Eero Lehtonen
D.Sc.(Tech.)
eero.lennart.lehtonen@utu.fi +358 50 577 9597 Joukahaisenkatu 1 Turku ORCID identifier: https://orcid.org/0000-0002-7327-7938 |
Areas of expertise
Computer and machine vision; sensor fusion
Computer and machine vision; sensor fusion
Biography
Eero Lehtonen received the M.Sc. degree in mathematics and the D.Sc. (Tech.) degree in electronics from the University of Turku, in 2006 and 2013, respectively. He is currently working as a Senior Researcher with the Digital Health Technology Group, Department of Computing, University of Turku, Finland, where his research interests include computer vision and medical imaging. He has also worked in several companies as a machine vision specialist.
Research
Computer vision and sensor fusion for improving biomedical diagnostics and medical imaging.
Publications 
1 of 5
- Expanding interpretability through complexity reduction in machine learning‐based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study (2025)
- European Journal of Clinical Investigation
(A1 Refereed original research article in a scientific journal) - Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data (2024)
- Annals of Nuclear Medicine
(A1 Refereed original research article in a scientific journal) - Incremental prognostic value of downstream PET perfusion imaging after coronary CT angiography (2023)
- EHJ Cardiovascular Imaging / European Heart Journal - Cardiovascular Imaging
(A1 Refereed original research article in a scientific journal) - Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification (2022)
- Journal of Nuclear Cardiology
(A1 Refereed original research article in a scientific journal) - Synthetization, Distortion, and Geometric Correction of Isoelectric Focusing Gels for Newborn Screening (2022)
- IEEE Access
(A1 Refereed original research article in a scientific journal) - A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography (2021)
- Sensors
(A1 Refereed original research article in a scientific journal) - Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks (2021)
- IEEE Access
(A1 Refereed original research article in a scientific journal) - Validation of Automated PET Segmentation Methods Based on Connected Components for Myocardium (2021)
- IEEE Nuclear Science Symposium and Medical Imaging Conference record
(A4 Refereed article in a conference publication ) - Estimation of optimal number of gates in dual gated F-18-FDG cardiac PET (2020)
- Scientific Reports
(A1 Refereed original research article in a scientific journal) - A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating (2019)
- Sensors
(A1 Refereed original research article in a scientific journal)
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