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Background: Several software tools have been developed for gated PET imaging that use distinct algorithms to analyze tracer uptake, myocardial perfusion, and left ventricle volumes and function. Studies suggest that different software tools cannot be used interchangeably in humans. In this study, we sought to compare the left ventricular parameters in gated F-FDG PET/CT imaging in mice by three commercially available software tools: PMOD, MIM, and QGS.
Methods And Results: Healthy mice underwent ECG-gated F-FDG imaging using a small-animal nanoPET/CT (Mediso) under isoflurane narcosis. Reconstructed gates PET images were subsequently analyzed in three different software tools, and cardiac volume and function (end-diastolic (EDV), end-systolic volumes (ESV), stroke volume (SV), and ejection fraction (EF)) were evaluated. While cardiac volumes correlated well between PMOD, MIM, and QGS, the left ventricular parameters and cardiac function differed in agreement using Bland-Altman analysis. EDV in PMOD vs. QGS: r = 0.85; p < 0.001, MIM vs. QGS: r = 0.92; p < 0.001, and MIM vs. PMOD: r = 0.88; p < 0.001, showed good correlations. Correlation was also found in ESV: PMOD vs. QGS: r = 0.48; p = 0.07, MIM vs QGS: r = 0.79; p < 0.001, and MIM vs. PMOD: r = 0.69; p < 0.01. SV showed good correlations in: PMOD vs. QGS: r = 0.73; p < 0.01, MIM vs. QGS: r = 0.86; p < 0.001, and MIM vs. PMOD: r = 0.92; p < 0.001. However, EF among correlated poorly: PMOD vs. QGS: r = -0.31; p = 0.26, MIM vs. QGS: r = 0.48; p = 0.07, and MIM vs. PMOD: r = 0.23; p = 0.41. Inter-class and intra-class correlation coefficient were > 0.9 underlining repeatability in using PMOD, MIM, and QGS for cardiac volume and function assessment.
Conclusions: All three commercially available software tools are feasible in small animal cardiac volume assessment in gated F-FDG PET/CT imaging. However, due to software-related differences in agreement analysis for cardiac volumes and function, PMOD, MIM, and QGS cannot be used interchangeably in murine research.
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http://dx.doi.org/10.1186/s13550-023-01026-w | DOI Listing |
Health Inf Manag
September 2025
Health Information Technology Research Center, Isfahan University of Medical Sciences, Isfahan, Iran.
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J Healthc Sci Humanit
January 2024
Formerly Associate Professor of Epidemiology and Risk Analysis, Department of Pathobiology/Department of Graduate Public Health, College of Veterinary Medicine, Tuskegee University, Phone: (334) 524-1988, Email:
The COVID-19 pandemic is a highly infectious disease of paramount public health importance. COVID-19 is mainly transmitted via human-to-human contact. This could be through self-inoculation resulting from failure to observe proper hand hygiene and infection control practices.
View Article and Find Full Text PDFJ Biomed Opt
September 2025
Leibniz University Hannover, Hannover Centre for Optical Technologies, Hannover, Germany.
Significance: Melanoma's rising incidence demands automatable high-throughput approaches for early detection such as total body scanners, integrated with computer-aided diagnosis. High-quality input data is necessary to improve diagnostic accuracy and reliability.
Aim: This work aims to develop a high-resolution optical skin imaging module and the software for acquiring and processing raw image data into high-resolution dermoscopic images using a focus stacking approach.
Front Neurosci
August 2025
First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China.
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View Article and Find Full Text PDFFront Syst Biol
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Minutia.AI Pte. Ltd., Singapore, Singapore.
A representation of the cause-effect mechanism is needed to enable artificial intelligence to represent how the world works. Bayesian Networks (BNs) have proven to be an effective and versatile tool for this task. BNs require constructing a structure of dependencies among variables and learning the parameters that govern these relationships.
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