Evaluation of Multi-Center PET/CT Quality Assurance: Multi-Paradigm Software Enables Automated PET Quality Control

Weitzel, Thilo; Prenosil, George; Hentschel, Michael; Fürstner, Markus; Krause, Thomas Michael; Rominger, Axel Oliver; Klaeser, Bernd (2018). Evaluation of Multi-Center PET/CT Quality Assurance: Multi-Paradigm Software Enables Automated PET Quality Control. European journal of nuclear medicine and molecular imaging, 45(S1), S310-S311. Springer-Verlag

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Context: PET quality assurance (QA) is becoming increasingly important as well as more complex, following the technological and radiopharmaceutical progress in PET imaging. Consequently, PET quality control (QC) has to handle an increasing heterogeneity of devices and diversity of clinical applications. However, actual PET QC focuses on equipment and good practice, while QC of individual datasets stays costly in terms of time and resources, and thus often lacks adequate diligence. Aim: Our aim is to promote automated PET QC of individual datasets and demonstrate its importance and feasibility as integral
part of PET imaging. First objective was to develop a software prototype for automated PET QC on diverse data sets. Second objective was to demonstrate feasibility and benefits of such software. In particular, the automated QC should enable an in depth evaluation of data from a nationwide PET survey organized by the Swiss Society of Nuclear Medicine (SGNM-SSMN). Materials and Methods: The software implements a multi-paradigm
approach based on an in-house software development tool. Among a range of modern design patterns, the software implements an embedded rule-based system to support decisions and reconfigurations of itself at runtime. A total of 453 datasets originating from 18 different PET/CT systems at 14 Swiss sites was available for QC. According to a detailed study protocol, each site was requested to provide a range of various image reconstructions from multiple acquisitions of two differing phantoms. The software was applied in a single run to all datasets, producing an individual quality report for each dataset and a database making all results available for further evaluation. Results: The automated QC correctly classified the diverse datasets according to acquisition protocol, reconstruction protocol and type of phantom used. Corresponding protocol adherence
verification and quantitative evaluations were performed. The system rejected 7% of the data sets because of serious violations of protocol. Another 57% of the datasets were categorized as of restricted use, because of a variety of minor violations of protocol or a mismatch between quantitative specifications and actual data. Only 36% of the data sets fully passed QC. The resulting database proved to be essential for further evaluations
as published elsewhere. Conclusion: The results indisputably prove the need, the feasibility and the benefits of automated PET QC. More intelligent software is a prerequisite for rigorous PET QC of individual datasets, especially given the increasing diversity of PET imaging applications.

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