Riku Klén

Assistant professor

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Finland

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Riku Klén is an Assistant Professor at the Turku PET Centre in Finland. His research focuses on the application of advanced imaging techniques and deep learning methodologies in oncology, particularly in the analysis of PET/MRI scans, tumor segmentation, and the evaluation of fat distribution and its effects on health outcomes. His recent articles explore innovative models for quantifying blood flow and assessing edema in patients, contributing significantly to the fields of radiology and machine learning in medical imaging.

Riku Klén

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Articles (20)

New compartment model for hepatic blood flow quantification in humans from 15O-water PET images

Pulmonary blood flow quantification in humans from 15O-water PET

New 3D segmentation algorithm for modeling of kidney in positron emission tomography images

Investigation of total-body applicable compartment models for kinetic modeling in total body $$^{15}$$O-water perfusion PET imaging

Differential associations of subcutaneous and visceral fat with bone turnover markers: A study on bariatric surgery patients with severe obesity and individuals without obesity

Deep learning‐based 3D classification of head and neck cancer PET/MRI: Radiologist comparison and Grad‐CAM interpretability

Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections

Convolutional neural networks for tumor segmentation by cancer type and imaging modality: a systematic review

Modified Dice Coefficients for Evaluation of Tumor Segmentation from PET Images: A Proof-of-Concept Study

Expanding interpretability through complexity reduction in machine learning‐based modelling of cardiovascular disease: A myocardial perfusion imaging PET/CT prognostic study

Automatic Segmentation of Head and Neck Cancer from PET-MRI Data Using Deep Learning

Development and validation of COEWS (COVID-19 Early Warning Score) for hospitalized COVID-19 with laboratory features: A multicontinental retrospective study

Classification of head and neck cancer from PET images using convolutional neural networks

Risk factors for revision due to prosthetic joint infection following total knee arthroplasty based on 62,087 knees in the Finnish Arthroplasty Register from 2014 to 2020

Carimas: An Extensive Medical Imaging Data Processing Tool for Research

Are Quantitative Errors Reduced with Time-of-Flight Reconstruction When Using Imperfect MR-Based Attenuation Maps for 18F-FDG PET/MR Neuroimaging?

A Respiratory Motion Estimation Method Based on Inertial Measurement Units for Gated Positron Emission Tomography

Learning to Denoise Gated Cardiac PET Images Using Convolutional Neural Networks

Preoperative Risk Prediction Models for Short-Term Revision and Death After Total Hip Arthroplasty

Predicting Skeletal Muscle and Whole-Body Insulin Sensitivity Using NMR-Metabolomic Profiling

Collaborators (8)

Pirjo Nuutila

Jukka Kemppainen

Anna Vasara

Jarna C. Hannukainen

Jussi Hirvonen

Mojtaba Jafari Tadi

Jukka Kemppainen

Juhani Knuuti

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