Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging

Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging Publisher

Vosoughi H^{1, 2} ; Hajizadeh M¹ ; Emami F² ; Momennezhad M³ ; Geramifar P⁴

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1. Department of Medical Physics, Mashhad University of Medical Science, Mashhad, Iran
2. Nuclear Medicine and Molecular Imaging Department, Imam Reza International University, Razavi Hospital, Mashhad, Iran
3. Nuclear Medicine Research Center, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
4. Research Center for Nuclear Medicine, Shariati Hospital, Tehran University of Medical Science, Tehran, Iran

Source: Data in Brief Published:2021

Abstract

The data presented here provide information about the role of reconstruction parameters on Positron Emission Tomography (PET) image quantification. Multiple phantom measurements in four different Spheres to Background Ratio (SBR) were performed on Biograph 6 TruePoint TrueV PET/CT scanner. PET raw data were reconstructed with/without resolution recovery algorithm using six various iteration x subsets with five different Full-Width Half-Maximum (FWHM) values of Gaussian post-smoothing filter. The Recovery Coefficient (RC) of six spheres using three common Volume of Interest (VOI) methods (max, 3D-50% Isocontour, and peak) were calculated. Moreover, SUVmax, SUVmean, and SUVpeak and volumetric indices, such as metabolic tumor volume (MTV), volume recovery coefficient (VRC), and total lesion glycolysis (TLG) were measured. RCmax, RC50%, and RCpeak as a function of sphere size were plotted in all reconstruction methods considering different SBRs. The data could be noticeable for standardization and optimization of quantitative metrics in PET imaging. © 2021

2. Impact of Time-Of-Flight and Point-Spread-Function for Respiratory Artifact Reduction in Pet/Ct Imaging: Focus on Standardized Uptake Value, Tanaffos (2017)

3. Multicenter Quantitative 18F-Fluorodeoxyglucose Positron Emission Tomography Performance Harmonization: Use of Hottest Voxels Towards More Robust Quantification, Quantitative Imaging in Medicine and Surgery (2023)

4. Reducing Scan Time Using Time-Of-Flight Protocol in Clinical [18F]Fdg-Pet/Ct Imaging: A Feasibility Study, Iranian Journal of Nuclear Medicine (2023)

5. Pet Images Enhancement Using Deep Training of Reconstructed Images With Bayesian Penalized Likelihood Algorithm, Journal of Medical and Biological Engineering (2024)

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Mohammad Reza Ay (3)

Parham Geramifar (2)

Style	Citing Format
MLA	Vosoughi H, et al.. "Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging." Data in Brief, vol. 37, no. , 2021, pp. -.
APA	Vosoughi H, Hajizadeh M, Emami F, Momennezhad M, Geramifar P (2021). Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging. Data in Brief, 37(), -.
Chicago	Vosoughi H, Hajizadeh M, Emami F, Momennezhad M, Geramifar P. "Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging." Data in Brief 37, no. (2021): -.
Harvard	Vosoughi H et al. (2021) 'Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging', Data in Brief, 37(), pp. -.
Vancouver	Vosoughi H, Hajizadeh M, Emami F, Momennezhad M, Geramifar P. Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging. Data in Brief. 2021;37():-.
BibTex	@article{ author = {Vosoughi H and Hajizadeh M and Emami F and Momennezhad M and Geramifar P}, title = {Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging}, journal = {Data in Brief}, volume = {37}, number = {}, pages = {-}, year = {2021} }
RIS	TY - JOUR AU - Vosoughi H AU - Hajizadeh M AU - Emami F AU - Momennezhad M AU - Geramifar P TI - Pet Nema Iq Phantom Dataset: Image Reconstruction Settings for Quantitative Pet Imaging JO - Data in Brief VL - 37 IS - SP - EP - PY - 2021 ER -

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