Department of Electrical Engineering, Tel Aviv University, Ramat-Aviv, Israel.
Diagnostic Imaging Institute, Sheba Medical Center, Affiliated with Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel.
Int J Comput Assist Radiol Surg. 2017 Dec;12(12):2145-2155. doi: 10.1007/s11548-017-1621-6. Epub 2017 Jun 10.
Low-dose CT screening of the lungs is becoming a reality, triggering many more CT-guided lung biopsies. During these biopsies, the patient is submitted to repeated guiding scans with substantial cumulated radiation dose. Extension of the dose reduction to the biopsy procedure is therefore necessary. We propose an image denoising algorithm that specifically addresses the setup of CT-guided lung biopsies. It minimizes radiation exposure while keeping the image quality appropriate for navigation to the target lesion.
A database of high-SNR CT patches is used to filter noisy pixels in a non-local means framework, while explicitly enforcing local spatial consistency in order to preserve fine image details and structures. The patch database may be created from a multi-patient set of high-SNR lung scans. Alternatively, the first scan, acquired at high-SNR right before the needle insertion, can provide a convenient patient-specific patch database.
The proposed algorithm is compared to state-of-the-art denoising algorithms for a dataset of 43 real CT-guided biopsy scans. Ultra-low-dose scans were simulated by synthetic noise addition to the sinogram, equivalent to a 96% reduction in radiation dose. The feature similarity score for the proposed algorithm outperformed the compared methods for all the scans in the dataset. The benefit of the patient-specific patch database over the multi-patient one is demonstrated in terms of recovered contrast for a tiny porcine lung nodule, following denoising with both approaches.
The proposed method provides a promising approach to the denoising of ultra-low-dose CT-guided biopsy images.
低剂量 CT 肺部筛查正成为现实，从而引发了更多的 CT 引导下肺活检。在此类活检中，患者需要接受多次重复的引导扫描，累积辐射剂量较大。因此，有必要将剂量减少扩展到活检过程中。我们提出了一种专门针对 CT 引导下肺活检的图像去噪算法。该算法在保持适当导航到目标病变的图像质量的同时，最大限度地减少辐射暴露。
使用高 SNR CT 补丁数据库在非局部均值框架中过滤噪声像素，同时明确执行局部空间一致性，以保留精细的图像细节和结构。补丁数据库可以从多患者高 SNR 肺部扫描集中创建。或者，在针插入前的高 SNR 采集的第一扫描可以提供一个方便的患者特定的补丁数据库。
将所提出的算法与用于 43 个真实 CT 引导活检扫描的数据集的最先进的去噪算法进行了比较。通过对正弦图添加合成噪声模拟超低剂量扫描，相当于辐射剂量减少 96%。在所研究的数据集的所有扫描中，对于该算法的特征相似性评分均优于比较方法。从两种方法的去噪后恢复的微小猪肺结节的对比度来看，患者特定的补丁数据库优于多患者的补丁数据库。
该方法为超低剂量 CT 引导活检图像的去噪提供了一种有前途的方法。
