Eero Lehtonen
D.Sc.(Tech.)
eero.lennart.lehtonen@utu.fi +358 50 577 9597 Joukahaisenkatu 1 Turku |
Computer and machine vision; sensor fusion
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.
Computer vision and sensor fusion for improving biomedical diagnostics and medical imaging.
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- 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
- Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data (2024)
- Annals of Nuclear Medicine
- Incremental prognostic value of downstream PET perfusion imaging after coronary CT angiography (2023)
- EHJ Cardiovascular Imaging / European Heart Journal - Cardiovascular Imaging
- Effect of respiratory motion correction and CT-based attenuation correction on dual-gated cardiac PET image quality and quantification (2022)
- Journal of Nuclear Cardiology
- Synthetization, Distortion, and Geometric Correction of Isoelectric Focusing Gels for Newborn Screening (2022)
- IEEE Access
- A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography (2021)
- Sensors
- Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks (2021)
- IEEE Access
- Validation of Automated PET Segmentation Methods Based on Connected Components for Myocardium (2021)
- IEEE Nuclear Science Symposium and Medical Imaging Conference record
- Estimation of optimal number of gates in dual gated F-18-FDG cardiac PET (2020)
- Scientific Reports
- A Computational Framework for Data Fusion in MEMS-Based Cardiac and Respiratory Gating (2019)
- Sensors
- Comprehensive Analysis of Cardiogenic Vibrations for Automated Detection of Atrial Fibrillation Using Smartphone Mechanocardiograms (2019)
- IEEE Sensors Journal
- Memristive stateful logic (2019) Handbook of Memristor Networks Eero Lehtonen, Jussi H. Poikonen, Mika Laiho
- Complexity Analysis of Iterative Basis Transformations Applied to Event-Based Signals (2018)
- Frontiers in Neuroscience
- A miniaturized low power biomedical sensor node for clinical research and long term monitoring of cardiovascular signals (2017) 2017 IEEE International Symposium on Circuits and Systems (ISCAS) Jarno Tuominen, Eero Lehtonen, Mojtaba Jafari Tadi, Juho Koskinen, Mikko Pänkäälä, Tero Koivisto
- A novel dual gating approach using joint inertial sensors: implications for cardiac PET imaging (2017)
- Physics in Medicine and Biology
- Associative search using pseudo-analog memristors (2017) 2017 IEEE International Symposium on Circuits and Systems (ISCAS) Laiho Mika, Grönroos Mika, Poikonen Jussi, Lehtonen Eero, Katsumura Reon, T.-Fukuchi Atsushi, Arita Masashi, Takahashi Yasuo
- Automated Detection of Atrial Fibrillation Based on Time-Frequency Analysis of Seismocardiograms (2017)
- IEEE Journal of Biomedical and Health Informatics
- Gyrocardiography: A New Non-invasive Monitoring Method for the Assessment of Cardiac Mechanics and the Estimation of Hemodynamic Variables (2017)
- Scientific Reports
- A Miniaturized MEMS Motion Processing System for Nuclear Medicine Imaging Applications (2016)
- Computing in Cardiology
- An Adaptive Approach for Heartbeat Detection Based on S-transform in Seismocardiograms (2016) 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) Mojtaba Jafari Tadi, Eero Lehtonen, Olli Lahdejoja, Mikko Pänkäälä, Tero Koivisto
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