Vallot Delphine, Caselles Olivier, Chaltiel Leonor, Fernandez Anthony, Gabiache Erwan, Dierickx Lawrence, Zerdoud Slimane, Bauriaud Mathilde, Courbon Frédéric
The University Cancer Institute Toulouse Oncopole, Toulouse, France.
Nucl Med Commun. 2017 Nov;38(11):979-984. doi: 10.1097/MNM.0000000000000729.
The aim of this study was to evaluate the impact of using the Bayesian penalized likelihood (BPL) algorithm on a bismuth germanium oxide positron emission tomography (PET)/computed tomography (CT) system for F-FDG PET/CT exams in case of low injected activity and scan duration.
F-FDG respiratory gated PET/CT performed on 102 cancer patients, injected with ∼2 MBq/kg of F-FDG, were reconstructed using two algorithms: ordered subset expectation maximization (OSEM) and BPL. The signal-to-noise ratio (SNR) was calculated as the ratio of mean standard uptake value (SUV) over the standard deviation in a reference volume defined automatically in the liver. The peak SUV and volumes were also measured in lesions larger than 2 cm thanks to the automated segmentation method.
On 85 respiratory gated patients, the median SNR was significantly higher with BPL (P<0.0001) and it is even better when the BMI of the patient increases (odds ratio=1.26).For the 55 lesions, BPL significantly increased the SUVpeak [difference: (-0.5; 1.4), median=0.4, P<0.0001] compared with OSEM in 83.6% of the cases. With BPL, the volume was lower in 61.8% of the cases compared with OSEM, but this was not statistically significant.
The BPL algorithm improves the image quality and lesion contrast and appears to be particularly appropriate for patients with a high BMI as it improves the SNR. However, it will be important for patient follow-up or multicenter studies to use the same algorithm and preferably BPL.
本研究旨在评估在低注射活度和扫描时长情况下，使用贝叶斯惩罚似然（BPL）算法对氧化铋锗正电子发射断层扫描（PET）/计算机断层扫描（CT）系统进行F-FDG PET/CT检查的影响。
对102例注射了约2 MBq/kg F-FDG的癌症患者进行F-FDG呼吸门控PET/CT检查，并使用两种算法进行重建：有序子集期望最大化（OSEM）和BPL。信噪比（SNR）计算为肝脏中自动定义的参考体积内平均标准摄取值（SUV）与标准差之比。借助自动分割方法，还对大于2 cm的病变测量了SUV峰值和体积。
在85例呼吸门控患者中，BPL算法的中位SNR显著更高（P<0.0001），且患者体重指数（BMI）增加时效果更佳（优势比=1.26）。对于55个病变，与OSEM相比，BPL在83.6%的病例中显著提高了SUV峰值[差异：（-0.5；1.4），中位数=0.4，P<0.0001]。使用BPL时，61.8%的病例中病变体积比OSEM算法更低，但差异无统计学意义。
BPL算法提高了图像质量和病变对比度，对于BMI较高的患者似乎尤为适用，因为它提高了SNR。然而，对于患者随访或多中心研究而言，使用相同算法（最好是BPL）非常重要。
