Dr. Mojtaba Lashgari is an accomplished researcher in biomedical engineering and computer science, currently serving as a postdoctoral researcher at the University of Oxford. With a PhD from the University of Leeds and a Marie Curie Early-Stage Research Scholarship, his work bridges artificial intelligence, medical image analysis, and computational modeling. Dr. Lashgari has published extensively in top-tier journals such as Medical Image Analysis and IEEE Transactions on Image Processing, focusing on virtual imaging trials, cardiac MRI, and coronary vessel segmentation. He has presented his research at international conferences and played active roles in scientific committees and conference organization. His interdisciplinary expertise, from MRI physics to deep learning, underpins innovative solutions in healthcare technology. Through academic service, teaching, and high-impact collaborations, Dr. Lashgari exemplifies scientific excellence and leadership. His work is contributing to the future of personalized medicine and diagnostic imaging, making him a highly deserving candidate for the Best Researcher Award.
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Dr. Mojtaba Lashgari’s educational background reflects a strong interdisciplinary foundation in engineering, biomedical sciences, and computational research. He earned his PhD in Computer Science from the University of Leeds in 2023, where he specialized in advanced medical image analysis and virtual imaging trials. Prior to this, he completed a Master of Science in Biomedical Engineering from Isfahan University of Medical Sciences in 2016, graduating as the top-ranked student in his cohort. His academic journey began with a Bachelor of Science in Electrical Engineering from Malek-Ashtar University of Technology in 2013, where he developed a solid base in electronics and signal processing. This unique blend of electrical, biomedical, and computer science education has equipped Dr. Lashgari with the analytical, technical, and problem-solving skills required for high-level research in medical technology and artificial intelligence. His academic achievements have been consistently marked by excellence, innovation, and a clear focus on impactful, real-world applications.
Dr. Mojtaba Lashgari has accumulated rich and diverse professional experience across leading academic institutions and interdisciplinary research domains. Currently, he serves as a Postdoctoral Researcher in the Department of Engineering Science at the University of Oxford (2023–2026), where he is engaged in pioneering research in medical imaging and AI-driven healthcare solutions. Prior to this, he worked as a part-time Technical Engineer at the Institute of Transport Studies, University of Leeds, contributing to computational modeling projects. His research journey also includes a tenure as a Graduate Researcher at the Medical Image and Signal Processing Research Centre in Isfahan, Iran, where he deepened his expertise in biomedical image analysis. Throughout his career, Dr. Lashgari has been actively involved in teaching, academic service, and organizing scientific events. His roles have consistently demonstrated his capabilities in bridging engineering, medicine, and computational sciences, establishing him as a well-rounded researcher with both technical depth and collaborative breadth.
Dr. Mojtaba Lashgari’s research interests lie at the dynamic intersection of medical imaging, computational modeling, and artificial intelligence. He is particularly passionate about developing innovative solutions for medical image analysis, with a focus on MRI physics, diffusion-weighted imaging, and virtual imaging trials. His work encompasses both model-based and image-based methodologies, integrating techniques such as finite element analysis, inverse problems, and machine and deep learning algorithms to simulate and interpret complex biological systems. A key area of his research involves the creation of patient-specific in-silico models to improve diagnostic accuracy and procedural planning in cardiovascular medicine. Dr. Lashgari is also deeply involved in advancing domain generalization methods for coronary vessel segmentation, aiming to enhance clinical applicability and robustness across diverse imaging environments. His interdisciplinary approach strives to bridge engineering, biomedical science, and data science, contributing significantly to the future of precision medicine and computational healthcare.
Dr. Mojtaba Lashgari has been recognized with several prestigious awards and honors that underscore his academic excellence and impactful research contributions. He was awarded the esteemed Marie Sklodowska-Curie Early-Stage Research Doctoral Scholarship in 2018, a testament to his potential in cutting-edge scientific innovation at the European level. In 2024, he received a partial scholarship to attend the Virtual Imaging Trial in Medicine (VITM) Summit at Duke University, USA, further highlighting his growing international recognition. His academic brilliance was also evident early in his career when he ranked first in his M.Sc. Biomedical Engineering class at Isfahan University of Medical Sciences in 2016. These honors reflect Dr. Lashgari’s dedication to advancing medical imaging technologies and his leadership within the global research community. His active involvement in scientific committees and organization of high-impact academic events also reinforces his role as an emerging thought leader in biomedical engineering and computational imaging science.
Dr. Mojtaba Lashgari stands out as a highly accomplished and forward-thinking researcher at the intersection of biomedical engineering, computer science, and medical image analysis. With a PhD from the University of Leeds and a current postdoctoral position at the University of Oxford, his academic journey reflects a deep commitment to scientific excellence. His contributions span advanced MRI simulations, deep learning for image segmentation, and virtual imaging trials, underscoring his role in shaping the future of computational medical diagnostics. Recognized with prestigious awards such as the Marie Curie Fellowship and actively engaged in academic leadership, Dr. Lashgari has proven himself a driving force in interdisciplinary research. His impactful publications, teaching roles, and organizational contributions further highlight his versatility and dedication. With a visionary approach to science and a growing international presence, Dr. Lashgari is well-positioned to make lasting contributions to healthcare technology and deserves strong consideration for the Best Researcher Award.
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Title: A comparative study of new and current methods for dental micro-CT image denoising
Authors: M Shahmoradi, M Lashgari, H Rabbani, J Qin, M Swain
Year: 2016
Citations: 29
Source: Dentomaxillofacial Radiology, 45(3), 20150302
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Title: Missing surface estimation based on modified Tikhonov regularization: Application for destructed dental tissue
Authors: M Lashgari, M Shahmoradi, H Rabbani, M Swain
Year: 2018
Citations: 16
Source: IEEE Transactions on Image Processing, 27(5), 2433–2446
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Title: A fast and accurate dental micro-CT image denoising based on total variation modeling
Authors: M Lashgari, H Rabbani, M Shahmorad, M Swain
Year: 2015
Citations: 7
Source: 2015 IEEE Workshop on Signal Processing Systems (SiPS), pp. 1–5
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Title: Three-dimensional micro-structurally informed in silico myocardium—Towards virtual imaging trials in cardiac diffusion weighted MRI
Authors: M Lashgari, N Ravikumar, I Teh, JR Li, DL Buckley, JE Schneider, …
Year: 2022
Citations: 4
Source: Medical Image Analysis, 82, 102592
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Title: Patient-specific in silico 3D coronary model in cardiac catheterisation laboratories
Authors: M Lashgari, RP Choudhury, A Banerjee
Year: 2024
Citations: 2
Source: Frontiers in Cardiovascular Medicine, 11, 1398290
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Title: SpinDoctor-IVIM: A virtual imaging framework for intravoxel incoherent motion MRI
Authors: M Lashgari, Z Yang, MO Bernabeu, JR Li, AF Frangi
Year: 2025
Citations: 1
Source: Medical Image Analysis, 99, 103369
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Title: Reconstruction of Connected Digital Lines Based on Constrained Regularization
Authors: M Lashgari, H Rabbani, G Plonka, I Selesnick
Year: 2022
Citations: 1
Source: IEEE Transactions on Image Processing, 31, 5613–5628
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Title: Splitting and Merging for Active Contours: Plug-and-Play
Authors: M Lashgari, A Banerjee, H Rabbani
Year: 2025
Source: Mathematics, 13(6), 991
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Title: Virtual Imaging Trial can Unveil the Relationship Between Intravascular Diffusivity and Intravoxel Incoherent Motion in MRI
Authors: M Lashgari, Z Yang, MO Bernabeu, JR Li, AF Frangi
Year: 2024
Source: Proceedings Virtual Imaging Trials in Medicine 2024, 1, 146
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Title: An in silico imaging framework for microstructure-sensitive myocardial diffusion-weighted MRI
Author: M Lashgari
Year: 2023
Source: PhD Thesis, University of Leeds
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Title: Myocardium numerical phantom at micro-scale: application in MRI simulation
Authors: M Lashgari, N Ravikumar, I Teh, J Schneider, A Frangi
Year: 2021
Source: Book of Abstracts, ESMRMB 2021 Online, 38th Annual Scientific Meeting