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Deep Learning-Based Rigid-Body Motion Correction in MRI using Multichannel Data
Miriam Hewlett1,2, Ivailo E Petrov2, and Maria Drangova1,2
1Medical Biophysics, Western University, London, ON, Canada, 2Robarts Research Institute, London, ON, Canada
Motion correction in single-channel images prior to coil combination improved performance compared to motion correction on coil-combined images. Simultaneous motion correction of multichannel data produced the worst result, likely a result of the model's limited modelling capacity.
Figure 3. Mean absolute error (MAE, mean and standard deviation) and structural similarity index (SSIM, mean and standard deviation) comparing the ground truth results to the uncorrected images (black), as well as images corrected with the combined (yellow), single-channel (blue), and multichannel (red) models. All differences are significant (p < 0.05).
Figure 4. Example images for each contrast; T2-weighted (top), T1-weighted (middle), and FLAIR (bottom). On the left are the ground truth images, and on the right those containing simulated motion artefacts. The centre three images are those corrected with the combined, single-channel, and multichannel models (from left to right).