Whole-body metabolic connectivity framework with functional PET

Reed, Murray Bruce; León, Magdalena Ponce de; Vraka, Chrysoula; Rausch, Ivo; Godbersen, GM; Popper, Valentin; Geist, Barbara; Komorowski, Arkadiusz; Nics, Lukas; Schmidt, Clemens; Klug, Sebastian; Langsteger, W; Karanikas, Georgios; Traub‐Weidinger, Tatjana; Hahn, Andreas; Lanzenberger, Rupert; Hacker, Marcus

doi:10.1016/j.neuroimage.2023.120030

Cited by 11 publications

(9 citation statements)

References 40 publications

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“…Last, to fully realize the potential of TB-PET imaging, innovative and receptive cooperative efforts outside nuclear medicine are needed. The evaluation of the organ systems' hierarchical organization in response to physiological or psychological stresses is a significant prospective application [115].…”

Section: Whole-body Connectomementioning

confidence: 99%

Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis

Sun,

Chen,

Liu

et al. 2024

iRADIOLOGY

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Total‐body positron emission tomography (TB‐PET) has ultra‐high sensitivity and the unique ability to conduct dynamic imaging of the entire body. Both the hardware configuration and the data acquired from a TB‐PET scanner differ from those of the conventional short axial field‐of‐view scanners. Therefore, various aspects concerning data processing need careful consideration when implementing TB‐PET in clinical settings. Additionally, advances in data analysis are needed to fully uncover the potential of these systems. Although some progress has been achieved, further research and innovation in scan data management are necessary. In this report, we provide a comprehensive overview of the current progress, challenges, and possible future directions for TB‐PET data processing and analysis. For a review of clinical applications, please find the other review accompanying this paper.

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Section: Whole-body Connectomementioning

confidence: 99%

Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis

Sun,

Chen,

Liu

et al. 2024

iRADIOLOGY

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“…For example, total-body clustering could be experimented in finding metastatic tumours and, otherwise, just different tissues that have the same uptake of the tracer. Organ segmentation also supports diverse system-level applications of image analysis [ 46 – 49 ].…”

Section: Discussionmentioning

confidence: 99%

Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering

Jaakkola,

Rantala,

Jalo

et al. 2023

International Journal of Biomedical Imaging

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Clustering time activity curves of PET images have been used to separate clinically relevant areas of the brain or tumours. However, PET image segmentation in multiorgan 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. These 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 preprocessing approaches, 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.

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“…Existing methods to enhance fPET data predominantly involve 3D filtering techniques. The most common and widely used approach is Gaussian smoothing, employed not only in fPET 10,11 , but also in structural and functional magnetic resonance imaging (fMRI) 12, 13 . Other 3D filtering techniques exist, such as highly constrained backprojection (hypr) 14 , static non-local means 15 (sNLM) and a more recent technique incorporating anatomical knowledge to enhance the image using a Bowsher-like prior 16 .…”

Section: Introductionmentioning

confidence: 99%

Optimal filtering strategies for task-specific functional PET imaging

Reed,

León,

Klug

et al. 2024

Preprint

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Functional Positron Emission Tomography (fPET) has advanced as an effective tool for investigating dynamic processes in glucose metabolism and neurotransmitter action, offering potential insights into brain function, disease progression, and treatment development. Despite significant methodological advances, extracting stimulation-specific information presents additional challenges in optimizing signal processing across both spatial and temporal domains, which are essential for obtaining clinically relevant insights. This study aims to provide a systematic evaluation of state-of-the-art filtering techniques for fPET imaging. Forty healthy participants underwent a single [18F]FDG PET/MR scan, engaging in the cognitive task Tetris®. Twenty thereof also underwent a second PET/MR session. Eight filtering techniques, including 3D and 4D Gaussian smoothing, highly constrained backprojection (hypr), iterative hypr (Ihypr4D), two MRI-Markov Random Field (MRI-MRF) filters (L=10 and 14 mm neighborhood) as well as static and dynamic Non-Local Means (sNLM and dNLM respectively) approaches, were applied to fPET data. Test-retest reliability (intraclass correlation coefficient), the identifiability of the task signal (temporal signal-to-noise ratio (tSNR)), spatial task-based activation (group level t-values), and sample size calculations were assessed. Results indicate distinct performance between filtering techniques. Compared to standard 3D Gaussian smoothing, dNLM, sNLM, MRI-MRF L=10 and Ihypr4D filters exhibited superior tSNR, while only dNLM and hypr showed improved test-retest reliability. Spatial task-based activation was enhanced by both NLM filters and MRI-MRF approaches. The dNLM enabled a minimum reduction of 15.4% in required sample size. The study systematically evaluated filtering techniques in fPET data processing, highlighting their strengths and limitations. The dNLM filter emerges as a promising choice, with improved performance across all metrics. However, filter selection should align with specific study objectives, considering factors like processing time and resource constraints.

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Whole-body metabolic connectivity framework with functional PET

Cited by 11 publications

References 40 publications

Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis

Current progress and future perspectives in total‐body PET imaging, part I: Data processing and analysis

Segmentation of Dynamic Total-Body [18F]-FDG PET Images Using Unsupervised Clustering

Optimal filtering strategies for task-specific functional PET imaging

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