Abstract

PurposeThis study evaluated the effects of new Bayesian penalized likelihood (BPL) reconstruction algorithm on visualization and quantification of upper abdominal malignant tumors in clinical FDG PET/CT examinations, comparing the results to those obtained by an ordered subset expectation maximization (OSEM) reconstruction algorithm. Metabolic tumor volume (MTV) and texture features (TFs), as well as SUV-related metrics, were evaluated to clarify the BPL effects on quantification.Materials and MethodsA total of 153 upper abdominal lesions (82 liver metastatic and 71 pancreatic cancers) were included in this study. FDG PET/CT images were acquired with a GE Discovery 710 scanner equipped with a time-of-flight system. Images were reconstructed using OSEM and BPL (beta 700) algorithms. In 58 lesions <1.5 cm in greatest diameter (small-lesion group), visual image quality of each lesion was evaluated using a four-point scale. SUVmax was obtained for quantitative metrics. Visual scores and SUVmax were compared between OSEM and BPL images. In 95 lesions >2.0 cm in greatest diameter (larger-lesion group), SUVmax, SUVpeak, MTV, and six TFs were compared between OSEM and BPL images. In addition to the size-based analyses, an increase of SUVmax with BPL was evaluated according to the original SUVmax in OSEM images.ResultsIn the small-lesion group, both visual score and SUVmax were significantly higher in the BPL than OSEM images. The increase in visual score was observed in 20 (34%) of all 58 lesions. In the larger-lesion group, no statistical difference was observed in SUVmax, SUVpeak, or MTV between OSEM and BPL images. BPL increased high gray-level zone emphasis and decreased low gray-level zone emphasis among six TFs compared to OSEM with statistical significance. No statistical differences were observed in other TFs. SUVmax-based analysis demonstrated that BPL increased and decreased SUVmax in lesions with low (<5) and high (>10) SUVmax in original OSEM images, respectively.ConclusionThis study demonstrated that BPL improved conspicuity of small or low-count upper abdominal malignant lesions in clinical FDG PET/CT examinations. Only two TFs represented significant differences between OSEM and BPL images of all quantitative metrics in larger lesions.

Highlights

  • Ordered subset expectation maximization (OSEM) iterative algorithm has been used for image reconstruction in positron emission tomography (PET) imaging

  • We evaluated the effects of this new algorithm on visualization and quantification of upper abdominal malignant tumors in FDG PET/computed tomography (CT) examinations, comparing the results to those obtained by an OSEM reconstruction algorithm

  • No statistical difference was observed in SUVmax, SUVpeak, or Metabolic tumor volume (MTV) between OSEM and Bayesian penalized likelihood (BPL) images (Table 2)

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Summary

Introduction

Ordered subset expectation maximization (OSEM) iterative algorithm has been used for image reconstruction in positron emission tomography (PET) imaging. OSEM is known to have a limitation in quantification as it stopped before reaching full convergence due to image noise increased with each iteration. This compromise result in providing insufficient quantitative values. Limited reports have been available so far as to the effects of BPL focusing on abdominal lesions in 2-deoxy-2[F-18]fluoro-D-glucose (FDG) PET imaging [7]. BPL is expected to improve the visualization and detection of these abdominal lesions as well as pulmonary lesions

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