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Iranian Journal of Nuclear Medicine، جلد ۳۳، شماره ۲، صفحات ۱۰۳-۱۱۵
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Optimization of image reconstruction protocol in neurological [18F]FDG brain PET imaging using BGO-based Discovery IQ Scanner |
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| چکیده انگلیسی مقاله |
Introduction: Since the Ordered Subset Expectation Maximization (OSEM) and Q.Clear algorithm each have advantages and disadvantages, we aimed to determine the optimal values of reconstruction protocols to achieve the best diagnostic parameters for the neurological PET brain images of BGO-based PET/CT scanners.
Methods: Images of point sources, as well as Hoffman and Carlson phantoms filled with [18F]FDG radiopharmaceutical, were acquired using a PET/CT scanner. In OSEM, images were reconstructed with multiple iterations and subsets, applying 3.2 mm or 6.4 mm Gaussian filters, with PSF recovery enabled. For comparison, one reconstruction was done without PSF recovery using Iteration-Subset=12–12. In Q.Clear, β values from 50 to 500 in 50-step increments were used for reconstruction. Parameters such as FWHM, COV and modified RC were evaluated. A cost function identified the best results, which were blindly assessed by two nuclear medicine experts for noise, contrast, and overall image quality.
Results: Quantitatively, β=50-200 and Iteration-Subset=20-12 were the parameters whose Cost Function values were higher than Iteration-Subset =12-12, which was routinely used to reconstruct brain images in our center. Visual evaluations show that β=200 has the lowest noise and the lowest contrast and evaluators gave the highest score for overall image quality to β=200 and β=150. This study has evaluated β=200 and β=150 as optimal for reconstructing brain images.
Conclusions: This study investigated the different reconstruction algorithms to obtain the optimal parameters. The Q.clear algorithm with penalty function of β=200 and β=150 is recommended for brain neurological images of GE Healthcare PET/CT scanner. |
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| کلیدواژه‌های انگلیسی مقاله |
PET/CT,Q.Clear,Bayesian penalized likelihood,Image quality,Optimization |
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| نویسندگان مقاله |
Mahsa Asami |
Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Farahnaz Aghahosseini |
Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
Saeed Farzanehfar |
Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
Pardis Ghafarian |
Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran
Yalda Salehi |
Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran
Mohammadreza Ay |
Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
Nima Kasraie |
Department of Radiology, UT Southwestern Medical Center, Dallas, TX 75390-9071, USA
Peyman Sheikhzadeh |
Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran
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| نشانی اینترنتی |
http://irjnm.tums.ac.ir/article_40439_687a21a76f13e7d6fe938682dcf3aaf6.pdf |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
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