Prior-image-based CT reconstruction using attenuation-mismatched priors.
Prior-image-based CT reconstruction using attenuation-mismatched priors.
复制标题
使用衰减不匹配先验进行基于先验图像的 CT 重建
DOI：
10.1088/1361-6560/abe760
复制
发表时间：
2021-03-17
影响因子：
3.5
通讯作者：
Xing L
中科院分区：
文献类型：
--
作者：
Zhang H;Capaldi D;Zeng D;Ma J;Xing L
基于先验图像的重建(PIBR)方法是降低辐射剂量、提高低剂量CT图像质量的有力工具。除了解剖变化之外，先前和当前图像也可能具有不同的衰减，因为它们来自不同的扫描仪或来自相同的扫描仪，但在数据采集期间具有不同的X射线束质量(例如，KVP设置、束过滤器)。在这种情况下，由于衰减不匹配的先验，PIBR是具有挑战性的。在这项工作中，我们研究了一种特殊的PIBR方法，称为统计图像重建，使用正常剂量图像诱导的非局部均值正则化(SIR-ndiNLM)来处理具有这种衰减失配先验的PIBR，并实现定量的低剂量CT成像。我们对原始的SIR-ndiNLM方法提出了两种校正方案，(1)全局直方图匹配法和(2)局部衰减校正法，以考虑PIBR中先前图像和当前图像之间的衰减差异。我们使用从双能量CT扫描仪获取的图像来模拟衰减失配，从而验证了所提出的方案的有效性。同时，我们使用不同的CT切片来模拟先前和当前低剂量图像之间的解剖不匹配或变化。我们观察到，当使用衰减失配先验时，原始的SIR-ndiNLM在重建中引入了伪影。此外，我们发现，在SIR-ndiNLM重建中，先前图像和当前图像之间的较大衰减失配会导致更严重的伪影。我们提出的两种校正方案使SIR-ndiNLM能够有效地处理两幅图像之间的衰减失配和解剖变化，并成功地消除了伪影。我们证明，所提出的技术允许SIR-ndiNLM利用衰减失配的先验，并从低通量和稀疏视图数据采集中实现定量的低剂量CT重建。这项工作允许对使用不同射束设置采集的CT数据进行稳健和可靠的PIBR。
Prior-image-based reconstruction (PIBR) methods are powerful tools for reducing radiation doses and improving the image quality of low-dose computed tomography (CT). Apart from anatomical changes, prior and current images can also have different attenuations because they originated from different scanners or from the same scanner but with different x-ray beam qualities (e.g., kVp settings, beam filters) during data acquisition. In such scenarios, with attenuation-mismatched priors, PIBR is challenging. In this work, we investigate a specific PIBR method, called statistical image reconstruction, using normal-dose image-induced nonlocal means regularization (SIR-ndiNLM), to address PIBR with such attenuation-mismatched priors and achieve quantitative low-dose CT imaging. We propose two corrective schemes for the original SIR-ndiNLM method, (1) a global histogram-matching approach and (2) a local attenuation correction approach, to account for the attenuation differences between the prior and current images in PIBR. We validate the efficacy of the proposed schemes using images acquired from dual-energy CT scanners to simulate attenuation mismatches. Meanwhile, we utilize different CT slices to simulate anatomical mismatches or changes between the prior and the current low-dose image. We observe that the original SIR-ndiNLM introduces artifacts to the reconstruction when an attenuation-mismatched prior is used. Furthermore, we find that a larger attenuation mismatch between the prior and current images results in more severe artifacts in the SIR-ndiNLM reconstruction. Our two proposed corrective schemes enable SIR-ndiNLM to effectively handle the attenuation mismatch and anatomical changes between the two images and successfully eliminate the artifacts. We demonstrate that the proposed techniques permit SIR-ndiNLM to leverage the attenuation-mismatched prior and achieve quantitative low-dose CT reconstruction from both low-flux and sparse-view data acquisitions. This work permits robust and reliable PIBR for CT data acquired using different beam settings.
影响因子：
3.8
作者：
Zhang H;Ma J;Wang J;Moore W;Liang Z
通讯作者：
Liang Z