The Impact of a Bayesian Penalized Likelihood Reconstruction Algorithm on the Evaluation of Indeterminate Pulmonary Nodules by Dual–Time Point 18F-FDG PET/CT

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Abstract

We present the case of a patient with history of colon cancer, referred for the evaluation of indeterminate pulmonary nodules by 18F-FDG PET/CT. A dual–time point protocol was performed, and images were reconstructed using VUE Point HD and a Bayesian-penalized likelihood reconstruction algorithm (Q.Clear). Visually, the quality of the images was considered better when Q.Clear was used with β value of 200, uptake in the smallest nodule (7 mm) was clearly visible only with Q.Clear reconstruction, and uptake in the smaller nodules was best defined in the delayed time point acquisition. Quantitative parameters were also higher for Q.Clear.

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