Quality analysis of non-contrast-enhanced CT images synthesized from contrast-enhanced CT images by deep learning model / 中华放射医学与防护杂志

ABSTRACT

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.