Department of Medicine and Surgery, University of Milano-Bicocca, 20900, Monza, Italy.
Department of Nuclear Medicine, Scientific Institute San Raffaele, 20132, Milano, Italy.
Med Phys. 2017 May;44(5):1823-1836. doi: 10.1002/mp.12205. Epub 2017 Apr 13.
The effects of regularizing priors on the maximum likelihood (ML) reconstruction of activity patterns in Positron Emission Tomography (PET) were assessed.
Two edge-preserving priors (one originally proposed by Nuyts et al. and nowadays implemented and commercialized by General Electric Medical Systems as Q.Clear software, and a second one originally proposed by Rapisarda et al. and our group) were assessed and compared to a standard Ordered Subset (OS)-ML reconstruction, assumed as reference. The main difference between the two priors is that Nuyts prior (NY-p) penalizes relative voxel differences while Rapisarda prior (RP-p) absolute ones. Prior parameters were selected by imposing a reference noise texture inside uniform regions with activity comparable to that measured in F-FluoroDeoxyGlucose (FDG) patient livers overall the field of view. Comparisons were then made: (a) on phantom data in terms of sphere recovery coefficients, ability to correctly reconstruct uniform irregularly shaped objects and heterogeneous patterns in patient backgrounds; (b) on patient data in terms of lesion detectability and image quality.
On phantoms, both priors succeeded in improving all the assessed features with respect to standard OS-ML reconstruction, mainly thanks to the better signal convergence and to the noise breakup control. On 10 mm spheres, an average recovery coefficient augment of 9% (NY-p) and 34% (RP-p) was obtained; homogeneity of uniform activity objects augmented of 4% (NY-p) and 11% (RP-p); accuracy in reconstructing heterogeneous lesions improved on average of 5% (NY-p) and 15% (RP-p). On patients, lesion detectability resulted improved (on 27 of 30 lesions), regardless of lesion anatomical districts and position in the scanner field of view. NY-p provides a spatial resolution and a noise texture more uniform in the field of view and an image quality similar to standard OS-ML. RP-p has instead a behavior more dependent on the local counting statistics that imposes a trade-off between spatial resolution uniformity and noise texture homogeneity.
The assessed regularizing priors improve PET uptake pattern reconstruction accuracy. Therefore, they should be considered both for oncological lesion detection and uptake spatial distribution assessment. Pitfalls and open challenges are also discussed.
评估正则化先验对正电子发射断层扫描（PET）中活性模式的最大似然（ML）重建的影响。
评估并比较了两种边缘保持先验（一种最初由 Nuyts 等人提出，现已由通用电气医疗系统实施和商业化，作为 Q.Clear 软件，另一种最初由 Rapisarda 等人提出，由我们小组提出）与标准有序子集（OS）-ML 重建作为参考。这两种先验的主要区别在于 Nuyts 先验（NY-p）惩罚相对体素差异，而 Rapisarda 先验（RP-p）惩罚绝对体素差异。通过在整个视场中具有与 F-氟脱氧葡萄糖（FDG）患者肝脏中测量的活性相当的活性的均匀区域内施加参考噪声纹理，选择先验参数。然后进行了以下比较：（a）在体模数据方面，比较了球体恢复系数、正确重建不均匀形状均匀物体和患者背景中异质模式的能力；（b）在患者数据方面，比较了病变可检测性和图像质量。
在体模上，两种先验都成功地提高了相对于标准 OS-ML 重建的所有评估特征，主要是由于更好的信号收敛和噪声分解控制。对于 10mm 球体，获得了平均恢复系数提高 9%（NY-p）和 34%（RP-p）；均匀活动物体的均匀性提高了 4%（NY-p）和 11%（RP-p）；重建异质病变的准确性平均提高了 5%（NY-p）和 15%（RP-p）。在患者中，无论病变解剖区域和在扫描仪视场中的位置如何，病变的可检测性都得到了改善。NY-p 提供了在视场中更均匀的空间分辨率和噪声纹理，以及与标准 OS-ML 相似的图像质量。RP-p 的行为则更多地取决于局部计数统计，这在空间分辨率均匀性和噪声纹理均匀性之间施加了权衡。
评估的正则化先验提高了 PET 摄取模式重建的准确性。因此，它们应同时用于肿瘤病变检测和摄取空间分布评估。还讨论了陷阱和未解决的挑战。
