OBJECTIVE: Intra-interventional respiratory motion estimation is becoming a vital component in modern radiation therapy delivery or high intensity focused ultrasound systems. The treatment quality could tremendously benefit from more accurate dose delivery using real-time motion tracking based on magnetic-resonance (MR) or ultrasound (US) imaging techniques. However, current practice often relies on indirect measurements of external breathing indicators, which has an inherently limited accuracy. In this work, we present a new approach that is applicable to challenging real-time capable imaging modalities like MR-Linac scanners and 3D-US by employing contrast-invariant feature descriptors. METHODS: We combine GPU-accelerated image-based realtime tracking of sparsely distributed feature points and a dense patient-specific motion-model for regularisation and sparse-to-dense interpolation within a unified optimization framework. RESULTS: We achieve highly accurate motion predictions with landmark errors of ≈ 1 mm for MRI (and ≈ 2 mm for US) and substantial improvements over classical template tracking strategies. CONCLUSION: Our technique can model physiological respiratory motion more realistically and deals particularly well with the sliding of lungs against the rib cage. SIGNIFICANCE: Our model-based sparse-to-dense image registration approach allows for accurate and realtime respiratory motion tracking in image-guided interventions.
OBJECTIVE: Intra-interventional respiratory motion estimation is becoming a vital component in modern radiation therapy delivery or high intensity focused ultrasound systems. The treatment quality could tremendously benefit from more accurate dose delivery using real-time motion tracking based on magnetic-resonance (MR) or ultrasound (US) imaging techniques. However, current practice often relies on indirect measurements of external breathing indicators, which has an inherently limited accuracy. In this work, we present a new approach that is applicable to challenging real-time capable imaging modalities like MR-Linac scanners and 3D-US by employing contrast-invariant feature descriptors. METHODS: We combine GPU-accelerated image-based realtime tracking of sparsely distributed feature points and a dense patient-specific motion-model for regularisation and sparse-to-dense interpolation within a unified optimization framework. RESULTS: We achieve highly accurate motion predictions with landmark errors of ≈ 1 mm for MRI (and ≈ 2 mm for US) and substantial improvements over classical template tracking strategies. CONCLUSION: Our technique can model physiological respiratory motion more realistically and deals particularly well with the sliding of lungs against the rib cage. SIGNIFICANCE: Our model-based sparse-to-dense image registration approach allows for accurate and realtime respiratory motion tracking in image-guided interventions.