Posts Tagged: MRI-guided interventions

Respiratory motion is known to be an important problem in non-invasive image-guided tumor interventions that needs to be accounted for to achieve an accurate treatment delivery. To this end, we propose a novel motion estimation method that estimates dense motion fields in realtime for the entire treatment region based on intra-interventional image data acquired by state-of-the-art treatment systems like MRI-Linacs. Our method achieves state-of-the-art tracking accuracy (≈ 1 mm) at high frame rates by combining GPU-accelerated sparse feature point matching and patient-specific regularisation using a learned PCA-based motion model in a unified registration framework.