Determination of optimal regularization factor in Bayesian penalized likelihood reconstruction of brain PET images using [ F]FDG and [ C]PiB.

Purpose: 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. The present study aimed to define the optimal β value in BPL required to diagnose Alzheimer disease from brain positron emission tomography (PET) images acquired using F-fluoro-2-deoxy-D-glucose ([ F]FDG) and C-labeled Pittsburg compound B ([ C]PiB).

Methods: Images generated from Hoffman 3D brain and cylindrical phantoms were acquired using a Discovery PET/computed tomography (CT) 710 and reconstructed using OSEM + time-of-flight (TOF) under clinical conditions and BPL + TOF (β = 20-1000). Contrast was calculated from images generated by the Hoffman 3D brain phantom, and noise and uniformity were calculated from those generated by the cylindrical phantom. Five cognitively healthy controls and five patients with Alzheimer disease were assessed using [ F]FDG and [ C]PiB PET to validate the findings from the phantom study. The β values were restricted by the findings of the phantom study, then one certified nuclear medicine physician and two certified nuclear medicine technologists visually determined optimal β values by scoring the quality parameters of image contrast, image noise, cerebellar stability, and overall image quality of PET images from 1 (poor) to 5 (excellent).

Results: The contrast in BPL satisfied the Japanese Society of Nuclear Medicine (JSNM) criterion of ≥55% and exceeded that of OSEM at ranges of β = 20-450 and 20-600 for [ F]FDG and [ C]PiB, respectively. The image noise in BPL satisfied the JSNM criterion of ≤15% and was below that in OSEM when β = 150-1000 and 400-1000 for [ F]FDG and [ C]PiB, respectively. The phantom study restricted the ranges of β values to 100-300 and 300-500 for [ F]FDG and [ C]PiB, respectively. The BPL scores for gray-white matter contrast and image noise, exceeded those of OSEM in [ F]FDG and [ C]PiB images regardless of β values. Visual evaluation confirmed that the optimal β values were 200 and 450 for [ F]FDG and [ C]PiB, respectively.

Conclusions: The BPL achieved better image contrast and less image noise than OSEM, while maintaining quantitative standardized uptake value ratios (SUVR) due to full convergence, more rigorous noise control, and edge preservation. The optimal β values for [ F]FDG and [ C]PiB brain PET were apparently 200 and 450, respectively. The present study provides useful information about how to determine optimal β values in BPL for brain PET imaging.