QModeling: a Multiplatform, Easy-to-Use and Open-Source Toolbox for {PET} Kinetic Analysis

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Authors

Francisco Javier López-González

José Paredes-Pacheco

Karl Thurnhofer-Hemsi

Carlos Rossi

Manuel Enciso

Daniel Toro-Flores

Belén Murcia-Casas

Antonio L. Gutiérrez-Cardo

Núria Roé-Vellvé

Published

1 January 2019

Publication details

Neuroinformatics vol. 17 (1), pages 103–114.

Links

Abstract

Citation

Please, cite this work as:

[Lóp+19] F. J. López-González, J. Paredes-Pacheco, K. Thurnhofer-Hemsi, et al. “QModeling: a Multiplatform, Easy-to-Use and Open-Source Toolbox for PET Kinetic Analysis”. In: Neuroinformatics 17.1 (2019), pp. 103-114. DOI: 10.1007/S12021-018-9384-Y. URL: https://doi.org/10.1007/s12021-018-9384-y.

BibTeX

@Article{LopezGonzalez2019,
     author = {Francisco Javier L{’o}pez-Gonz{’a}lez and Jos{’e} Paredes-Pacheco and Karl Thurnhofer-Hemsi and Carlos Rossi and Manuel Enciso and Daniel Toro-Flores and Bel{’e}n Murcia-Casas and Antonio L. Guti{’e}rrez-Cardo and N{’u}ria Ro{’e}-Vellv{’e}},
     journal = {Neuroinformatics},
     title = {QModeling: a Multiplatform, Easy-to-Use and Open-Source Toolbox for {PET} Kinetic Analysis},
     year = {2019},
     number = {1},
     pages = {103–114},
     volume = {17},
     bibsource = {dblp computer science bibliography, https://dblp.org},
     biburl = {https://dblp.org/rec/journals/ni/Lopez-GonzalezP19.bib},
     doi = {10.1007/S12021-018-9384-Y},
     timestamp = {Tue, 07 May 2024 01:00:00 +0200},
     url = {https://doi.org/10.1007/s12021-018-9384-y},
}

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Citations
PubMed - Citation Indexes: 9
Scopus - Citation Indexes: 11

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Mendeley - Readers: 28

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Papers citing this work

The following is a non-exhaustive list of papers that cite this work:

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[2] K. Chiotis, C. Johansson, E. Rodriguez-Vieitez, et al. “Tracking reactive astrogliosis in autosomal dominant and sporadic Alzheimer’s disease with multi-modal PET and plasma GFAP”. In: Molecular Neurodegeneration 18.1 (Sep. 2023). ISSN: 1750-1326. DOI: 10.1186/s13024-023-00647-y. URL: http://dx.doi.org/10.1186/s13024-023-00647-y.

[3] J. Jiao, F. Heeman, R. Dixon, et al. “NiftyPAD - Novel Python Package for Quantitative Analysis of Dynamic PET Data”. In: Neuroinformatics 21.2 (Jan. 2023), p. 457–468. ISSN: 1559-0089. DOI: 10.1007/s12021-022-09616-0. URL: http://dx.doi.org/10.1007/s12021-022-09616-0.

[4] M. Meindl, A. Zatcepin, J. Gnörich, et al. “Assessment of [18F]PI-2620 Tau-PET Quantification via Non-Invasive Automatized Image Derived Input Function”. In: European Journal of Nuclear Medicine and Molecular Imaging 51.11 (May. 2024), p. 3252–3266. ISSN: 1619-7089. DOI: 10.1007/s00259-024-06741-7. URL: http://dx.doi.org/10.1007/s00259-024-06741-7.

[5] T. Puri, M. L. Frost, A. E. B. Moore, et al. “Input function and modeling for determining bone metabolic flux using [18F] sodium fluoride PET imaging: A step‐by‐step guide”. In: Medical Physics 50.4 (Dec. 2022), p. 2071–2088. ISSN: 2473-4209. DOI: 10.1002/mp.16125. URL: http://dx.doi.org/10.1002/mp.16125.

[6] E. Rodriguez-Vieitez, A. Kumar, M. Malarte, et al. “Imaging Neuroinflammation: Quantification of Astrocytosis in a Multitracer PET Approach”. In: Biomarkers for Alzheimer’s Disease Drug Development. Springer US, 2024, p. 195–218. ISBN: 9781071637746. DOI: 10.1007/978-1-0716-3774-6_13. URL: http://dx.doi.org/10.1007/978-1-0716-3774-6_13.

[7] S. N. Roemer, M. Brendel, J. Gnörich, et al. “Subcortical tau is linked to hypoperfusion in connected cortical regions in 4-repeat tauopathies”. In: Brain 147.7 (Jun. 2024), p. 2428–2439. ISSN: 1460-2156. DOI: 10.1093/brain/awae174. URL: http://dx.doi.org/10.1093/brain/awae174.

[8] K. Sabaroedin, A. Razi, S. Chopra, et al. “Frontostriatothalamic effective connectivity and dopaminergic function in the psychosis continuum”. In: Brain 146.1 (Jan. 2022), p. 372–386. ISSN: 1460-2156. DOI: 10.1093/brain/awac018. URL: http://dx.doi.org/10.1093/brain/awac018.

[9] H. Theis, M. T. Barbe, A. Drzezga, et al. “Progressive Supranuclear Palsy: Subcortical Tau Depositions Are Associated with Cortical Perfusion in Frontal and Limbic Regions”. In: Journal of Parkinson’s Disease 14.6 (Jul. 2024), p. 1271–1276. ISSN: 1877-718X. DOI: 10.3233/jpd-240210. URL: http://dx.doi.org/10.3233/jpd-240210.

[10] H. Theis, G. N. Bischof, N. Brüggemann, et al. “In Vivo Measurement of Tau Depositions in Anti-IgLON5 Disease Using [18F]PI-2620 PET”. In: Neurology 101.22 (Nov. 2023). ISSN: 1526-632X. DOI: 10.1212/wnl.0000000000207870. URL: http://dx.doi.org/10.1212/wnl.0000000000207870.

[11] P. Vizza, E. Succurro, G. Pozzi, et al. “A Methodology to Measure Glucose Metabolism by Quantitative Analysis of PET Images”. In: Journal of Healthcare Informatics Research 8.4 (Sep. 2024), p. 640–657. ISSN: 2509-498X. DOI: 10.1007/s41666-024-00172-7. URL: http://dx.doi.org/10.1007/s41666-024-00172-7.

[12] J. Yun and Y. Kim. “Deficits of Neurotransmitter Systems and Altered Brain Connectivity in Major Depression: A Translational Neuroscience Perspective”. In: Translational Research Methods for Major Depressive Disorder. Springer US, 2022, p. 311–324. ISBN: 9781071620830. DOI: 10.1007/978-1-0716-2083-0_14. URL: http://dx.doi.org/10.1007/978-1-0716-2083-0_14.