Optimization of image reconstruction protocol in neurological [18F]FDG brain PET imaging using BGO-based Discovery IQ Scanner

Document Type : Original Article

Authors

1 Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran

2 Department of Nuclear Medicine, Imam Khomeini Hospital Complex, Tehran University of Medical Sciences, Tehran, Iran

3 Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases, Shahid Beheshti University of Medical Sciences, Tehran, Iran

4 PET/CT and Cyclotron Center, Masih Daneshvari Hospital, Shahid Beheshti University of Medical Sciences, Tehran, Iran

5 Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran

6 Department of Radiology, UT Southwestern Medical Center, Dallas, TX 75390-9071, USA

7 Research Center for Nuclear Medicine, Tehran University of Medical Sciences, Tehran, Iran

Abstract

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.

Keywords

Main Subjects

References

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