A Physiological Variability Inspired Spatial Regularization for Joint Rigid-Deformable Abdominal MR Image Registration

Deep Learning deformable Image Registration (DLIR) has exhibited valuable results for accurately analyzing different patients' medical images. However, the most common DLIR approaches produce deformations assuming high-quality rigid/affine pre-registered images and global regularization regardless of image content and admissible motion nature. To address these shortcomings, we present MANGO, a spatial regularization strategy to perform jointly rigid and deformable abdominal MRI DLIR. In particular, MANGO fixes translational and rotational deformation components resulting from a suboptimal rigid pre-registration. Furthermore, physiologically generated Deformation Vector Fields guarantee DL model predictions that capture the patients' physiological heterogeneity as much as possible. Compared to state-of-the-art iterative and DLIR methods, our solution leads up to 10% DSC improvements on inter-patient abdominal MR images and proves to preserve (i.e., no statistical difference) the same accuracy when removing the pre-registration step.