EMATA: a toolbox for the automatic extraction and modeling of arterial inputs for tracer kinetic analysis in [ 18 F]FDG brain studies.

Autor: De Francisci M; Department of Information Engineering, University of Padova, Padova, Italy., Silvestri E; Department of Information Engineering, University of Padova, Padova, Italy., Bettinelli A; Department of Information Engineering, University of Padova, Padova, Italy.; Medical Physics Department, Veneto Institute of Oncology - IOV IRCSS, Padova, Italy., Volpi T; Padova Neuroscience Center, University of Padova, Padova, Italy.; Department of Radiology and Biomedical Imaging, Yale University, New Haven, CT, USA., Goyal MS; Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA., Vlassenko AG; Neuroimaging Laboratories at the Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, USA., Cecchin D; Padova Neuroscience Center, University of Padova, Padova, Italy.; Department of Medicine, Unit of Nuclear Medicine, University of Padova, Padova, Italy., Bertoldo A; Department of Information Engineering, University of Padova, Padova, Italy. alessandra.bertoldo@unipd.it.; Padova Neuroscience Center, University of Padova, Padova, Italy. alessandra.bertoldo@unipd.it.
Jazyk: angličtina
Zdroj: EJNMMI physics [EJNMMI Phys] 2024 Dec 24; Vol. 11 (1), pp. 105. Date of Electronic Publication: 2024 Dec 24.
DOI: 10.1186/s40658-024-00707-2
Abstrakt: Purpose: PET imaging is a pivotal tool for biomarker research aimed at personalized medicine. Leveraging the quantitative nature of PET requires knowledge of plasma radiotracer concentration. Typically, the arterial input function (AIF) is obtained through arterial cannulation, an invasive and technically demanding procedure. A less invasive alternative, especially for [ 18 F]FDG, is the image-derived input function (IDIF), which, however, often requires correction for partial volume effect (PVE), usually performed via venous blood samples. The aim of this paper is to present EMATA: Extraction and Modeling of Arterial inputs for Tracer kinetic Analysis, an open-source MATLAB toolbox. EMATA automates IDIF extraction from [ 18 F]FDG brain PET images and additionally includes a PVE correction procedure that does not require any blood sampling.
Methods: To assess the toolbox generalizability and present example outputs, EMATA was applied to brain [ 18 F]FDG dynamic data of 80 subjects, extracted from two distinct datasets (40 healthy controls, 40 glioma patients). Additionally, to compare with the reference standard, quantification using both IDIF and AIF was carried out on a third open-access dataset of 18 healthy individuals.
Results: EMATA consistently performs IDIF extraction across all datasets, despite differences in scanners and acquisition protocols. Remarkably high agreement is observed when comparing Patlak's K i between IDIF and AIF (R 2 : 0.98 ± 0.02).
Conclusion: EMATA proved adaptability to different datasets characteristics and the ability to provide arterial input functions that can be used for reliable PET quantitative analysis.
Competing Interests: Declarations. Ethics approval: All assessments and imaging procedures were approved by Human Research Protection Office and Radioactive Drug Research Committee at Washington University in St. Louis (healthy controls). Concerning glioma patients, the protocol has been approved by the local Ethics Committee of the University Hospital of Padova. All procedures performed in studies were conducted in accordance with the 1964 Declaration of Helsinki and its subsequent amendments. Consent to participate: Informed written consent was obtained from all individual participants included in the study. Consent to publish: Participants signed informed consent regarding publishing their data. Competing interests: The authors declare no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.
(© 2024. The Author(s).)
Databáze: MEDLINE
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