Finally, the earring artifacts and artifacts caused by abnormal signal foci were inpainted to verify the generalization of the models.
Besides, the artifact area of clinical MRI images was inpainted based on the mask sketched by physicians. The inpainting effect on the test dataset using dental masks was also evaluated.
The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. Based on the randomly generated mask, U-Net, pix2pix, PConv with partial convolution, and GatedConv were used to inpaint the artifact region of MRI images. A total of 10 000 slices were obtained after data enhancement, of which 8000 slices were used for training. MRI images containing or near the teeth of 70 patients were collected, and the scanning sequence was a T1-weighted high-resolution isotropic volume examination sequence. The generative adversarial network GatedConv with gated convolution (GC) and contextual attention (CA) was used to inpaint the metal artifact region in MRI images. Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, but it is susceptible to metal artifacts.
0 kommentar(er)
