Im Jessica Y, Halliburton Sandra S, Mei Kai, Perkins Amy E, Wong Eddy, Roshkovan Leonid, Gang Grace J, Noël Peter B
Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA.
Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12925. doi: 10.1117/12.3006547. Epub 2024 Apr 1.
Deep learning CT reconstruction (DLR) has become increasingly popular as a method for improving image quality and reducing radiation exposure. Due to their nonlinear nature, these algorithms result in resolution and noise performance which are object-dependent. Therefore, traditional CT phantoms, which lack realistic tissue morphology, have become inadequate for assessing clinical imaging performance. We propose to utilize 3D-printed PixelPrint phantoms, which exhibit lifelike attenuation profiles, textures, and structures, as a better tool for evaluating DLR performance. In this study, we evaluate a DLR algorithm (Precise Image (PI), Philips Healthcare) using a custom PixelPrint lung phantom and perform head-to-head comparisons between DLR, iterative reconstruction, and filtered back projection (FBP) with scans acquired at a broad range of radiation exposures (CTDI: 0.5, 1, 2, 4, 6, 9, 12, 15, 19, and 20 mGy). We compared the performance of each resultant image using noise, peak signal to noise ratio (PSNR), structural similarity index (SSIM), feature-based similarity index (FSIM), information theoretic-based statistic similarity measure (ISSM) and universal image quality index (UIQ). Iterative reconstruction at 9 mGy matches the image quality of FBP at 12 mGy (diagnostic reference level) for all metrics, demonstrating a dose reduction capability of 25%. Meanwhile, DLR matches the image quality of diagnostic reference level FBP images at doses between 4 - 9 mGy, demonstrating dose reduction capabilities between 25% and 67%. This study shows that DLR allows for reduced radiation dose compared to both FBP and iterative reconstruction without compromising image quality. Furthermore, PixelPrint phantoms offer more realistic testing conditions compared to traditional phantoms in the evaluation of novel CT technologies. This, in turn, promotes the translation of new technologies, such as DLR, into clinical practice.
深度学习CT重建（DLR）作为一种提高图像质量和减少辐射暴露的方法越来越受欢迎。由于这些算法具有非线性特性，其分辨率和噪声性能取决于对象。因此，缺乏逼真组织形态的传统CT体模已不足以评估临床成像性能。我们建议使用3D打印的PixelPrint体模，其具有逼真的衰减剖面、纹理和结构，作为评估DLR性能的更好工具。在本研究中，我们使用定制的PixelPrint肺部体模评估一种DLR算法（Precise Image（PI），飞利浦医疗保健公司），并在广泛的辐射暴露水平（CTDI：0.5、1、2、4、6、9、12、15、19和20 mGy）下进行扫描，对DLR、迭代重建和滤波反投影（FBP）进行直接比较。我们使用噪声、峰值信噪比（PSNR）、结构相似性指数（SSIM）、基于特征的相似性指数（FSIM）、基于信息论的统计相似性度量（ISSM）和通用图像质量指数（UIQ）比较每个所得图像的性能。对于所有指标，9 mGy的迭代重建与12 mGy（诊断参考水平）的FBP图像质量相匹配，显示出25%的剂量降低能力。同时，DLR在4 - 9 mGy的剂量下与诊断参考水平FBP图像的质量相匹配，显示出25%至67%的剂量降低能力。本研究表明，与FBP和迭代重建相比，DLR在不影响图像质量的情况下可降低辐射剂量。此外，在评估新型CT技术时，与传统体模相比，PixelPrint体模提供了更逼真的测试条件。这反过来又促进了DLR等新技术向临床实践的转化。
