. In his current work, he is particularly interested in tackling these topics from the perspective of trustworthy AI, dealing in particular with issues such as validation methodologies, benchmarking, uncertainty, quality control, transparency and reproducibility, with a strong focus on statistical methods underlying these topics. He also dedicates major efforts in identifying and overcoming barriers that may prevent translation of new methodologies to clinical care. He has contributed to various multi-center clinical research studies (inclucling clinical trials for drug assessment) as well as in various studies using massive real-life clinical routine data coming from hospital data warehouses. He is also actively engaged in open science and, together with his group, has extensive experience in developing, maintaining and disseminating open source software.

Haldar, Justin
University of Southern California, United States
Bio: Justin Haldar (Senior Member, IEEE) is a Professor of Electrical and Computer Engineering and Biomedical Engineering at the University of Southern California, where he currently also serves as Director of the longstanding Signal and Image Processing Institute (https://sipi.usc.edu). His research interests include: innovative constrained image reconstruction frameworks to enable faster and better imaging; estimating subvoxel microstructure beyond the resolution limit using advanced models and high-dimensional data acquisition; parameter estimation and quantitative imaging; optimal design of imaging experiments; sampling and interpolation theory; fast algorithms and efficient computation; and performance characterization frameworks to identify trustable methods and safeguard against hallucination. While his imaging methods are often general and broadly applicable, much of his applied work focuses on magnetic resonance imaging. His work has been recognized with honors such as the NSF CAREER Award, the IEEE ISBI best paper award, and the IEEE EMBC first-place student paper award, among many others. He is the current Chair of the IEEE Signal Processing Society's Technical Committee on Computational Imaging. In addition to serving for many years on the TMI editorial board, he also serves on the editorial boards of Magnetic Resonance in Medicine (currently Deputy Editor) and the IEEE Transactions on Computational Imaging (currently Deputy Editor-in-Chief).
Expertise: Justin develops advanced image reconstruction techniques to enhance the quality/speed of MRI. He is known for contributions to sparse, low-rank, structured-low rank, and linear predictive modeling of MRI data, and for developing novel corresponding regularization methods. He also develops innovative/optimized data acquisition strategies for MRI, and often synergistically combines novel data acquisition methods with appropriate advanced reconstruction methods. This includes optimized k-space sampling and optimal non-Fourier imaging with RF excitation, optimized pulse sequence parameters to maximize sensitivity to MRI contrast parameters, and novel high-dimensional experiments to resolve microstructural compartments beyond the spatial resolution limit. He is an expert on optimization methods and has developed several fast algorithms. He is also an expert in statistical modeling of MRI data. He is well-versed in functional analysis concepts that are relevant to image reconstruction (Hilbert and Banach spaces, kernel interpolation). He also has substantial experience with AI/machine learning/data-driven image reconstruction methods for MRI, and despite their excellent apparent performance, he has identified major limitations in the way that these methods are typically evaluated (e.g., common metrics like MSE or PSNR are typically insensitive to spatial resolution, standard training approaches can be misled by noise in the reference data, etc.).

Kim, Chulhong
Pohang University of Science and Technology (POSTECH), Republic of Korea
Bio: Chulhong Kim (Fellow, IEEE, SPIE, OPTICA, AIMBE, and NAE-Korea) currently holds Namgo Chair Professorship, Young Distinguished Professorship, and Mueunjae Chair Professorship of School of Convergence Science and Technology (Head), Convergence IT Engineering (Department Chair) and Medical Science and Engineering (Program Chair) at Pohang University of Science and Technology (Pohang, Republic of Korea). He is the Director of Medical Device Innovation Center supported by Ministry of Education. He is also the Chief Executive Officer of Opticho Inc., a spinoff company to commercialize preclinical and clinical photoacoustic imaging systems. He is the recipients of the 2022 Korean Presidential Award from Ministry of SMEs and Startups, the 2020-2021 IEEE EMBS Distinguished Lecturer, the 2017 IEEE EMBS Early Career Achievement Award, etc. His group’s works have been selected for the 1st place of the USenhance and TDSC-ABUS Challenges in the 26th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI 2023), and the 2022 Photoacoustics Journal Elsevier Best Paper Award in the Photons Plus Ultrasound Conference (Photonics West, SPIE).
Expertise: His group has significantly contributed to multiparametric photoacoustic/ultrasound imaging from super-resolution microscopy to clinical imaging systems. Particularly, his team pioneered highly sensitive transparent ultrasound transducers for multimodal optical and ultrasound fusion imaging from microscopy to endoscopy and clinical imaging systems. His contributions expand to not only other optical (fluorescence imaging, optical coherence tomography, laser speckle imaging, etc.) and ultrasound (ultrafast Doppler imaging, quantitative imaging, etc.) imaging modalities but also AI-powered imaging.

Shen, Dinggang
ShanghaiTech University, China
Bio: Dinggang Shen (Fellow, IEEE, AIMBE, IAPR, MICCAI, ISMRM, and IAMBE) is a Professor and a Founding Dean with School of Biomedical Engineering, ShanghaiTech University, and also a Co-CEO of United Imaging Intelligence (UII), Shanghai, China. He was a recipient of the Distinguished Investigator Award from The Academy for Radiological & Biomedical Imaging Research, USA (2019). He was also a Jeffrey Houpt Distinguished Investigator and a Full Professor (Tenured) with University of North Carolina at Chapel Hill, USA, a Tenure-track Assistant Professor in University of Pennsylvanian, and an Instructor in Johns Hopkins University. His research interests include medical image analysis and artificial intelligence, in which he published >1,500 peer-reviewed papers, with H-index 153 and 98000+ citations. He serves as Editor-in-Chief for Frontiers in Radiology, Senior Editor for Medical Image Analysis, Senior Editor for IEEE TBME, and editorial board member for six international journals. Also, he has served in the Board of Directors, The Medical Image Computing and Computer Assisted Intervention (MICCAI) Society, in 2012-2015, and was the General Chair for MICCAI 2019.
Expertise: Dr. Dinggang Shen is a preeminent authority in medical image analysis, particularly known for developing and applying deep learning models within medical imaging. His research is crucial in advancing algorithms for medical image analysis, profoundly impacting disease detection and analysis, including significant advancements in neurological disorders such as Alzheimer’s and Parkinson’s diseases. His groundbreaking contributions in image segmentation and registration techniques are widely adopted, significantly influencing academic research and clinical practice worldwide. Dr. Shen’s innovations have achieved remarkable clinical success, evidenced by 15 FDA 510K clearances, 31 NMPA certificates, and 13 CE certificates, with his technologies deployed across over 3,000 hospitals and processing over 100,000 radiology studies daily. His scholarly achievements include authoring 1,500+ peer-reviewed papers, demonstrating significant impact with an H-index of 153 and more than 90,000 citations. His leadership is recognized through roles such as General Chair of MICCAI 2019 and fellowships in prestigious organizations including IEEE, AIMBE, IAPR, MICCAI, ISMRM, and IAMBE. Additionally, Dr. Shen has made substantial educational contributions, mentoring over 300 PhD students and postdocs, and 50+ junior faculty members. He founded the School of Biomedical Engineering at Shanghai Tech University and has held significant roles such as the former Co-Director of Faculty Development at UNC Chapel Hill's Department of Radiology, furthering the academic and professional growth of numerous scholars and practitioners in the field.

Yu, Hengyong
University of Massachusetts Lowell, United States
Bio: Hengyong Yu (Fellow, IEEE, AAPM, AIMBE, AAIA, AIIA) is a Full Professor and Director of the Imaging and Informatics Lab in the Department of Electrical and Computer Engineering at the University of Massachusetts Lowell. He received dual Bachelor’s degrees in information science & technology and computational mathematics in 1998, and a PhD degree in information & communication engineering in 2003, all from Xi’an Jiaotong University. Dr. Yu’s research interests include tomographic image reconstruction, medical image processing, and medical informatics. Dr. Yu has authored or coauthored over 350 peer-reviewed journal papers and conference proceedings, with a Google Scholar H-index of 50. Throughout his career, Dr. Yu has received numerous accolades. In 2005, he was awarded the first prize for the Best Natural Science Paper by the Association of Science & Technology of Zhejiang Province. In January 2012, he received an NSF CAREER award for development of CS-based interior tomography. More recently, in September 2022, he was honored with the IEEE Region 1 Technological Innovation Award (Academic) for “pioneering contributions and international leadership in tomographic imaging, especially interior tomography and machine learning-based tomographic imaging”.
Expertise: Dr. Yu’s research interests include tomographic image reconstruction, medical image processing and analysis, and medical informatics. In the past two decades, he has made major contributions to the sciences. (1) Theoretically Exact Cone-beam CT Reconstruction: for the first time, he worked with his collaborators generalized filtered backprojection and backprojection filtration algorithms for rather flexible cone-beam scanning trajectories under a mild condition. (2) Interior tomography: In 2009, he proved that if an object is piecewise constant, an internal ROI can be exactly and stably reconstructed in the compressive sensing framework. (3) Dictionary Learning based CT Reconstruction: He is one of the pioneers to apply the machine learning techniques to tomographic reconstruction. His vectorized and then tensorized dictionary learning based methods are the state-of-the-art sparsity-promoting techniques for low-dose CT. (4) Deep Learning based CT Reconstruction: he developed and applied the deep learning methods to the image reconstruction field, leading a new field “deep reconstruction”. As the lead contact author and corresponding author, he published two papers in Patterns (Cell Press) to stabilize deep reconstruction networks in 2022.

Zhang, Yi
Sichuan University, China
Bio: Yi Zhang (Senior Member, IEEE) received his B.S., M.S., and Ph.D. degrees in Computer Science and Technology from the College of Computer Science, Sichuan University, Chengdu, China, in 2005, 2008, and 2012, respectively. From 2014 to 2015, he served as a Post-Doctoral Researcher in the Department of Biomedical Engineering at Rensselaer Polytechnic Institute, Troy, NY, USA. He is currently a Full Professor at the School of Cyber Science and Engineering, Sichuan University, and serves as the Director of the Deep Imaging Group (DIG). Dr. Zhang's research centers on the application of artificial intelligence in medical imaging, with a particular focus on interpretability and security challenges concerning both data and models. He has authored over 100 publications in the field of image processing, many of which have appeared in leading journals and conferences such as IEEE Transactions on Medical Imaging, IEEE Transactions on Radiation and Plasma Medical Sciences, Medical Image Analysis, and MICCAI. His work has been featured by the Institute of Physics (IOP) and highlighted at the Lindau Nobel Laureate Meeting. Dr. Zhang is actively contributing to the academic community as an Associate Editor for both IEEE Transactions on Medical Imaging and IEEE Transactions on Radiation and Plasma Medical Sciences.
Expertise: Prof. Zhang specializes in artificial intelligence for medical imaging, focusing on advancing image reconstruction, segmentation, and interpretation. His research addresses challenges such as interpretability and security for AI models and datasets, with recent work exploring federated learning and efficient deep learning architectures. He has published over 100 papers in leading journals and conferences, including IEEE Transactions on Medical Imaging, Medical Image Analysis, and MICCAI. His contributions aim to bridge computational advancements with clinical needs, enabling privacy-preserving, robust, and interpretable medical image reconstruction and analysis. Additionally, he collaborates closely with clinicians to ensure my work’s translational impact, driving innovation in healthcare applications.

Zhou, Luping
University of Sydney, Australia
Bio: Luping Zhou (Senior Member, IEEE) is an Associate Professor and Director of Software Engineering at the School of Electrical and Computer Engineering, University of Sydney. She is a recipient of ARC Discovery Early Career Researcher Award (DECRA) and co-leads the Digital Health Imaging (DHI) research theme under the university’s Digital Sciences Initiatives (DSI), fostering impactful industry collaborations in AI and medical imaging. She is affiliated with the Sydney Artificial Intelligence Centre and the Brain and Mind Centre. A/Prof Zhou has served as Associate Editor for multiple leading journals in medical imaging and pattern recognition. She has intensively served the MICCAI community: Workshop Chair for MICCAI 2025, Tutorial Chair for MICCAI 2019, Area Chair for MICCAI 2020–2022 and 2024, Oral Session Chair for MICCAI 2021, and Award Selection Committee for MICCAI 2022. She has been an Area Chair for top-tier conferences such as CVPR 2025, ICLR 2025, and ECCV 2024, and has served as a Senior PC for AAAI in 2023 and 2024. Her research interests lie in developing innovative methodologies, such as visual language models, deep generative models, and advanced graphical models, to advance the state of the art in medical AI and foster applications with real-world impact.
Expertise: A/Prof Luping Zhou’s research expertise lies in artificial intelligence and its applications in healthcare. She specializes in developing deep generative models for medical image synthesis, with applications in dose-less imaging, cross-modality synthesis, super-resolution, and denoising. Her work on low-light enhancement for endoscopy image restoration received the Best Paper Honorable Mention award in MICCAI’24. A/Prof Zhou’s expertise extends to brain image and signal analysis, focusing on early diagnosis of neurological disorders such as Alzheimer’s disease and epilepsy through advanced deep learning and graphical models. Additionally, she has significant expertise in automated medical report generation, medical VQA, and medical grounding, leveraging visual language models and deep learning to bridge the gap between AI systems and clinical practice. Her work combines cutting-edge methodologies with real-world applications, driving innovation at the intersection of AI and medicine.
Newly Inducted Associate Editors
Associate Editors
Guest Associate Editors

Knopp, Tobias
Universitätsklinikum Hamburg-Eppendorf (UKE) & Technische Universität Hamburg (TUHH)
Germany
Interim Scientific Advisory Committee
The Scientific Advisory Committee (SAC) consists of internationally renowned scientists who have served on the TMI editorial board. Members of SAC collectively represent the broad spectrum of research areas encompassed by TMI. SAC offers advice and guidance on TMI’s strategic development and growth. This encompasses the evaluation of proposals for special issues as well as invited review articles.