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.

基于先验图像的重建 (PIBR) 方法是降低辐射剂量和提高低剂量计算机断层扫描 (CT) 图像质量的有力工具。除了解剖学变化之外，先前和当前图像也可能具有不同的衰减，因为它们源自不同的扫描仪或来自同一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、光束过滤器）。在这种情况下，由于先验衰减不匹配，PIBR 具有挑战性。在这项工作中，我们研究了一种称为统计图像重建的特定 PIBR 方法，使用正常剂量图像诱导的非局部均值正则化 (SIR-ndiNLM)，以解决具有这种衰减不匹配先验的 PIBR 问题，并实现定量低剂量 CT 成像。我们为原始的 SIR-ndiNLM 方法提出了两种校正方案，(1) 全局直方图匹配方法和 (2) 局部衰减校正方法，以解决 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 CT 扫描仪获取的图像来模拟衰减不匹配来验证所提出方案的有效性。同时，我们利用不同的 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，当使用衰减不匹配先验时，原始 SIR-ndiNLM 会在重建中引入伪影。此外，我们发现先前图像和当前图像之间较大的衰减不匹配会导致 SIR-ndiNLM 重建中出现更严重的伪影。我们提出的两个校正方案使 SIR-ndiNLM 能够有效地处理两个图像之间的衰减不匹配和解剖学变化，并成功消除伪影。我们证明，所提出的技术允许 SIR-ndiNLM 利用先验衰减不匹配，并从低通量和稀疏视图数据采集中实现定量低剂量 CT 重建。这项工作允许对使用不同光束设置获取的 CT 数据进行稳健可靠的 PIBR。