MAUDGAN: Motion Artifact Unsupervised Disentanglement Generative Adversarial Network of Multicenter MRI Data with Different Brain tumors

Purpose This study proposed a novel retrospective motion reduction method named motion artifact unsupervised disentanglement generative adversarial network (MAUDGAN) that reduces the motion artifacts from brain images with tumors and metastases. The MAUDGAN was trained using a mutlimodal multicenter 3D T1-Gd and T2-fluid attenuated inversion recovery MRI images. Approach The motion artifact with different artifact levels were simulated in k -space for the 3D T1-Gd MRI images. The MAUDGAN consisted of two generators, two discriminators and two feature extractor networks constructed using the residual blocks. The generators map the images from content space to artifact space and vice-versa. On the other hand, the discriminators attempted to discriminate the content codes to learn the motion-free and motion-corrupted content spaces. Results We compared the MAUDGAN with the CycleGAN and Pix2pix-GAN. Qualitatively, the MAUDGAN could remove the motion with the highest level of soft-tissue contrasts without adding spatial and frequency distortions. Quantitatively, we reported six metrics including normalized mean squared error (NMSE), structural similarity index (SSIM), multi-scale structural similarity index (MS-SSIM), peak signal-to-noise ratio (PSNR), visual information fidelity (VIF), and multi-scale gradient magnitude similarity deviation (MS-GMSD). The MAUDGAN got the lowest NMSE and MS-GMSD. On average, the proposed MAUDGAN reconstructed motion-free images with the highest SSIM, PSNR, and VIF values and comparable MS-SSIM values. Conclusions The MAUDGAN can disentangle motion artifacts from the 3D T1-Gd dataset under a multimodal framework. The motion reduction will improve automatic and manual post-processing algorithms including auto-segmentations, registrations, and contouring for guided therapies such as radiotherapy and surgery. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement NSERC CREATE RHHDS program and NSERC discovery grant. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The Cancer Imaging Archieve I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes All data produced are available online at