Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy.
Assessment of prior image induced nonlocal means regularization for low-dose CT reconstruction: Change in anatomy.
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DOI：
10.1002/mp.12378
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发表时间：
2017-09
期刊：
影响因子：
3.8
通讯作者：
Liang Z
中科院分区：
文献类型：
--
作者：
Zhang H;Ma J;Wang J;Moore W;Liang Z
重复计算机断层扫描 (CT) 扫描适用于某些临床应用，例如肺结节监测。多项研究表明，将高质量的先前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。我们之前提出的 LDCT 正常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，基于现有图像的方法的一个主要问题是，当先前图像和当前 LDCT 图像显示不同的结构时（例如，如果肺结节随着时间的推移出现、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的情况下的性能。我们将 ndiNLM 正则化纳入统计图像重建 (SIR) 框架中，以重建后续 LDCT 图像。由于其基于补丁的搜索机制，先前图像和当前 LDCT 图像之间的粗略配准对于 SIR-ndiNLM 方法来说就足够了。我们评估了 SIR-ndiNLM 方法在两种不同情况下肺结节监测中的性能：（1）在基线检查中未发现结节，但在后续 LDCT 扫描中出现； (2) 结节在基线检查中存在，但在后续 LDCT 扫描中消失。我们进一步研究了结节尺寸对 SIR-ndiNLM 方法性能的影响。我们发现SIR-ndiNLM方法应该使用相对较大的搜索窗口（例如33×33）来解决先前图像和当前LDCT图像之间的未对准问题，并确保可以在先前图像中找到足够的相似补丁。通过正确选择其他参数，两个患者数据集的实验结果表明，SIR-ndiNLM 方法不会错过真实结节，也不会在上述肺结节监测场景中引入假结节。我们还发现，当先验图像与当前的 LDCT 解剖图像相似时，SIR-ndiNLM 重建显示出图像质量的提高。这些图像质量的提升在目视检查时可能显得很小，但可以使用定量测量来检测。最后，SIR-ndiNLM 方法在超低剂量条件和不同结节尺寸下也表现良好。本研究评估了 SIR-ndiNLM 方法在先前图像和当前 LDCT 图像显示显着解剖差异（特别是肺结节变化）的情况下的性能。实验结果表明，SIR-ndiNLM方法不会引入假肺结节，也不会错过真正的结节，这缓解了该方法可能产生错误信息的担忧。然而，没有足够的证据表明这些发现适用于各种解剖学变化。
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: (1) the nodule was not found in a baseline exam but appears in a follow-up LDCT scan; (2) 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.
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