Background: The Bayesian penalized likelihood PET reconstruction (BPL) algorithm, Q.Clear (GE Healthcare), has recently been clinically applied to clinical image reconstruction. The BPL includes a relative difference penalty (RDP) as a penalty function.
View Article and Find Full Text PDFPurpose: The Bayesian penalized likelihood (BPL) reconstruction algorithm, Q.Clear, can achieve a higher signal-to-noise ratio on images and more accurate quantitation than ordered subset-expectation maximization (OSEM). The reconstruction parameter (β) in BPL requires optimization according to the radiopharmaceutical tracer.
View Article and Find Full Text PDFBackground: The Bayesian penalized likelihood (BPL) algorithm Q.Clear (GE Healthcare) allows fully convergent iterative reconstruction that results in better image quality and quantitative accuracy, while limiting image noise. The present study aimed to optimize BPL reconstruction parameters for F-NaF PET/CT images and to determine the feasibility of F-NaF PET/CT image acquisition over shorter durations in clinical practice.
View Article and Find Full Text PDFObjective: Many advances in PET/CT technology can potentially improve image quality and the ability to detect small lesions. A new digital TOF-PET/CT scanner based on silicon photomultipliers (SiPM) integrated with a Bayesian penalized likelihood (BPL) PET reconstruction algorithm (Q.Clear; GE Healthcare) has been introduced into clinical practice.
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