Open-source phantom with dedicated in-house software for image quality assurance in hybrid PET systems.

Autores de IIS La Fe

• Carmen Salvador Ribes

  Autor

• Carina Soler Pons

  Autor

• Maria Jesus Sanchez Garcia

  Autor

• Consuelo Olivas Arroyo

  Autor

• 

  Irene Torres Espallardo

  Autor

• José Pérez Calatayud

  Autor

• 

  Luis Martí Bonmatí

  Autor

• Montserrat Carles Fariña

  Autor

Participantes ajenos a IIS La Fe

• Fechter, Tobias
• Baltas, Dimos
• Mix, Michael

Grupos

• Investigacion Biomédica en Imagen (GIBI 2-30)

• Núcleo de investigación traslacional integrado Urológico de Valencia (NITIUV)

Abstract

BACKGROUND: Patients' diagnosis, treatment and follow-up increasingly rely on multimodality imaging. One of the main limitations for the optimal implementation of hybrid systems in clinical practice is the time and expertise required for applying standardized protocols for equipment quality assurance (QA). Experimental phantoms are commonly used for this purpose, but they are often limited to a single modality and single quality parameter, lacking automated analysis capabilities. In this study, we developed a multimodal 3D-printed phantom and software for QA in positron emission tomography (PET) hybrid systems, with computed tomography (CT) or magnetic resonance (MR), by assessing signal, spatial resolution, radiomic features, co-registration and geometric distortions. RESULTS: Phantom models and Python software for the proposed QA are available to download, and a user-friendly plugin compatible with the open-source 3D-Slicer software has been developed. The QA viability was proved by characterizing a Philips-Gemini-TF64-PET/CT in terms of signal response (mean, ), intrinsic variability for three consecutive measurements (daily variation coefficient, CoVd) and reproducibility over time (variation coefficient across 5months, CoVm). For this system, averaged recovery coefficient for activity concentration was =0.90±0.08 (CoVd=0.6%, CoVm=9%) in volumes ranging from 7 to 42ml. CT calibration-curve averaged over time was HU = ( 951 ± 12 ) * density - ( 944 ± 15 ) with variability of slope and y-intercept of (CoVd=0.4%, CoVm=1.2%) and (CoVd=0.4%, CoVm=1.6%), respectively. Radiomics reproducibility resulted in (CoVd=18%, CoVm=30%) for PET and (CoVd=15%, CoVm=22%) for CT. Co-registration was assessed by Dice-Similarity-Coefficient (DSC) along 37.8cm in superior-inferior (z) direction (well registered if DSC=0.91 and Deltaz=2mm), resulting in 3/7days well co-registered. Applicability to other scanners was additionally proved with Philips-Vereos-PET/CT (V), Siemens-Biograph-Vison-600-PET/CT (S) and GE-SIGNA-PET/MR (G). PET concentration accuracy was (=0.86, CoVd=0.3%) for V, (=0.87, CoVd=0.8%) for S, and (=1.10, CoVd=0.34%) for G. MR(T2) was well co-registered with PET in 3/4 cases, did not show significant distortion within a transaxial diameter of 27.8cm and along 37cm in z, and its radiomic variability was CoVd=13%. CONCLUSIONS: Open-source QA protocol for PET hybrid systems has been presented and its general applicability has been proved. This package facilitates simultaneously simple and semi-automated evaluation for various imaging modalities, providing a complete and efficient QA solution.

© 2025. The Author(s).

Keywords

• 3D-printing; Experimental phantoms; Medical imaging; Oncology; PET/CT; PET/MR; Quality assurance; Radiotherapy