■图像引导放射治疗(IGRT)中的锥形束计算机断层扫描(CBCT)的成像剂量对患者健康造成不利影响。为了提高稀疏视角低剂量CBCT图像的质量,提出了一种投影合成卷积神经网络(SynCNN)模型。
■包含在此回顾展中,单中心研究纳入了北京肿瘤医院诊断为脑肿瘤的223例患者。所提出的SynCNN模型估计了两对正交方向可分离的空间内核,以通过局部卷积运算合成输入相邻稀疏视图投影之间的缺失投影。SynCNN模型对150名真实患者进行了训练,以学习视图间投影合成的模式。来自30名真实患者的CBCT数据用于验证SynCNN,而来自体模和43名真实患者的数据被用于外部测试SynCNN。模拟了具有原始采样率的1/2、1/4和1/8的稀疏视图投影数据集,并使用SynCNN模型恢复相应的全视图投影数据集。然后用Feldkamp-Davis-Kress算法重建断层图像。均方根误差(RMSE),峰值信噪比(PSNR),和结构相似性(SSIM)指标在投影和图像域进行了测量。邀请五名专家对40个随机选择的具有四级规则的评估组进行图像质量的盲目评级,其中大于或等于2的分数被认为是可接受的图像质量。记录SynCNN模型的运行时间。在1/4稀疏视图重建上,将SynCNN模型直接与其他三种方法进行比较。
■体模和患者研究表明,缺失的投影是准确合成的。在图像域中,对于幻影研究,与从稀疏视图投影重建的图像相比,具有SynCNN合成的图像显示出显着改善的质量,RMSE值降低,PSNR和SSIM值增加。对于患者研究,在有和没有SynCNN合成的结果之间,平均RMSE分别下降3.4×10-4、10.3×10-4和21.7×10-4,平均PSNR分别上升3.4、6.6和9.4dB,对于1/2、1/4和1/8稀疏视图重建,平均SSIM增加了5.2×10-2、18.9×10-2和33.9×10-2,分别。在专家主观评价中,SynCNN合成图像的中位数得分和接受率均高于稀疏视图投影重建图像.该模型用不到0.01s的时间来合成视图间投影。与其他三种方法相比,SynCNN模型在这两个领域的三个指标方面都获得了最好的分数。
■所提出的SynCNN模型以较低的时间成本有效地提高了稀疏视图CBCT图像的质量。有了SynCNN模型,IGRT的CBCT成像剂量可能会降低。
UNASSIGNED: The imaging dose of cone-beam computed tomography (CBCT) in image-guided radiotherapy (IGRT) poses adverse effects on patient health. To improve the quality of sparse-view low-dose CBCT images, a projection synthesis convolutional neural network (SynCNN) model is proposed.
UNASSIGNED: Included in this retrospective, single-center study were 223 patients diagnosed with brain tumours from Beijing Cancer Hospital. The proposed SynCNN model estimated two pairs of orthogonally direction-separable spatial kernels to synthesize the missing projection in between the input neighboring sparse-view projections via local convolution operations. The SynCNN model was trained on 150 real patients to learn patterns for inter-view projection synthesis. CBCT data from 30 real patients were used to validate the SynCNN, while data from a phantom and 43 real patients were used to test the SynCNN externally. Sparse-view projection datasets with 1/2, 1/4, and 1/8 of the original sampling rate were simulated, and the corresponding full-view projection datasets were restored using the SynCNN model. The tomographic images were then reconstructed with the Feldkamp-Davis-Kress algorithm. The root-mean-square error (RMSE), peak signal-to-noise ratio (PSNR), and structural similarity (SSIM) metrics were measured in both the projection and image domains. Five experts were invited to grade the image quality blindly for 40 randomly selected evaluation groups with a four-level rubric, where a score greater than or equal to 2 was considered acceptable image quality. The running time of the SynCNN model was recorded. The SynCNN model was directly compared with the three other methods on 1/4 sparse-view reconstructions.
UNASSIGNED: The phantom and patient studies showed that the missing projections were accurately synthesized. In the image domain, for the phantom study, compared with images reconstructed from sparse-view projections, images with SynCNN synthesis exhibited significantly improved qualities with decreased values in RMSE and increased values in PSNR and SSIM. For the patient study, between the results with and without the SynCNN synthesis, the averaged RMSE decreased by 3.4×10-4, 10.3×10-4, and 21.7×10-4, the averaged PSNR increased by 3.4, 6.6, and 9.4 dB, and the averaged SSIM increased by 5.2×10-2, 18.9×10-2 and 33.9×10-2, for the 1/2, 1/4, and 1/8 sparse-view reconstructions, respectively. In expert subjective evaluation, both the median scores and acceptance rates of the images with SynCNN synthesis were higher than those reconstructed from sparse-view projections. It took the model less than 0.01 s to synthesize an inter-view projection. Compared with the three other methods, the SynCNN model obtained the best scores in terms of the three metrics in both domains.
UNASSIGNED: The proposed SynCNN model effectively improves the quality of sparse-view CBCT images at a low time cost. With the SynCNN model, the CBCT imaging dose in IGRT could be reduced potentially.