Recently, a Bayesian penalized likelihood (BPL) reconstruction algorithm was introduced for a commercial PET/CT with the potential to improve image quality. We compared the performance of this BPL algorithm with conventional reconstruction algorithms under realistic clinical conditions such as daily practiced at many European sites, i.e. low 18F-FDG dose and short acquisition times. To study the performance of the BPL algorithm, regular clinical 18F-FDG whole body PET scans were made. In addition, two types of phantoms were scanned with 4-37 mm sized spheres filled with 18F-FDG at sphere-to-background ratios of 10-to-1, 4-to-1, and 2-to-1. Images were reconstructed using standard ordered-subset expectation maximization (OSEM), OSEM with point spread function (PSF), and the BPL algorithm using β-values of 450, 550 and 700. To quantify the image quality, the lesion detectability, activity recovery, and the coefficient of variation (COV) within a single bed position (BP) were determined. We found that when applying the BPL algorithm both smaller lesions in clinical studies as well as spheres in phantom studies can be detected more easily due to a higher SUV recovery, especially for higher contrast ratios. Under standard clinical scanning conditions, i.e. low number of counts, the COV is higher for the BPL (β=450) than the OSEM+PSF algorithm. Increase of the β-value to 550 or 700 results in a COV comparable to OSEM+PSF, however, at the cost of contrast, though still better than OSEM+PSF. At the edges of the axial field of view (FOV) where BPs overlap, COV can increase to levels at which bands become visible in clinical images, related to the lower local axial sensitivity of the PET/CT, which is due to the limited bed overlap of 23% such as advised by the manufacturer. The BPL algorithm performs better than the standard OSEM+PSF algorithm on small lesion detectability, SUV recovery, and noise suppression. Increase of the percentage of bed overlap, time per BP, administered activity, or the β-value, all have a direct positive impact on image quality, though the latter with some loss of small lesion detectability. Thus, BPL algorithms are very interesting for improving image quality, especially in small lesion detectability.

中文翻译：

---

低计数临床¹⁸ F-FDG PET / CT的贝叶斯惩罚似然重建算法的评估

最近，针对具有改进图像质量潜力的商业PET / CT引入了贝叶斯惩罚似然（BPL）重建算法。我们将这种BPL算法与常规重建算法在现实临床条件下（例如在许多欧洲站点的日常实践，即18F-FDG剂量低和采集时间短）的性能进行了比较。为了研究BPL算法的性能，定期进行了临床18F-FDG全身PET扫描。另外，用球体与背景之比为10：1、4：1和2：1的4-37 mm大小的球体填充18F-FDG来扫描两种类型的体模。使用标准有序子集期望最大化（OSEM），具有点扩展函数的OSEM（PSF）和BPL算法（β值分别为450、550和700）重建图像。为了量化图像质量，确定了单个床位（BP）内的病变可检测性，活动恢复和变异系数（COV）。我们发现，当使用BPL算法时，由于较高的SUV恢复率（尤其是对于较高的对比度），临床研究中的较小病变以及体模研究中的球体都可以更容易地检测到。在标准临床扫描条件下，即计数数少，BPL的COV较高（β= 450），比OSEM + PSF算法高。将β值提高到550或700可以得到与OSEM + PSF相当的COV，尽管代价还是要好于OSEM + PSF。在BP重叠的轴向视场（FOV）边缘，COV可以增加到在临床图像中可见条带的水平，与PET / CT的较低局部轴向灵敏度有关，这是由于床层重叠受限（如制造商建议的那样）为23％。BPL算法在小病灶可检测性，SUV恢复和噪声抑制方面比标准OSEM + PSF算法表现更好。病床重叠百分比，每BP时间，施加的活动或β值的增加都对图像质量有直接的积极影响，尽管后者会损失一些小的病变。因此，BPL算法对于提高图像质量非常有意思，特别是在小的病变可检测性方面。病床重叠百分比，每BP时间，施加的活动或β值的增加都对图像质量有直接的积极影响，尽管后者会损失一些小的病变。因此，BPL算法对于提高图像质量非常有意思，特别是在小的病变可检测性方面。病床重叠百分比，每BP时间，施加的活动或β值的增加都对图像质量有直接的积极影响，尽管后者会损失一些小的病变。因此，BPL算法对于提高图像质量非常有意思，特别是在小的病变可检测性方面。