Model-Based Regularisation for Respiratory Motion Estimation with Sparse Features in Image-Guided Interventions
Intra-interventional respiratory motion estimation has become vital for image-guided interventions, especially radiation therapy. While real-time tracking of highly discriminative landmarks like tumours and markers is possible with classic approaches (e.g. template matching), their robustness decreases when used with non-ionising imaging (4D MRI or US). Furthermore, they ignore the motion of neighbouring structures. We address these challenges by dividing the computation of dense deformable registration in two phases: First, a low-parametric full domain patient-specific motion model is learnt. Second, a sparse subset of feature locations is used to track motion locally, while the global motion patterns are constrained by the learnt model. In contrast to previous work, we optimise both objectives (local similarity and globally smooth motion) jointly using a coupled convex energy minimisation. This improves the tracking robustness and leads to a more accurate global motion estimation. The algorithm is computationally efficient and significantly outperforms classic template matching-based dense field estimation in 12 of 14 challenging 4D MRI and 4D ultrasound sequences.
This work is partially funded by the German Research Foundation DFG (HE 7364/1)
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