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

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, µ), intrinsic variability for three consecutive measurements (daily variation coefficient, CoV_d) and reproducibility over time (variation coefficient across 5 months, CoV_m). For this system, averaged recovery coefficient for activity concentration was µ = 0.90 ± 0.08 (CoV_d = 0.6%, CoV_m = 9%) in volumes ranging from 7 to 42 ml. CT calibration-curve averaged over time was $\text{HU}=(951\pm 12)\times \text{density}-(944\pm 15)$ with variability of slope and y-intercept of (CoV_d = 0.4%, CoV_m = 1.2%) and (CoV_d = 0.4%, CoV_m = 1.6%), respectively. Radiomics reproducibility resulted in (CoV_d = 18%, CoV_m = 30%) for PET and (CoV_d = 15%, CoV_m = 22%) for CT. Co-registration was assessed by Dice-Similarity-Coefficient (DSC) along 37.8 cm in superior-inferior (z) direction (well registered if DSC ≥ 0.91 and Δz ≤ 2 mm), resulting in 3/7 days 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 (µ = 0.86, CoV_d = 0.3%) for V, (µ = 0.87, CoV_d = 0.8%) for S, and (µ = 1.10, CoV_d = 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 27.8 cm and along 37 cm in z, and its radiomic variability was CoV_d = 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.