Department of Radiology, Stony Brook University, NY, 11794, USA.
Department of Biomedical Engineering, Stony Brook University, NY, 11794, USA.
Med Phys. 2017 Sep;44(9):e264-e278. doi: 10.1002/mp.12378.
Repeated computed tomography (CT) scans are prescribed for some clinical applications such as lung nodule surveillance. Several studies have demonstrated that incorporating a high-quality prior image into the reconstruction of subsequent low-dose CT (LDCT) acquisitions can either improve image quality or reduce data fidelity requirements. Our proposed previous normal-dose image induced nonlocal means (ndiNLM) regularization method for LDCT is an example of such a method. However, one major concern with prior image based methods is that they might produce false information when the prior image and the current LDCT image show different structures (for example, if a lung nodule emerges, grows, shrinks, or disappears over time). This study aims to assess the performance of the ndiNLM regularization method in situations with change in anatomy.
We incorporated the ndiNLM regularization into the statistical image reconstruction (SIR) framework for reconstruction of subsequent LDCT images. Because of its patch-based search mechanism, a rough registration between the prior image and the current LDCT image is adequate for the SIR-ndiNLM method. We assessed the performance of the SIR-ndiNLM method in lung nodule surveillance for two different scenarios: (a) the nodule was not found in a baseline exam but appears in a follow-up LDCT scan; (b) the nodule was present in a baseline exam but disappears in a follow-up LDCT scan. We further investigated the effect of nodule size on the performance of the SIR-ndiNLM method.
We found that a relatively large search-window (e.g., 33 × 33) should be used for the SIR-ndiNLM method to account for misalignment between the prior image and the current LDCT image, and to ensure that enough similar patches can be found in the prior image. With proper selection of other parameters, experimental results with two patient datasets demonstrated that the SIR-ndiNLM method did not miss true nodules nor introduce false nodules in the lung nodule surveillance scenarios described above. We also found that the SIR-ndiNLM reconstruction shows improved image quality when the prior image is similar to the current LDCT image in anatomy. These gains in image quality might appear small upon visual inspection, but they can be detected using quantitative measures. Finally, the SIR-ndiNLM method also performed well in ultra-low-dose conditions and with different nodule sizes.
This study assessed the performance of the SIR-ndiNLM method in situations in which the prior image and the current LDCT image show substantial anatomical differences, specifically, changes in lung nodules. The experimental results demonstrate that the SIR-ndiNLM method does not introduce false lung nodules nor miss true nodules, which relieves the concern that this method might produce false information. However, there is insufficient evidence that these findings will hold true for all kinds of anatomical changes.
重复进行计算机断层扫描（CT）是某些临床应用的规定，如肺结节监测。多项研究表明，将高质量的先验图像纳入后续低剂量 CT（LDCT）采集的重建中，可以提高图像质量或降低数据保真度要求。我们提出的用于 LDCT 的先前标准剂量图像诱导非局部均值（ndiNLM）正则化方法就是这种方法的一个例子。然而，基于先验图像的方法的一个主要问题是，当先验图像和当前 LDCT 图像显示不同的结构时（例如，如果肺结节随时间出现、生长、缩小或消失），它们可能会产生错误信息。本研究旨在评估 ndiNLM 正则化方法在解剖结构发生变化时的性能。
我们将 ndiNLM 正则化纳入到用于重建后续 LDCT 图像的统计图像重建（SIR）框架中。由于其基于补丁的搜索机制，先验图像和当前 LDCT 图像之间的粗略配准对于 SIR-ndiNLM 方法来说已经足够了。我们评估了 SIR-ndiNLM 方法在两种不同情况下的肺结节监测性能：（a）在基线检查中未发现结节，但在后续 LDCT 扫描中出现；（b）在基线检查中存在结节，但在后续 LDCT 扫描中消失。我们进一步研究了结节大小对 SIR-ndiNLM 方法性能的影响。
我们发现，为了考虑先验图像和当前 LDCT 图像之间的配准偏差，并确保可以在前图像中找到足够相似的补丁，应该使用相对较大的搜索窗口（例如，33×33）。通过对两个患者数据集的实验结果进行适当的参数选择，结果表明，在上述肺结节监测场景中，SIR-ndiNLM 方法既不会遗漏真正的结节，也不会引入虚假的结节。我们还发现，当先验图像与当前 LDCT 图像在解剖结构上相似时，SIR-ndiNLM 重建可以提高图像质量。通过视觉检查，这些图像质量的提高可能看起来很小，但可以通过定量测量来检测。最后，SIR-ndiNLM 方法在超低剂量条件下和不同结节大小时也表现良好。
本研究评估了 SIR-ndiNLM 方法在先验图像和当前 LDCT 图像显示明显解剖差异的情况下的性能，特别是在肺结节的变化情况下。实验结果表明，SIR-ndiNLM 方法不会引入虚假的肺结节，也不会遗漏真正的结节，这减轻了人们对该方法可能会产生错误信息的担忧。然而，没有足够的证据表明这些发现对所有类型的解剖变化都适用。
