Author

Riku Klén

University of TurkuORCID 0000-0002-0982-8360Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

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Bioinformatics· 1 Neuroscience· 1

Signature Assays, Organisms & Methods

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Research Focus:benchmarking· 2 Approach:statistical model· 2 Research Focus:clustering· 1 Organism:human· 1 Assay:imaging· 1 Organism:rat· 1

Frequent Co-Authors

Aino Latva‐RaskuiD· 1 shared Anna JaloiD· 1 shared Anna K. KirjavaineniD· 1 shared Chunlei Han· 1 shared Francisco R. Lopez‐PiconiD· 1 shared Harri HarjuiD· 1 shared Henri KärpijokiiD· 1 shared Hidehiro IidaiD· 1 shared

Activity by Year

2023-2026 · peak 1/yr

Co-Author Collaboration Network

10 top collaborators · 24 mutual co-author links

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1 shared paperNeuroscience

Aino Latva‐RaskuORCID

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2 papers

Recent·Most cited

NeuroscienceEuropean Journal of Nuclear Medicine and Molecular ImagingPeer-reviewedJan 31, 2026

Automated Long Axial Field of View PET Image Processing and Kinetic Modelling with the TurBO Toolbox

Jouni Tuisku, Santeri Palonen, Henri Kärpijoki…·Lauri Nummenmaa

The paper introduces an automated pipeline for LAFOV PET data analysis, showing reliable parameter estimates and reduced manual effort.

4 citationstop 1%

statistical modelbenchmarking

𝕏 PostBluesky

BioinformaticsInternational Journal of Biomedical ImagingPeer-reviewedDec 5, 2023

SEGMENTATION OF DYNAMIC TOTAL-BODY -FDG PET IMAGES USING UNSUPERVISED CLUSTERING

Maria K. Jaakkola, Maria Rantala, Anna Jalo…·Riku Klén

This study evaluates unsupervised clustering methods for segmenting dynamic total-body PET images at the organ level, finding that k-means with PCA performs best but still has limited accuracy.

6 citations

𝕏 PostBluesky