Phantom and Clinical Evaluation of Block Sequential Regularized Expectation Maximization (Bsrem) Reconstruction Algorithm in 68Ga-Psma Pet-Ct Studies Publisher Pubmed



Sadeghi F^{1, 2} ; Sheikhzadeh P^{1, 3} ; Kasraie N⁴ ; Farzanehfar S³ ; Abbasi M³ ; Salehi Y³ ; Ay M^{1, 2}
Source: Physical and Engineering Sciences in Medicine Published:2023

Abstract

In this study, we aimed to examine the effect of varying β-values in the block sequential regularized expectation maximization (BSREM) algorithm under differing lesion sizes to determine an optimal penalty factor for clinical application. The National Electrical Manufacturers Association phantom and 15 prostate cancer patients were injected with 68Ga-PSMA and scanned using a GE Discovery IQ PET/CT scanner. Images were reconstructed using ordered subset expectation maximization (OSEM) and BSREM with different β-values. Then, the background variability (BV), contrast recovery, signal-to-noise ratio, and lung residual error were measured from the phantom data, and the signal-to-background ratio (SBR) and contrast from the clinical data. The increment of BV using a β-value of 100 was 120.0%, and the decrement of BV using a β-value of 1000 was 40.5% compared to OSEM. As β decreased from 1000 to 100, the SUVmax increased by 59.0% for a sphere with a diameter of 10 mm and 26.4% for a sphere with a diameter of 37 mm. Conversely, Δ SNR100 - 1000 increased by 140.5% and 29.0% in the smallest and largest spheres, respectively. Furthermore, the Δ LEOSEM-100 and Δ LEOSEM-1000 were − 41.1% and − 36.7%, respectively. In the clinical study, OSEM exhibited the lowest SBR and contrast. When the β-value was reduced from 500 to 100, the SBR and contrast increased by 69.7% and 71.8% in small and 35.6% and 33.0%, respectively, in large lesions. Moreover, the optimal β-value decreased as lesion size decreased. In conclusion, a β-value of 400 is optimal for small lesion reconstruction, while β-values of 600 and 500 are optimal for large lesions in phantom and clinical studies, respectively. © 2023, Australasian College of Physical Scientists and Engineers in Medicine.