Bone SPECT/CT has been shown to offer superior sensitivity and specificity compared to conventional whole-body planar scanning. Furthermore, bone SPECT/CT allows quantitative imaging, which is challenging with planar methods. In order to gain better quantitative accuracy, Bayesian reconstruction algorithms, including both image derived and anatomically guided priors, have been utilized in reconstruction in PET/CT scanning, but they have not been widely used in SPECT/CT studies. Therefore, the aim of this work was to evaluate the performance of CT-guided reconstruction in quantitative bone SPECT. Three Bayesian reconstruction methods were evaluated against the conventional ordered subsets expectation maximization (OSEM) reconstruction method. One of the studied Bayesian methods was the relative difference prior (RDP), which has recently gained popularity in PET reconstruction. The other two methods, anatomically guided smoothing prior (AMAP-S) and anatomically guided relative difference prior (AMAP-R), utilized anatomical information from the CT scan. The reconstruction methods were evaluated in terms of quantitative accuracy with artificial lesions inserted in clinical patient studies and with 20 real clinical patients. Maximum and mean standardized uptake values (SUVs) of the lesions were defined. The analyses showed that all studied Bayesian methods performed better than OSEM and the anatomical priors also outperformed RDP. The average relative error in mean SUV for the artificial lesion study for OSEM, RDP, AMAP-S, and AMAP-R was − 53%, − 35%, − 15%, and − 10%, when the CT study had matching lesions. In the patient study, the RDP method gave 16 ± 9% higher maximum SUV values than OSEM, while AMAP-S and AMAP-R offered increases of 36 ± 8% and 36 ± 9%, respectively. Mean SUV increased for RDP, AMAP-S, and AMAP-R by 18 ± 9%, 26 ± 5%, and 33 ± 5% when compared to OSEM. The Bayesian methods with anatomical prior, especially the relative difference prior-based method (AMAP-R), outperformed OSEM and reconstruction without anatomical prior in terms of quantitative accuracy.

中文翻译：

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利用解剖学信息定量骨SPECT / CT重建

与传统的全身平面扫描相比，骨SPECT / CT已显示出更高的灵敏度和特异性。此外，骨骼SPECT / CT可以进行定量成像，这对于平面方法而言具有挑战性。为了获得更好的定量精度，贝叶斯重建算法，包括图像导出和解剖学指导的先验，已被用于PET / CT扫描的重建中，但尚未在SPECT / CT研究中广泛使用。因此，这项工作的目的是评估定量骨SPECT中CT引导重建的性能。针对常规有序子集期望最大化（OSEM）重建方法，评估了三种贝叶斯重建方法。贝叶斯方法之一是相对先验相对差（RDP），最近在PET重建中受到欢迎。其他两种方法是解剖学上先导的平滑先验（AMAP-S）和解剖学上先导的相对差先验（AMAP-R），它们利用了来自CT扫描的解剖学信息。根据在临床患者研究中插入的人工病变和20名实际临床患者的定量准确性评估了重建方法。定义了病变的最大和平均标准化摄取值（SUV）。分析表明，所有研究的贝叶斯方法都比OSEM表现更好，并且解剖学先验也优于RDP。当CT研究具有相匹配的病变时，针对OSEM，RDP，AMAP-S和AMAP-R的人工病变研究，平均SUV的平均相对误差为-53％，-35％，-15％和-10％。 。在病人研究中 RDP方法的最大SUV值比OSEM高出16±9％，而AMAP-S和AMAP-R分别提高了36±8％和36±9％。与OSEM相比，RDP，AMAP-S和AMAP-R的平均SUV增加了18±9％，26±5％和33±5％。在定量准确性方面，具有解剖先验的贝叶斯方法，尤其是基于相对差异先验的方法（AMAP-R）优于OSEM和没有解剖先验的重建。