Duke University Health System, Department of Radiology, 2301 Erwin Road, Box 3808, Durham, NC, 27710, United States.
Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, NC, 27710, United States.
Eur J Radiol. 2021 Jun;139:109734. doi: 10.1016/j.ejrad.2021.109734. Epub 2021 Apr 24.
Dual-source (DS) CT, dual-energy (DE) field of view (FoV) is limited to the size of the smaller detector array. The purpose was to establish a deep learning-based approach to DE extrapolation by estimating missing image data using data from both tubes to evaluate renal lesions.
A DE extrapolation deep-learning (DEEDL) algorithm had been trained on DECT data of 50 patients using a DSCT with DE-FoV = 33 cm (Somatom Flash). Data from 128 patients with known renal lesions falling within DE-FoV was retrospectively collected (100/140 kVp; reference dataset 1). A smaller DE-FoV = 20 cm was simulated excluding the renal lesion of interest (dataset 2) and the DEEDL was applied to this dataset. Output from the DEEDL algorithm was evaluated using ReconCT v14.1 and Syngo.via. Mean attenuation values in lesions on mixed images (HU) were compared calculating the root-mean-squared-error (RMSE) between the datasets using MATLAB R2019a.
The DEEDL algorithm performed well reproducing the image data of the kidney lesions (Bosniak 1 and 2: 125, Bosniak 2F: 6, Bosniak 3: 1 and Bosniak 4/(partially) solid: 32) with RSME values of 10.59 HU, 15.7 HU for attenuation, virtual non-contrast, respectively. The measurements performed in dataset 1 and 2 showed strong correlation with linear regression (r: attenuation = 0.89, VNC = 0.63, iodine = 0.75), lesions were classified as enhancing with an accuracy of 0.91.
This DEEDL algorithm can be used to reconstruct a full dual-energy FoV from restricted data, enabling reliable HU value measurements in areas not covered by the smaller FoV and evaluation of renal lesions.
双源（DS）CT 的双能量（DE）视野（FoV）仅限于较小探测器阵列的尺寸。目的是建立一种基于深度学习的 DE 外推方法，通过使用来自两个管的数据来估计缺失的图像数据，从而评估肾脏病变。
在使用具有 DE-FoV=33cm（Somatom Flash）的 DSCT 对 50 名患者的 DECT 数据进行训练后，开发了一种 DE 外推深度学习（DEEDL）算法。回顾性收集了 128 名已知有肾脏病变且位于 DE-FoV 内的患者的数据（100/140kVp；参考数据集 1）。排除感兴趣的肾脏病变模拟较小的 DE-FoV=20cm（数据集 2）并应用 DEEDL 算法。使用 ReconCT v14.1 和 Syngo.via 评估 DEEDL 算法的输出。使用 MATLAB R2019a 计算数据集之间的均方根误差（RMSE），比较混合图像上病变的平均衰减值（HU）。
DEEDL 算法在复制肾脏病变的图像数据方面表现良好（Bosniak 1 和 2：125，Bosniak 2F：6，Bosniak 3：1 和 Bosniak 4/（部分）实体：32），RMSE 值分别为 10.59HU、15.7HU 用于衰减、虚拟非对比。在数据集 1 和 2 中进行的测量与线性回归具有很强的相关性（r：衰减=0.89，VNC=0.63，碘=0.75），病变的分类为增强，准确率为 0.91。
该 DEEDL 算法可用于从受限数据重建全双能 FoV，从而能够在较小 FoV 未覆盖的区域可靠地测量 HU 值并评估肾脏病变。
