Background and Objective. When radiologists diagnose lung diseases in chest radiography, they can miss some lung nodules overlapped with ribs or clavicles. Dual-energy subtraction (DES) imaging performs well because it can produce soft tissue images, in which the bone components in chest radiography were almost suppressed but the visibility of nodules and lung vessels was still maintained. However, most routinely available X-ray machines do not possess the DES function. Thus, we presented a data-driven decomposition model to perform virtual DES function for decomposing a single conventional chest radiograph into soft tissue and bone images. Methods. For a given chest radiograph, similar chest radiographs with corresponding DES soft tissue and bone images are selected from the training database as exemplars for decomposition. The corresponding fields between the observed chest radiograph and the exemplars are solved by a hierarchically dense matching algorithm. Then, nonparametric priors of soft tissue and bone components are constructed by sampling image patches from the selected soft tissue and bone images according to the corresponding fields. Finally, these nonparametric priors are integrated into our decomposition model, the energy function of which is efficiently optimized by an iteratively reweighted least-squares scheme (IRLS). Results. The decomposition method is evaluated on a data set of posterior-anterior DES radiography (503 cases), as well as on the JSRT data set. The proposed method can produce soft tissue and bone images similar to those produced by the actual DES system. Conclusions. The proposed method can markedly reduce the visibility of bony structures in chest radiographs and shows potential to enhance diagnosis.

中文翻译：

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使用非参数图像先验的常规胸片的软组织/骨分解。

背景和目的。当放射科医生在胸片中诊断肺部疾病时，他们可能会漏掉一些与肋骨或锁骨重叠的肺结节。双能减影（DES）成像表现良好，因为它可以产生软组织图像，其中胸片中的骨骼成分几乎被抑制，但结节和肺血管的可见性仍然保持。然而，大多数常规可用的 X 射线机不具备 DES 功能。因此，我们提出了一种数据驱动的分解模型来执行虚拟 DES 功能，以将单个常规胸片分解为软组织和骨骼图像。方法. 对于给定的胸片，从训练数据库中选择具有相应 DES 软组织和骨骼图像的类似胸片作为样本进行分解。观察到的胸片和样本之间的对应场由分层密集匹配算法解决。然后，通过根据相应字段从选定的软组织和骨骼图像中采样图像块，构建软组织和骨骼成分的非参数先验。最后，这些非参数先验被集成到我们的分解模型中，其能量函数通过迭代重加权最小二乘方案 (IRLS) 得到有效优化。结果. 分解方法在前后 DES 射线照相数据集（503 例）以及 JSRT 数据集上进行评估。所提出的方法可以生成类似于实际 DES 系统生成的软组织和骨骼图像。结论。所提出的方法可以显着降低胸片中骨结构的可见性，并显示出增强诊断的潜力。