Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PET Article Swipe
Lucas Narciso
,
Graham Deller
,
Praveen Dassanayake
,
Linshan Liu
,
Samara Pinto
,
Udunna Anazodo
,
Andrea Soddu
,
Keith St. Lawrence
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s40658-024-00614-6
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1186/s40658-024-00614-6
Background Quantification of the cerebral metabolic rate of glucose (CMRGlu) by dynamic [ 18 F]FDG PET requires invasive arterial sampling. Alternatives to using an arterial input function (AIF) include the simultaneous estimation (SIME) approach, which models the image-derived input function (IDIF) by a series of exponentials with coefficients obtained by fitting time activity curves (TACs) from multiple volumes-of-interest. A limitation of SIME is the assumption that the input function can be modelled accurately by a series of exponentials. Alternatively, we propose a SIME approach based on the two-tissue compartment model to extract a high signal-to-noise ratio (SNR) model-derived input function (MDIF) from the whole-brain TAC. The purpose of this study is to present the MDIF approach and its implementation in the analysis of animal and human data. Methods Simulations were performed to assess the accuracy of the MDIF approach. Animal experiments were conducted to compare derived MDIFs to measured AIFs ( n = 5). Using dynamic [ 18 F]FDG PET data from neurologically healthy volunteers ( n = 18), the MDIF method was compared to the original SIME-IDIF. Lastly, the feasibility of extracting parametric images was investigated by implementing a variational Bayesian parameter estimation approach. Results Simulations demonstrated that the MDIF can be accurately extracted from a whole-brain TAC. Good agreement between MDIFs and measured AIFs was found in the animal experiments. Similarly, the MDIF-to-IDIF area-under-the-curve ratio from the human data was 1.02 ± 0.08, resulting in good agreement in grey matter CMRGlu: 24.5 ± 3.6 and 23.9 ± 3.2 mL/100 g/min for MDIF and IDIF, respectively. The MDIF method proved superior in characterizing the first pass of [ 18 F]FDG. Groupwise parametric images obtained with the MDIF showed the expected spatial patterns. Conclusions A model-driven SIME method was proposed to derive high SNR input functions. Its potential was demonstrated by the good agreement between MDIFs and AIFs in animal experiments. In addition, CMRGlu estimates obtained in the human study agreed to literature values. The MDIF approach requires fewer fitting parameters than the original SIME method and has the advantage that it can model the shape of any input function. In turn, the high SNR of the MDIFs has the potential to facilitate the extraction of voxelwise parameters when combined with robust parameter estimation methods such as the variational Bayesian approach.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1186/s40658-024-00614-6
- https://ejnmmiphys.springeropen.com/counter/pdf/10.1186/s40658-024-00614-6
- OA Status
- gold
- Cited By
- 4
- References
- 63
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4391316890
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4391316890Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1186/s40658-024-00614-6Digital Object Identifier
- Title
-
Simultaneous estimation of a model-derived input function for quantifying cerebral glucose metabolism with [18F]FDG PETWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-29Full publication date if available
- Authors
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Lucas Narciso, Graham Deller, Praveen Dassanayake, Linshan Liu, Samara Pinto, Udunna Anazodo, Andrea Soddu, Keith St. LawrenceList of authors in order
- Landing page
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https://doi.org/10.1186/s40658-024-00614-6Publisher landing page
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https://ejnmmiphys.springeropen.com/counter/pdf/10.1186/s40658-024-00614-6Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ejnmmiphys.springeropen.com/counter/pdf/10.1186/s40658-024-00614-6Direct OA link when available
- Concepts
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Carbohydrate metabolism, Function (biology), Positron emission tomography, Nuclear medicine, Medicine, Brain function, Computer science, Internal medicine, Neuroscience, Biology, Evolutionary biologyTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 2, 2024: 2Per-year citation counts (last 5 years)
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63Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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