PDF(Pubmed)
Abstract：
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).
CONCLUSIONS: DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
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).
CONCLUSIONS: DLR improved the detectability of lesions in neck CT imaging. Dose reduction to 0.5 mGy maintained lesion detectability when denoising reconstruction was used.
CONCLUSIONS: 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.
CONCLUSIONS: 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.
摘要：
背景：计算机断层扫描（CT）重建算法可以提高图像质量，特别是深度学习重建(DLR)。我们比较了DLR,迭代重建(IR)，和过滤反投影（FBP）用于颈部CT中的病变检测。
方法：用320层扫描仪以六种剂量检查9个患者模仿颈部体模：0.5、1、1.6、2.1、3.1和5.2mGy。八个体模中的每个体模在咽旁间隙中都包含一个圆形病变（直径1cm；与背景的对比度-30HU）；一个体模没有病变。重建是用FBP进行的，IR,和DLR。对于每种剂量和重建算法，13位读者的任务是识别和定位32张具有病变和20张没有病变的图像中的病变。进行接收器工作特性（ROC）和定位ROC（LROC）分析。
结果：DLR改善了病变检测，使用DLR的ROC曲线下面积（AUC）0.724±0.023（平均值±平均值的标准误差）与使用IR的0.696±0.021（p=0.037）和使用FBP的0.671±0.023（p<0.001）。同样，DLR改善病变定位，LROCAUC为0.407±0.039，IR为0.338±0.041（p=0.002），FBP为0.313±0.044（p<0.001）。与剂量≥2.1mGy（p≤0.024）相比，FBP重建图像中的剂量减少至0.5mGy损害了病变检测，而DLR或IR没有观察到影响（p≥0.058）。
结论：DLR提高了颈部CT成像中病变的可检测性。当使用去噪重建时,将剂量减少至0.5mGy保持病变可检测性。
结论：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，提供改进的诊断性能和X射线剂量减少的潜力。
结论：在解剖逼真的颈部CT体模中评估了低对比度病变的可检测性。深度学习重建（DLR）优于滤波反投影和迭代重建。剂量对解剖背景结构的病变可检测性几乎没有影响。
方法：用320层扫描仪以六种剂量检查9个患者模仿颈部体模：0.5、1、1.6、2.1、3.1和5.2mGy。八个体模中的每个体模在咽旁间隙中都包含一个圆形病变（直径1cm；与背景的对比度-30HU）；一个体模没有病变。重建是用FBP进行的，IR,和DLR。对于每种剂量和重建算法，13位读者的任务是识别和定位32张具有病变和20张没有病变的图像中的病变。进行接收器工作特性（ROC）和定位ROC（LROC）分析。
结果：DLR改善了病变检测，使用DLR的ROC曲线下面积（AUC）0.724±0.023（平均值±平均值的标准误差）与使用IR的0.696±0.021（p=0.037）和使用FBP的0.671±0.023（p<0.001）。同样，DLR改善病变定位，LROCAUC为0.407±0.039，IR为0.338±0.041（p=0.002），FBP为0.313±0.044（p<0.001）。与剂量≥2.1mGy（p≤0.024）相比，FBP重建图像中的剂量减少至0.5mGy损害了病变检测，而DLR或IR没有观察到影响（p≥0.058）。
结论：DLR提高了颈部CT成像中病变的可检测性。当使用去噪重建时,将剂量减少至0.5mGy保持病变可检测性。
结论：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，提供改进的诊断性能和X射线剂量减少的潜力。
结论：在解剖逼真的颈部CT体模中评估了低对比度病变的可检测性。深度学习重建（DLR）优于滤波反投影和迭代重建。剂量对解剖背景结构的病变可检测性几乎没有影响。
