通过深度学习模型从增强CT图像合成平扫CT图像的质量分析

目的通过基于卷积神经网络深度学习方法从增强CT合成平扫CT图像, 临床主观和客观评估合成平扫CT图像(DL-SNCT)与金标准平扫CT图像的相似性, 探讨其潜在临床价值。方法同时行常规平扫和增强CT扫描的患者34例, 通过深度学习模型将增强CT图像合成DL-SNCT图像, 以平扫CT图像为金标准, 主观评价DL-SNCT的图像质量(评价指标包括解剖结构清晰度、伪影、噪声、图像结构完整性、图像变形, 均采用4分制);利用配对t检验比较DL-SNCT与金标准平扫CT图像不同血供特点的解剖部位(主动脉、肾脏、肝实质、臀大肌)以及不同强化模式的肝脏病变(肝癌、肝血管瘤、肝转移瘤、肝囊肿)的CT值。结果主观评价上, DL-SNCT图像在伪影、噪声、图像结构完整性、图像变形方面评分都达到4分, 与平扫CT图像评分相一致(P>0.05);在解剖结构清晰度方面评分略低于平扫CT图像(3.59±0.70)分vs. 4分)], 差异有统计学意义(Z=-2.89, P <0.05)。对于不同解剖部位而言, DL-SNCT图像主动脉、肾脏的CT值显著高于平扫CT图像(t=-12.89、-9.58...

Quality analysis of non-contrast-enhanced CT images synthesized from contrast-enhanced CT images by deep learning model

Objective To synthesize non-contrast-enhanced CT images from enhanced CT images using deep learning method based on convolutional neural network, and to evaluate the similarity between synthesized non-contrast-enhanced CT images by deep learning(DL-SNCT) and plain CT images considered as gold standard subjectively and objectively, as well as to explore their potential clinical value.Methods Thirty-four patients who underwent conventional plain scan and enhanced CT scan at the same time were enrolled. Using deep learning model, DL-SNCT images were generated from the enhanced CT images for each patient. With plain CT images as gold standard, the image quality of DL-SNCT images was evaluated subjectively. The evaluation indices included anatomical structure clarity, artifacts, noise level, image structure integrity and image deformation using a 4-point system). Paired t-test was used to compare the difference in CT values of different anatomical parts with different hemodynamics (aorta, kidney, liver parenchyma, gluteus maximus) and different liver diseases with distinct enhancement patterns (liver cancer, liver hemangioma, liver metastasis and liver cyst) between DL-SNCT images and plain CT images.Results In subjective evaluation, the average scores of DL-SNCT images in artifact, noise, image structure integrity and image distortion were all 4 points, which were consistent with those of plain CT images (P>0.05). However, the average score of anatomical clarity was slightly lower than that of plain CT images (3.59±0.70 vs. 4) with significant difference (Z=-2.89, P<0.05). For different anatomical parts, the CT values of aorta and kidney in DL-SNCT images were significantly higher than those in plain CT images (t=-12.89,-9.58, P<0.05). There was no statistical difference in the CT values of liver parenchyma and gluteus maximus between DL-SNCT images and plain CT images (P>0.05). For liver lesions with different enhancement patterns, the CT values of liver cancer, liver hemangioma and liver metastasis in DL-SNCT images were significantly higher than those in plain CT images(t=-10.84, -3.42, -3.98,P<0.05). There was no statistical difference in the CT values of liver cysts between DL-SNCT iamges and plain CT images (P>0.05).Conclusions The DL-SNCT image quality as well as the CT values of some anatomical structures with simple enhancement patterns is comparable to those of plain CT images considered as gold-standard. For those anatomical structures with variable enhancement and those liver lesions with complex enhancement patterns, there is still vast space for DL-SNCT images to be improved before it can be readily used in clinical practice.