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A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies

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A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies. / Tonietto, Matteo; Rizzo, Gaia; Veronese, Mattia; Borgan, Faith; Bloomfield, Peter; Howes, Oliver; Bertoldo, Alessandra.

In: IEEE Transactions on Biomedical Engineering, 01.01.2018.

Research output: Contribution to journalArticle

Harvard

Tonietto, M, Rizzo, G, Veronese, M, Borgan, F, Bloomfield, P, Howes, O & Bertoldo, A 2018, 'A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies', IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2018.2874308

APA

Tonietto, M., Rizzo, G., Veronese, M., Borgan, F., Bloomfield, P., Howes, O., & Bertoldo, A. (Accepted/In press). A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/TBME.2018.2874308

Vancouver

Tonietto M, Rizzo G, Veronese M, Borgan F, Bloomfield P, Howes O et al. A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies. IEEE Transactions on Biomedical Engineering. 2018 Jan 1. https://doi.org/10.1109/TBME.2018.2874308

Author

Tonietto, Matteo ; Rizzo, Gaia ; Veronese, Mattia ; Borgan, Faith ; Bloomfield, Peter ; Howes, Oliver ; Bertoldo, Alessandra. / A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies. In: IEEE Transactions on Biomedical Engineering. 2018.

Bibtex Download

@article{182e2c315d6c43d299a6a1fa1cf4b768,
title = "A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies",
abstract = "Objective: Full quantification of dynamic PET data requires the knowledge of tracer concentration in the arterial plasma. However, its accurate measurement is challenging due to the presence of radiolabeled metabolites and measurement noise. Mathematical models are fitted to the plasma data for both radiometabolite correction and data denoising. However, the models used are generally not physiologically informed and not consistently applied across studies even when quantifying the kinetics of the same radiotracer, introducing methodological variability affecting the results interpretation. The aim of this study was to develop and validate a unified framework for the arterial data modelling to achieve an accurate and fully-automated description of the plasma tracer kinetics. Methods: The proposed pipeline employs basis pursuit techniques for estimating both radiometabolites and parent concentration models from the raw plasma measurements, allowing the resulting algorithm to be both robust and flexible to the different quality of data available. The pipeline was tested on four PET tracers ([11C]PBR28, [11C]MePPEP, [11C]WAY-100635 and [11C]PIB) with continuous and discrete blood sampling. Results: Compared to the standard procedure, the pipeline provided similar fit of the parent fraction but yielded a better description of the total plasma radioactivity, which in turn allowed a more accurate fit of the tissue PET data. Conclusion: The new method showed superior fits compared to the standard pipeline, for both continuous and discrete arterial sampling protocol, yielding to better description of PET data. Significance: The proposed pipeline has the potential to standardize the blood data modeling in dynamic PET studies given its robustness, flexibility and easiness of use.",
keywords = "Biological system modeling, Data models, Input function, Kinetic modelling, Mathematical model, Plasmas, Positron emission tomography, Positron Emission Tomography, Radiometry, Receptor imaging, Standards",
author = "Matteo Tonietto and Gaia Rizzo and Mattia Veronese and Faith Borgan and Peter Bloomfield and Oliver Howes and Alessandra Bertoldo",
year = "2018",
month = "1",
day = "1",
doi = "10.1109/TBME.2018.2874308",
language = "English",
journal = "IEEE Transactions on Biomedical Engineering",
issn = "0018-9294",
publisher = "IEEE Computer Society",

}

RIS (suitable for import to EndNote) Download

TY - JOUR

T1 - A unified framework for plasma data modelling in dynamic Positron Emission Tomography studies

AU - Tonietto, Matteo

AU - Rizzo, Gaia

AU - Veronese, Mattia

AU - Borgan, Faith

AU - Bloomfield, Peter

AU - Howes, Oliver

AU - Bertoldo, Alessandra

PY - 2018/1/1

Y1 - 2018/1/1

N2 - Objective: Full quantification of dynamic PET data requires the knowledge of tracer concentration in the arterial plasma. However, its accurate measurement is challenging due to the presence of radiolabeled metabolites and measurement noise. Mathematical models are fitted to the plasma data for both radiometabolite correction and data denoising. However, the models used are generally not physiologically informed and not consistently applied across studies even when quantifying the kinetics of the same radiotracer, introducing methodological variability affecting the results interpretation. The aim of this study was to develop and validate a unified framework for the arterial data modelling to achieve an accurate and fully-automated description of the plasma tracer kinetics. Methods: The proposed pipeline employs basis pursuit techniques for estimating both radiometabolites and parent concentration models from the raw plasma measurements, allowing the resulting algorithm to be both robust and flexible to the different quality of data available. The pipeline was tested on four PET tracers ([11C]PBR28, [11C]MePPEP, [11C]WAY-100635 and [11C]PIB) with continuous and discrete blood sampling. Results: Compared to the standard procedure, the pipeline provided similar fit of the parent fraction but yielded a better description of the total plasma radioactivity, which in turn allowed a more accurate fit of the tissue PET data. Conclusion: The new method showed superior fits compared to the standard pipeline, for both continuous and discrete arterial sampling protocol, yielding to better description of PET data. Significance: The proposed pipeline has the potential to standardize the blood data modeling in dynamic PET studies given its robustness, flexibility and easiness of use.

AB - Objective: Full quantification of dynamic PET data requires the knowledge of tracer concentration in the arterial plasma. However, its accurate measurement is challenging due to the presence of radiolabeled metabolites and measurement noise. Mathematical models are fitted to the plasma data for both radiometabolite correction and data denoising. However, the models used are generally not physiologically informed and not consistently applied across studies even when quantifying the kinetics of the same radiotracer, introducing methodological variability affecting the results interpretation. The aim of this study was to develop and validate a unified framework for the arterial data modelling to achieve an accurate and fully-automated description of the plasma tracer kinetics. Methods: The proposed pipeline employs basis pursuit techniques for estimating both radiometabolites and parent concentration models from the raw plasma measurements, allowing the resulting algorithm to be both robust and flexible to the different quality of data available. The pipeline was tested on four PET tracers ([11C]PBR28, [11C]MePPEP, [11C]WAY-100635 and [11C]PIB) with continuous and discrete blood sampling. Results: Compared to the standard procedure, the pipeline provided similar fit of the parent fraction but yielded a better description of the total plasma radioactivity, which in turn allowed a more accurate fit of the tissue PET data. Conclusion: The new method showed superior fits compared to the standard pipeline, for both continuous and discrete arterial sampling protocol, yielding to better description of PET data. Significance: The proposed pipeline has the potential to standardize the blood data modeling in dynamic PET studies given its robustness, flexibility and easiness of use.

KW - Biological system modeling

KW - Data models

KW - Input function

KW - Kinetic modelling

KW - Mathematical model

KW - Plasmas

KW - Positron emission tomography

KW - Positron Emission Tomography

KW - Radiometry

KW - Receptor imaging

KW - Standards

UR - http://www.scopus.com/inward/record.url?scp=85054682973&partnerID=8YFLogxK

U2 - 10.1109/TBME.2018.2874308

DO - 10.1109/TBME.2018.2874308

M3 - Article

AN - SCOPUS:85054682973

JO - IEEE Transactions on Biomedical Engineering

JF - IEEE Transactions on Biomedical Engineering

SN - 0018-9294

ER -

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