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Magnetic resonance imaging (MRI) is a widely used modality for visualizing patient anatomy due to its non-invasiveness and superior soft tissue contrast. MRI can be used to quantify anatomical changes before and after therapy to assess treatment outcome and efficacy. However, longitudinal tracking depends on the accurate alignment of multiple image sets, which is challenged by rigid and deformable displacements. Deformable image registration is a promising tool to account for these changes and enable accurate longitudinal assessments. In this study, we aim to develop a deep learning-based method for automatic deformable registration to align post-treatment and pre-treatment head and neck (HN) MRIs. Our proposed method, named dual-feasible framework, is implemented by a mutual network that consists of a forward module and a backward module. The two modules alternate in generating a deformation vector field (DVF) for image registration. First the pre-treatment MRI is registered to the post-treatment MRI and then the post-treatment MRI is registered to the pre-treatment MRI, and the process repeats under a mutual enhancing strategy. Dual feasible loss is used to optimize the mutual network. We conducted longitudinal experiments on 4 public patient datasets (40 MRI scans), each with 2 head and neck (HN) MRI sequences (T1-weighted and T2-weighted) across 5 timepoints: one pre-treatment MRI and four posttreatment MRIs. To evaluate the proposed method, the pre-treatment MRIs were used as the target, and we calculated the peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM) and mean absolute error (MAE) between the deformed post-treatment MRI and the pre-treatment MRI. The PSNR, SSIM and MAE are 29.3±0.3 dB, 0.89±0.02 and 52.4±2.7 for the T1-weighted MRI, and are 27.0±0.8 dB, 0.87±0.03 and 97.5±14.1 for the T2-weighted MRI. These results demonstrate the feasibility and efficacy of our proposed method for MRI deformable image registration.
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