PDF(Pubmed)
Abstract：
Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels.Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image.Results.DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%-83% in the small phantom and by 50%-83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR.Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments.
摘要：
目标
深度学习重建(DLR)算法表现出与对象相关的分辨率和噪声性能。因此，传统的几何CT体模不能完全捕获DLR的临床影像学表现。这项研究使用患者衍生的3D打印PixelPrint肺部模型来评估各种辐射剂量水平的商业DLR算法。＆＃xD;＆＃xD;方法＆＃xD;本研究中使用的肺部体模是基于包含毛玻璃混浊的患者胸部CT扫描，并使用PixelPrint3D打印技术制造的。将体模放置在两个不同尺寸的延伸环内以模仿中小型患者，并在常规CT扫描仪上以0.5至20mGy的曝光量进行扫描。使用滤波反投影（FBP）重建每个扫描，迭代重建，和DLR在五个级别的去噪。图像噪声,对比度噪声比(CNR)，均方根误差(RMSE)，结构相似性指数(SSIM)，并计算每幅图像的多尺度SSIM(MSSSIM)。
结果
与FBP和迭代重建相比，DLR在两种体模尺寸下的所有测量指标都表现出了卓越的性能，具有更好的性能，更积极的去噪水平。对于本研究中测量的任何指标，DLR估计在小体模中减少25-83％的剂量，在中等体模中减少50-83％的剂量，而不会降低图像质量。与仅考虑噪声和CNR时获得的估计相比，这些剂量减少估计更保守。＆＃xD;＆＃xD;结论＆＃xD;DLR具有在降低高达83％的辐射剂量下产生诊断图像质量的能力，可以提高低剂量CT扫描的临床实用性和可行性。此外，与传统的CT体模相比，本研究中使用的PixelPrint体模提供了改进的测试环境，具有更真实的组织结构，允许基于结构的图像质量评估超越噪声和基于对比度的评估。
