Assessment of a digital and an analog PET/CT system for accurate myocardial perfusion imaging with a flow phantom.

In Myocardial Perfusion Imaging (MPI) with Positron Emission Tomography/Computed Tomography (PET/CT) systems, accurate quantification is essential. We assessed flow quantification accuracy over various injected activities using a flow phantom. METHODS: The study was performed on the digital 4-ring Discovery MI (DMI-20) and analog Discovery 690 (D690) PET/CT systems, using 325-1257 MBq of [15O]H2O. PET performance and flow quantification accuracy were assessed in terms of count-rates, dead-time factors (DTF), scatter fractions (SF), time-activity curves (TACs), areas-under-the-curves (AUCs) and flow values. RESULTS: On DMI-20, prompts of 12.8 Mcps, DTF of 2.06 and SF of 46.1% were measured with 1257 MBq of activity. On the D690, prompts of 6.85 Mcps, DTF of 1.57 and SF of 32.5% were measured with 1230 MBq of activity. AUC values were linear over all activities. Mean wash-in flow error was - 9% for both systems whereas wash-out flow error was - 5% and - 6% for DMI-20 and D690. With the highest activity, wash-out flow error was - 12% and - 7% for the DMI-20 and D690. CONCLUSION: DMI-20 and D690 preserved accurate flow quantification over all injected activities, with maximum error of - 12%. In the future, flow quantification accuracy over the activities and count-rates evaluated in this study should be assessed.

Preliminary protocol for measuring the reproducibility and accuracy of flow values on digital PET/CT systems in [15O]H2O myocardial perfusion imaging using a flow phantom.

BACKGROUND: Several factors may decrease the accuracy of quantitative PET myocardial perfusion imaging (MPI). It is therefore essential to ensure that myocardial blood flow (MBF) values are reproducible and accurate, and to design systematic protocols to achieve this. Until now, no systematic phantom protocols have been available to assess the technical factors affecting measurement accuracy and reproducibility in MPI. MATERIALS AND METHODS: We implemented a standard measurement protocol, which applies a flow phantom in order to compare image-derived flow values with respect to a ground truth flow value with [15O]H2O MPI performed on both a Discovery MI (DMI-20, GE Healthcare) and a Biograph Vision 600 (Vision-600, Siemens Healthineers) system. Both systems have automatic [15O]H2O radio water generators (Hidex Oy) individually installed, allowing us to also study the differences occurring due to two different bolus delivery systems. To investigate the technical factors contributing to the modelled flow values, we extracted the [15O]H2O bolus profiles, the flow values from the kinetic modeling (Qin and Qout), and finally calculated their differences between test-retest measurements on both systems. RESULTS: The measurements performed on the DMI-20 system produced Qin and Qout values corresponging to each other as well as to the reference flow value across all test-retest measurements. The repeatability differences on DMI-20 were 2.1% ± 2.6% and 3.3% ± 4.1% for Qin and Qout, respectively. On Vision-600 they were 10% ± 8.4% and 11% ± 10% for Qin and Qout, respectively. The measurements performed on the Vision-600 system showed more variation between Qin and Qout values across test-retest measurements and exceeded 15% difference in 7/24 of the measurements. CONCLUSIONS: A preliminary protocol for measuring the accuracy and reproducibility of flow values in [15O]H2O MPI between digital PET/CT systems was assessed. The test-retest reproducibility falls below 15% in majority of the measurements conducted between two individual injector systems and two digital PET/CT systems. This study highlights the importance of implementing a standardized bolus injection and delivery protocol and importance of assessing technical factors affecting flow value reproducibility, which should be carefully investigated in a multi-center setting.

Study into COVID-19 Crisis Using Primary Care Mental Health Consultations and Prescriptions Data.

The effect of the 2020 pandemic, and of the national measures introduced to control it, is not yet fully understood. The aim of this study was to investigate how different types of primary care data can help quantify the effect of the coronavirus disease (COVID-19) crisis on mental health. A retrospective cohort study investigated changes in weekly counts of mental health consultations and prescriptions. The data were extracted from one the UK's largest primary care databases between January 1st 2015 and October 31st 2020 (end of follow-up). The 2020 trends were compared to the 2015-19 average with 95% confidence intervals using longitudinal plots and analysis of covariance (ANCOVA). A total number of 504 practices (7,057,447 patients) contributed data. During the period of national restrictions, on average, there were 31% (3957 ± 269, p < 0.001) fewer events and 6% (4878 ± 1108, p < 0.001) more prescriptions per week as compared to the 2015-19 average. The number of events was recovering, increasing by 75 (± 29, p = 0.012) per week. Prescriptions returned to the 2015-19 levels by the end of the study (p = 0.854). The significant reduction in the number of consultations represents part of the crisis. Future service planning and quality improvements are needed to reduce the negative effect on health and healthcare.

Building confidence in digital health through metrology.

Healthcare is increasingly and routinely generating large volumes of data from different sources, which are difficult to handle and integrate. Confidence in data can be established through the knowledge that the data are validated, well-curated and with minimal bias or errors. As the National Measurement Institute of the UK, the National Physical Laboratory (NPL) is running an interdisciplinary project on digital health data curation. The project addresses one of the key challenges of the UK's Measurement Strategy, to provide confidence in the intelligent and effective use of data. A workshop was organised by NPL in which important stakeholders from NHS, industry and academia outlined the current and future challenges in healthcare data curation. This paper summarises the findings of the workshop and outlines NPL's views on how a metrological approach to the curation of healthcare data sets could help solve some of the important and emerging challenges of utilising healthcare data.

Primary Care Prostate Cancer Case Ascertainment.

Although routine healthcare data are not collected for research, they are increasingly used in epidemiology and are key real-world evidence for improving healthcare. This study presents a method to identify prostate cancer cases from a large English primary care database. 19,619 (1.3%) men had a code for prostate cancer diagnosis. Codes for medium and high Gleason grading enabled identification of additional 94 (0.5%) cases. Many studies do not report codes used to identify patients, and if published, the lists of codes differ from study to study. This can lead to poor research reproducibility and hinder validation. This work demonstrates that carefully developed comprehensive lists of clinical codes can be used to identify prostate cancer; and that approaches that do not solely rely on clinical codes such as ontologies or data linkage should also be considered.