Biology subjects

Klen, R.

Publications and source records attributed to Klen, R..

4 recordsLinked to original sources

bioRxiv2025

TURBO: Automated Total-body PET Image Processing and Kinetic Modeling Toolbox

Long axial field of view (LAFOV) PET imaging requires a high level of automation and standardization, as the large number of target tissues increases the manual workload significantly. We introduce an automated analysis pipeline (TurBO, Turku total-BOdy) for preprocessing and kinetic modelling of LAFOV [15O]H2O and [18F]FDG PET data, enabling efficient and reproducible analysis of tissue perfusion and metabolism at regional and voxel-levels. The approach employs automated processing including co-registration, motion correction, automated CT segmentation for region of interest (ROI) delineation, image-derived input determination, and region-specific kinetic modelling of PET data. MethodsWe validated the analysis pipeline using Biograph Vision Quadra (Siemens Healthineers) LAFOV PET/CT scans from 21 subjects scanned with [15O]H2O and 16 subjects scanned with [18F]FDG using six segmented CT-based ROIs (cortical brain gray matter, left iliopsoas muscle, right kidney cortex and medulla, pancreas, spleen and liver) representing different levels of blood flow and glucose metabolism. ResultsModel fits showed good quality with consistent parameter estimates at both regional and voxel-levels (R{superscript 2} > 0.83 for [15O]H2O, R{superscript 2} > 0.99 for [18F]FDG). Estimates from manual and automated input functions were in concordance (R{superscript 2} > 0.74 for [15O]H2O, and R{superscript 2} > 0.78 for [18F]FDG) with minimal bias (<4% for [15O]H2O and <10% for [18F]FDG). Manually and automatically (CT-based) extracted ROI level data showed strong agreement (R{superscript 2} > 0.82 for [15O]H2O and R{superscript 2} > 0.83 for [18F]FDG), while motion correction had little impact on parameter estimates (R{superscript 2} > 0.71 for [15O]H2O and R{superscript 2} > 0.78 for [18F]FDG) compared with uncorrected data. ConclusionOur automated analysis pipeline provides reliable and reproducible parameter estimates across different regions, with an approximate processing time of 1-1.5 h per subject. This pipeline completely automates LAFOV PET analysis, reducing manual effort and enabling reproducible studies of inter-organ blood flow and metabolism, including brain-body interactions.

neuroscience↗

bioRxiv2023

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

AO_SCPLOWBSTRACTC_SCPLOWClustering time activity curves of PET images has been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multi-organ level is much less studied due to the available total-body data being limited to animal studies. Now the new PET scanners providing the opportunity to acquire total-body PET scans also from humans are becoming more common, which opens plenty of new clinically interesting opportunities. Therefore, organ level segmentation of PET images has important applications, yet it lacks sufficient research. In this proof of concept study, we evaluate if the previously used segmentation approaches are suitable for segmenting dynamic human total-body PET images in organ level. Our focus is on general-purpose unsupervised methods that are independent of external data and can be used for all tracers, organisms, and health conditions. Additional anatomical image modalities, such as CT or MRI, are not used, but the segmentation is done purely based on the dynamic PET images. The tested methods are commonly used building blocks of the more sophisticated methods rather than final methods as such, and our goal is to evaluate if these basic tools are suited for the arising human total-body PET image segmentation. First we excluded methods that were computationally too demanding for the large datasets from human total-body PET scanners. This criteria filtered out most of the commonly used approaches, leaving only two clustering methods, k-means and Gaussian mixture model (GMM), for further analyses. We combined k-means with two different pre-processings, namely principal component analysis (PCA) and independent component analysis (ICA). Then we selected a suitable number of clusters using 10 images. Finally, we tested how well the usable approaches segment the remaining PET images in organ level, highlight the best approaches together with their limitations, and discuss how further research could tackle the observed shortcomings. In this study, we utilised 40 total-body [18F]fluorodeoxyglucose PET images of rats to mimic the coming large human PET images and a few actual human total-body images to ensure that our conclusions from the rat data generalise to the human data. Our results show that ICA combined with k-means has weaker performance than the other two computationally usable approaches and that certain organs are easier to segment than others. While GMM performed sufficiently, it was by far the slowest one among the tested approaches, making k-means combined with PCA the most promising candidate for further development. However, even with the best methods the mean Jaccard index was slightly below 0.5 for the easiest tested organ and below 0.2 for the most challenging organ. Thus, we conclude that there is a lack of accurate and computationally light general-purpose segmentation method that can analyse dynamic total-body PET images. Key pointsO_LIMajority of the considered clustering methods were computationally too intense even for our total-body rat images. The coming total-body human images are 10-fold bigger. C_LIO_LIHeterogeneous VOIs like brain require more sophisticated segmentation method than the basic clustering tested here. C_LIO_LIPCA combined with k-means had the best balance between performance and running speed among the tested methods, but without further preprocessing, it is not accurate enough for practical applications. C_LI FundingResearch of both first authors was supported by donation funds of Faculty of Medicine at University of Turku. JCH reports funding from The Academy of Finland (decision 317332), the Finnish Cultural Foundation, the Finnish Cultural Foundation Varsinais-Suomi Regional Fund, the Diabetes Research Foundation of Finland, and State Research Funding/Hospital District of Southwest Finland. KAV report funding from The Academy of Finland (decision 343410), Sigrid Juselius Foundation and State Research Funding/Hospital District of Southwest Finland. JH reports funding from The Finnish Cultural Foundation Varsinais-Suomi Regional Fund. These funding sources do not present any conflict of interest. Data availabilityThe codes used in this study are available from Github page https://github.com/rklen/Dynamic_FDG_PET_clustering. The example data used in this study have not been published at the time of writing.

bioinformatics↗

bioRxiv2021

Natural loss of function of ephrin-B3 shapes spinal flight circuitry in birds

Flight in birds evolved through patterning of the wings from forelimbs and transition from alternating gait to synchronous flapping. In mammals, the spinal midline guidance molecule ephrin-B3 instructs the wiring that enables limb alternation, and its deletion leads to synchronous hopping gait. Here we show that the ephrin-B3 protein in birds lacks several motifs present in other vertebrates, diminishing its affinity for the EphA4 receptor. The avian ephrin-B3 gene lacks an enhancer that drives midline expression, and is missing in Galliformes. The morphology and wiring at brachial levels of the chick spinal cord resemble those of ephrin-B3 null mice. Importantly, dorsal midline decussation, evident in the mutant mouse, is apparent at the chick brachial level, and is prevented by expression of exogenous ephrin-B3 at the roof plate. Our findings support a role for loss of ephrin-B3 function in shaping the avian brachial spinal cord circuitry and facilitating synchronous wing flapping. TeaserWalking vs flying: Deciphering the organization and evolution of the neuronal network that controls wing flapping in birds.

neuroscience↗

bioRxiv2018

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

Respiratory viruses are highly infectious; however, the variation of individuals physiologic responses to viral exposure is poorly understood. Most studies examining molecular predictors of response focus on late stage predictors, typically near the time of peak symptoms. To determine whether pre- or early post-exposure factors could predict response, we conducted a community-based analysis to identify predictors of resilience or susceptibility to several respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV) using peripheral blood gene expression profiles collected from healthy subjects prior to viral exposure, as well as up to 24 hours following exposure. This analysis revealed that it is possible to construct models predictive of symptoms using profiles even prior to viral exposure. Analysis of predictive gene features revealed little overlap among models; however, in aggregate, these genes were enriched for common pathways. Heme Metabolism, the most significantly enriched pathway, was associated with higher risk of developing symptoms following viral exposure.

immunology↗