A Learning-Based Metal Artifacts Correction Method for MRI Using Dual-Polarity Readout Gradients and Simulated Data. In: Frangi AF, Schnabel JA, Davatzikos C, Alberola-lópez C, Fichtinger G, editors. Medical Image Computing and Computer Assisted Intervention – MICCAI 2018. Cham: Springer International Publishing; 2018. pp. 189-97. [DOI: 10.1007/978-3-030-00928-1_22]

Inpainting the metal artifact region in MRI images by using generative adversarial networks with gated convolution

PURPOSE

Magnetic resonance imaging (MRI) plays an important role in clinical diagnosis, but it is susceptible to metal artifacts. The generative adversarial network GatedConv with gated convolution (GC) and contextual attention (CA) was used to inpaint the metal artifact region in MRI images.

METHODS

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. A total of 10,000 slices were obtained after data enhancement, of which 8,000 slices were used for training. MRI images were normalized to [-1,1]. 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. The mean absolute error (MAE) and peak signal-to-noise ratio (PSNR) for the mask were used to compare the results of these methods. The inpainting effect on the test data set using dental masks was also evaluated. Besides, the artifact area of clinical MRI images was inpainted based on the mask sketched by physicians. Finally, the earring artifacts and artifacts caused by abnormal signal foci were inpainted to verify the generalization of the models.

RESULTS

GatedConv could directly and effectively inpaint the incomplete MRI images generated by masks in the image domain. For the results of U-Net, pix2pix, PConv, and Gatedconv, the masked MAEs were 0.1638, 0.1812, 0.1688, and 0.1596, respectively, and the masked PSNRs were 18.2136, 17.5692, 18.2258, and 18.3035 dB, respectively. Using dental masks, the results of U-Net, pix2pix, and PConv differed more from the real images in terms of alveolar shape and surrounding tissue compared with GatedConv. GatedConv could inpaint the metal artifact region in clinical MRI images more effectively than the other models, but the increase in the mask area could reduce the inpainting effect. Inpainted MRI images by GatedConv and CT images with metal artifact reduction coincided with alveolar and tissue structure, and GatedConv could successfully inpaint artifacts caused by abnormal signal foci while the other models failed. The ablation study demonstrated that GC and CA increased the reliability of the inpainting performance of GatedConv.

CONCLUSION

MRI images are affected by metal, and signal void areas appear near metal. GatedConv can inpaint the MRI metal artifact region in the image domain directly and effectively and improve image quality. Medical image inpainting by GatedConv has potential value for tasks such as PET attenuation correction in PET/MRI and adaptive radiotherapy of synthetic CT based on MRI. This article is protected by copyright. All rights reserved.