Department of Radiology, Charité-Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany.
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, 1095 Jiefang Avenue, Wuhan, 430030, Hubei Province, China.
Eur Radiol Exp. 2024 Jul 24;8(1):84. doi: 10.1186/s41747-024-00486-6.
BACKGROUND: Computed tomography (CT) reconstruction algorithms can improve image quality, especially deep learning reconstruction (DLR). We compared DLR, iterative reconstruction (IR), and filtered back projection (FBP) for lesion detection in neck CT. METHODS: Nine patient-mimicking neck phantoms were examined with a 320-slice scanner at six doses: 0.5, 1, 1.6, 2.1, 3.1, and 5.2 mGy. Each of eight phantoms contained one circular lesion (diameter 1 cm; contrast -30 HU to the background) in the parapharyngeal space; one phantom had no lesions. Reconstruction was made using FBP, IR, and DLR. Thirteen readers were tasked with identifying and localizing lesions in 32 images with a lesion and 20 without lesions for each dose and reconstruction algorithm. Receiver operating characteristic (ROC) and localization ROC (LROC) analysis were performed. RESULTS: DLR improved lesion detection with ROC area under the curve (AUC) 0.724 ± 0.023 (mean ± standard error of the mean) using DLR versus 0.696 ± 0.021 using IR (p = 0.037) and 0.671 ± 0.023 using FBP (p < 0.001). Likewise, DLR improved lesion localization, with LROC AUC 0.407 ± 0.039 versus 0.338 ± 0.041 using IR (p = 0.002) and 0.313 ± 0.044 using FBP (p < 0.001). Dose reduction to 0.5 mGy compromised lesion detection in FBP-reconstructed images compared to doses ≥ 2.1 mGy (p ≤ 0.024), while no effect was observed with DLR or IR (p ≥ 0.058). CONCLUSION: DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used. RELEVANCE STATEMENT: Deep learning enhances lesion detection in neck CT imaging compared to iterative reconstruction and filtered back projection, offering improved diagnostic performance and potential for x-ray dose reduction. KEY POINTS: Low-contrast lesion detectability was assessed in anatomically realistic neck CT phantoms. Deep learning reconstruction (DLR) outperformed filtered back projection and iterative reconstruction. Dose has little impact on lesion detectability against anatomical background structures.
背景：计算机断层扫描（CT）重建算法可以改善图像质量，尤其是深度学习重建（DLR）。我们比较了 DLR、迭代重建（IR）和滤波反投影（FBP）在颈部 CT 中对病变检测的效果。
方法：使用 320 层扫描仪对 9 个患者模拟的颈部体模进行检查，剂量分别为 0.5、1、1.6、2.1、3.1 和 5.2 mGy。每个体模的咽旁间隙内均有一个圆形病变（直径 1cm；与背景的对比 -30 HU）；一个体模无病变。使用 FBP、IR 和 DLR 进行重建。对于每个剂量和重建算法，13 名读者需要在 32 张有病变和 20 张无病变的图像中识别和定位病变。进行了受试者工作特征（ROC）和定位 ROC（LROC）分析。
结果：与 IR（p=0.037）和 FBP（p<0.001）相比，DLR 提高了病变检测的 ROC 曲线下面积（AUC），分别为 0.724±0.023（均值±标准误差）和 0.696±0.021。同样，与 IR（p=0.002）和 FBP（p<0.001）相比，DLR 提高了病变定位的 LROC AUC，分别为 0.407±0.039 和 0.313±0.044。与剂量≥2.1 mGy 相比，剂量降低至 0.5 mGy 会降低 FBP 重建图像中病变的检测能力（p≤0.024），而 DLR 或 IR 则没有这种影响（p≥0.058）。
结论：与迭代重建和滤波反投影相比，深度学习在颈部 CT 成像中提高了病变的检测能力。当使用降噪重建时，将剂量降低至 0.5 mGy 可以保持病变的检测能力。
相关性声明：与迭代重建和滤波反投影相比，深度学习增强了颈部 CT 成像中的病变检测，提供了更好的诊断性能和潜在的 X 射线剂量降低。
关键点：在解剖上逼真的颈部 CT 体模中评估低对比度病变的检测能力。深度学习重建（DLR）优于滤波反投影和迭代重建。剂量对病变与解剖背景结构的检测能力影响不大。
Ther Innov Regul Sci. 2021-11
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