Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation 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.