Q.Clear is a Bayesian penalized likelihood (BPL) reconstruction algorithm that presents improvements in signal-to-noise ratio (SNR) in clinical positron emission tomography (PET) scans. Brain studies in research require a reconstruction that provides a good spatial resolution and accentuates contrast features however, filtered back-projection (FBP) reconstruction is not available on GE SIGNA PET-Magnetic Resonance (PET-MR) and studies have been reconstructed with an ordered subset expectation maximization (OSEM) algorithm. This study aims to propose a strategy to approximate brain PET quantitative outcomes obtained from images reconstructed with Q.Clear versus traditional FBP and OSEM. Contrast recovery and background variability were investigated with the National Electrical Manufacturers Association (NEMA) Image Quality (IQ) phantom. Resolution, axial uniformity and SNR were investigated using the Hoffman phantom. Both phantoms were scanned on a Siemens Biograph 6 TruePoint PET-Computed Tomography (CT) and a General Electric SIGNA PET-MR, for FBP, OSEM and Q.Clear. Differences between the metrics obtained with Q.Clear with different β values and FBP obtained on the PET-CT were determined. For in plane and axial resolution, Q.Clear with low β values presented the best results, whereas for SNR Q.Clear with higher β gave the best results. The uniformity results are greatly impacted by the β value, where β < 600 can yield worse uniformity results compared with the FBP reconstruction. This study shows that Q.Clear improves contrast recovery and provides better resolution and SNR, in comparison to OSEM, on the PET-MR. When using low β values, Q.Clear can provide similar results to the ones obtained with traditional FBP reconstruction, suggesting it can be used for quantitative brain PET kinetic modelling studies.

中文翻译：

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Q.Clear重建框架与常规重建算法在定量脑PET-MR研究中的性能评估

Q.Clear是一种贝叶斯罚分似然（BPL）重建算法，可在临床正电子发射断层扫描（PET）扫描中提高信噪比（SNR）。研究中的脑研究需要重建，以提供良好的空间分辨率并强调对比度特征，但是，在GE SIGNA PET磁共振（PET-MR）上无法使用滤波反投影（FBP）重建，并且已经按顺序重建了研究子集期望最大化（OSEM）算法。这项研究旨在提出一种策略，以估计从Q.Clear与传统FBP和OSEM重建的图像中获得的大脑PET定量结果。使用美国国家电器制造商协会（NEMA）图像质量（IQ）体模研究了对比度恢复和背景变化。解析度，使用霍夫曼模型对轴向均匀性和SNR进行了研究。两种体模均在Siemens Biograph 6 TruePoint PET计算机断层扫描（CT）和General Electric SIGNA PET-MR上进行了FBP，OSEM和Q.Clear扫描。确定了使用具有不同β值的Q.Clear获得的指标与在PET-CT上获得的FBP之间的差异。对于平面和轴向分辨率，具有低β值的Q.Clear表现出最好的结果，而对于具有高β值的SNR Q.Clear，表现出最好的结果。β值极大地影响了均匀性结果，与FBP重建相比，β<600可能会产生更差的均匀性结果。这项研究表明，与OSEM相比，Q.Clear在PET-MR上改善了对比度恢复，并提供了更好的分辨率和SNR。当使用低β值时，Q。