, Volume 17, Issue 1, pp 103–114 | Cite as

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

  • Francisco J. López-GonzálezEmail author
  • José Paredes-Pacheco
  • Karl Thurnhofer-Hemsi
  • Carlos Rossi
  • Manuel Enciso
  • Daniel Toro-Flores
  • Belén Murcia-Casas
  • Antonio L. Gutiérrez-Cardo
  • Núria Roé-Vellvé
Software Original Article


Kinetic modeling is at the basis of most quantification methods for dynamic PET data. Specific software is required for it, and a free and easy-to-use kinetic analysis toolbox can facilitate routine work for clinical research. The relevance of kinetic modeling for neuroimaging encourages its incorporation into image processing pipelines like those of SPM, also providing preprocessing flexibility to match the needs of users. The aim of this work was to develop such a toolbox: QModeling. It implements four widely-used reference-region models: Simplified Reference Tissue Model (SRTM), Simplified Reference Tissue Model 2 (SRTM2), Patlak Reference and Logan Reference. A preliminary validation was also performed: The obtained parameters were compared with the gold standard provided by PMOD, the most commonly-used software in this field. Execution speed was also compared, for time-activity curve (TAC) estimation, model fitting and image generation. QModeling has a simple interface, which guides the user through the analysis: Loading data, obtaining TACs, preprocessing the model for pre-evaluation, generating parametric images and visualizing them. Relative differences between QModeling and PMOD in the parameter values are almost always below 10−8. The SRTM2 algorithm yields relative differences from 10−3 to 10−5 when \( {k}_2^{\prime } \) is not fixed, since different, validated methods are used to fit this parameter. The new toolbox works efficiently, with execution times of the same order as those of PMOD. Therefore, QModeling allows applying reference-region models with reliable results in efficient computation times. It is free, flexible, multiplatform, easy-to-use and open-source, and it can be easily expanded with new models.


Kinetic analysis PET Parametric images SRTM Patlak QModeling 



Francisco J. López-González is funded by a grant (PTA2014-09677-I) from the Spanish Ministry of Economy, Industry and Competitiveness under the Technical Support Staff (PTA) program.

Karl Thurnhofer-Hemsi and José Paredes-Pacheco are funded by PhD scholarships (FPU15/06512 and FPU16/05108, respectively) from the Spanish Ministry of Education, Culture and Sport under the FPU program.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Francisco J. López-González
    • 1
    • 2
    Email author
  • José Paredes-Pacheco
    • 1
    • 2
  • Karl Thurnhofer-Hemsi
    • 1
    • 3
  • Carlos Rossi
    • 3
  • Manuel Enciso
    • 3
  • Daniel Toro-Flores
    • 1
  • Belén Murcia-Casas
    • 4
  • Antonio L. Gutiérrez-Cardo
    • 1
    • 5
  • Núria Roé-Vellvé
    • 1
  1. 1.Molecular Imaging Unit, Centro de Investigaciones Médico-Sanitarias, Fundación General de la Universidad de MálagaMálagaSpain
  2. 2.Molecular Imaging and Medical Physics Group, Department of Psychiatry, Radiology and Public HealthUniversidade de CompostelaGaliciaSpain
  3. 3.Department of Computer Languages and Computer ScienceUniversidad de MálagaMálagaSpain
  4. 4.Internal MedicineHospital Virgen de la VictoriaMálagaSpain
  5. 5.Nuclear MedicineHospital Regional UniversitarioMálagaSpain

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