Quantitative evaluation of TOF benefits indifferent tumor regions of Overweight patients in clinical PET/CT scanner

Document Type: Conference Proceedings

Authors

1 Department of Medical Radiation Engineering, Tehran Science and Research Branch, Islamic Azad University, Tehran, Iran. (hanieh_jozi69@yahoo.com)

2 Chronic Respiratory Diseases Research Center, National Research Institute of Tuberculosis and Lung Diseases (NRITLD), Shahid Beheshti University of Medical Sciences, Tehran, Iran. (pardis.ghafarian@sbmu.ac.ir) PET/CT and Cyclotron Center, NRITLD, Shahid Beheshti University of Medical Sciences, Tehran, Iran.

Abstract

Introduction: Using TOF information in image reconstruction reduce the noise propagation along the LOR during forward and back projection of the data, and improve the image signal-to-noise ratio (SNR) of PET scanners. This improvement is dependent on the scintillator decay time, time response of coincidence circuits and front-end electronics. The goals of this study were to evaluate the benefits of time of flight (TOF) in clinical PET/CT images of overweight patients in relation to the tumor-to-background ratios, and to determine what SNR gains in PET performance could be achieved.
Materials and Methods: 32 overweight patient studies with normal 18F-fluoro-deoxyglucose (18F-FDG) uptake were chosen. FDG-PET/CT imaging was performed on the LYSO-based PET component of Discovery RX PET/CT scanner. Various tumor sizes in different locations of the thorax and abdomen in PET images have been investigated. The PET data were reconstructed with the baseline ordered-subsets expectation maximization (OSEM) algorithm+ PSF model and OSEM + PSF + TOF model. The image quality was evaluated using AMIDE to estimate the SNR, contrast, coefficient of variation(COV), and the standardized uptake values (𝑆𝑈𝑉𝑚𝑎𝑥). The results were then subjectively analyzed as a function of patient body-mass index (25<BMI < 30 and BMI ≥ 30), and type of imaging (TOF and Non-TOF).
Results: The results demonstrated reduction in COV when utilizing TOF algorithms. The reduced COV using TOF reconstruction was 30% among patients with 25 <BMI <30. The maximum SNRgain and CNR, for a tumor with an average size of 2 cm for overweight patients (BMI ≥ 30) in the lung region were obtained 54% and 33%, respectively. Also, the maximum SUV value for larger patients is enhanced by 24% and 14% in the lung and abdomen, respectively.
Conclusion: The results confirmed advantages of TOF information in image reconstruction including better identification of image details, better contrast, and image noise reduction in terms of SNR. Thus, the clinical studies demonstrate the improved contrast of the smallest tumor for larger patients (BMI ≥ 30) with TOF. These gains are evident from visual inspection of the images as well as a quantitative evaluation of contrast recovery of the smallest tumors and noise in different background.

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