Abstract
Objective We applied computed tomography (CT) to explore the imaging manifestations of rare parts of osteochondroma. Based on the medical images, deblurring using a convolutional neural network (CNN), and three-dimensional (3D) reconstruction of the images is performed in order to improve the image diagnosis. Methods Twelve cases of osteochondroma in rare locations confirmed by surgical pathology or clinical long-term dynamic observation were retrospectively analyzed using medical imaging and image reconstruction. There are 7 males and 5 females, with an average age of 43 years. CT examinations were performed in all cases. Image deblurring via the GAN model is performed followed by the 3D reconstruction of the higher quality images is implemented. A retrospective study was performed on the imaging manifestations of the above cases; the imaging characteristics were summarized. Results The imaging features are the following lesions, including 4 cases of the proximal radius, 4 cases of the scapula, 2 cases of the pelvis, and 2 cases of the proximal ribs. The cartilage caps, cortex, and sternum were typical structures of the bone surface of the studied cases. In the continuous imaging features, calcification was visible in some cases, and no significant enhancement was seen in enhanced scans; there was no obvious direction of lesion growth. The image processing techniques that we performed are useful in enhancing the quality of the medical diagnosis. Conclusions Rare site osteochondroma has certain imaging features. In most cases, we can accurately diagnose rare site osteochondroma through these features via the image processing methods that are proposed in this paper.
Highlights
At present, the research in the field of image deblurring in the medical field mainly includes the medical image processing of data sets following the implementation of deblurring algorithms
In order to verify the effectiveness of the model, this paper explores the performance of the image motion blur removal model proposed in this paper from three aspects: visual effect, peak signal-to-noise ratio, and training time
On the left is the original blurred image in the data set, and on the right is the clear image generated after the model in this article. It can be seen from the experimental visual effects that the model proposed in this paper achieves a clear resolution of the visual effect of removing the blur of medical image images, and the calculation time of each step during training is increased by 0.04 seconds, and the test time is reduced by 0.04 seconds
Summary
The research in the field of image deblurring in the medical field mainly includes the medical image processing of data sets following the implementation of deblurring algorithms. Compared with other image degradation and restoration problems, it is more difficult to obtain a data set for medical image deblurring, because it is difficult to capture a pair of clear images with exactly the same factors in a real lesion map, even if two consecutive images are taken. These two images will not correspond exactly due to changes in some factors, so it is almost impossible to obtain a pair of clear and blurred images with exactly the same content. Osteochondroma has no pain or tenderness and produces corresponding symptoms when the nerve is compressed
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