Departments of Nuclear Medicine.
Medical Imaging, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Zhifu District, Yantai, Shangdong Province, People's Republic of China.
Nucl Med Commun. 2022 Mar 1;43(3):284-291. doi: 10.1097/MNM.0000000000001516.
Recently, a new Bayesian penalty likelihood (BPL) reconstruction algorithm has been applied in PET, which is expected to provide better image resolution than the widely used ordered subset expectation maximization (OSEM). The purpose of this study is to compare the differences between these two algorithms in terms of image quality and effects on clinical diagnostics and quantification of lymphoma.
A total of 246 FDG-positive lesions in 70 patients with lymphoma were retrospectively analyzed by using BPL and OSEM + time-of-flight + point spread function algorithms. Visual analysis was used to evaluate the effects of different reconstruction algorithms on clinical image quality and diagnostic certainty. Quantitative analysis was used to compare the differences between pathology and lesion size.
There were significant differences in lesion-related SUVmax, total-lesion-glycolysis (TLG), and signal-to-background ratio (SBR) (P < 0.01). The variation Δ SUVmax% and Δ SBR% caused by the two reconstruction algorithms were negatively correlated with tumor diameter, while Δ MTV% and Δ TLG% were positively correlated with tumor diameter. In the grouped analysis based on pathology, there were significant differences in lesion SUVmax, lesion SUVmean, and SBR. In non-Hodgkin's lymphoma (diffuse large B cells and follicular lymphoma), diversities were significantly found in SUVmax, SUVmean, SBR, and TLG of the lesions (P < 0.05). According to the grouped analysis based on lesion size, for lesions smaller than 1 cm and 2 cm, there was a significant difference in SUVmean, SUVmax, SBR, and MTV, but not in lesions larger than or equal to 2 cm (P > 0.05), and the liver background SUVmean (P > 0.05) remained unchanged.
BPL reconstruction algorithm could effectively improve clinical image quality and diagnostic certainty. In quantitative analysis, there were no significant differences among different pathological groups, but there were significant diversities in lesion sizes. Especially for small lesions, lesion SUVmax increased and SBR was significantly improved, which may better assist in the diagnosis of small lesions of lymphoma.
最近，一种新的贝叶斯惩罚似然（BPL）重建算法已应用于 PET 中，预计比广泛使用的有序子集期望最大化（OSEM）提供更好的图像分辨率。本研究旨在比较这两种算法在图像质量以及对淋巴瘤的临床诊断和定量评估方面的差异。
回顾性分析了 70 例淋巴瘤 FDG 阳性病变患者的 246 个病灶，采用 BPL 和 OSEM+飞行时间+点扩散函数算法。采用视觉分析评估不同重建算法对临床图像质量和诊断确定性的影响。采用定量分析比较病理与病灶大小之间的差异。
病灶相关 SUVmax、总病灶糖酵解（TLG）和信号与背景比（SBR）存在显著差异（P＜0.01）。两种重建算法引起的病灶 SUVmax 变化率（Δ SUVmax%）和 SBR 变化率（Δ SBR%）与肿瘤直径呈负相关，而 Δ MTV%和 Δ TLG%与肿瘤直径呈正相关。基于病理分组分析，病灶 SUVmax、病灶 SUVmean 和 SBR 存在显著差异。在非霍奇金淋巴瘤（弥漫性大 B 细胞和滤泡性淋巴瘤）中，病灶 SUVmax、SUVmean、SBR 和 TLG 的多样性存在显著差异（P＜0.05）。基于病灶大小分组分析，对于直径小于 1cm 和 2cm 的病灶，SUVmean、SUVmax、SBR 和 MTV 存在显著差异，但直径大于或等于 2cm 的病灶无差异（P＞0.05），且肝脏背景 SUVmean 无差异（P＞0.05）。
BPL 重建算法可有效提高临床图像质量和诊断确定性。在定量分析中，不同病理组之间无显著差异，但病灶大小存在显著差异。特别是对于小病灶，病灶 SUVmax 增加，SBR 明显改善，这可能有助于淋巴瘤小病灶的诊断。
