Fast Intra- and Intermodal Deformable Registration Based on Local Subvolume Matching

1. Fast Intra- and Intermodal Deformable Registration Based on Local Subvolume Matching Matthias Söhn1, Verena Scheel2, Markus Alber1 (1) Radiooncological Clinic, Section for Biomedical Physics, University of Tübingen, Germany (2) Laboratory for Preclinical Imaging and Imaging Technology, Department of Radiology, University of Tübingen, Germany Forschungszentrum für Hochpräzisionsbetrahlung

2. Deformable Registration for Radiotherapy 4D-CT our approach… CT-ConeBeamCT Featurelet-based deformable registration CT-MRI Requirements & Challenges: • accuracy • fast • no or little user interaction • versatility

3. Algorithmic Implementation 1 Cover region of interest in reference image with regular 3D-grid of featurelets typical size: 1.5x1.5x1.5 cm

4. 2 Individual rigid registration of each featurelet reference image (exhale) target image (inhale) for each featurelet maximization of local normalized mutual information (NMI) allowing 3D-shifts within local search region  fast & parallelizable!

5. 2 regions with mismatched featurelets Individual rigid registration of each featurelet reference image (exhale) target image (inhale) for each featurelet maximization of local normalized mutual information (NMI) allowing 3D-shifts within local search region  fast & parallelizable!

6. 3 registered target featurelet local similarity measure field (NMI) reference featurelet accept position => shift to position with minimal local deformation energy => ? …shift to position with minimal local deformation energy within NMI-optimum => Automatic assessment of local registration quality

7. 3 Automatic assessment of local registration quality -- Result

8. 4 Relaxation Step: Iterative Minimization of Deformation Energy for mismatched Featurelets

9. 5 interpolation of shift vectors => continuous deformation field! target image, final featurelet positions B-Spline Interpolation of Featurelet shift vectors

10. Results • RCCT Inhale-Exhale deformable registration: Visual evaluation before… …after registration

11. Results • CT-ConeBeamCT deformable registration: Visual evaluation before… …after registration Elekta XVI ConeBeam-CT data, courtesy D. Yan, Y. Chi (Beaumont)

12. Results • CT-MRI deformable registration: Visual evaluation CT MRI MRI (backtransformed) before… …after registration

13. Results • Quantitative evaluation: Anatomical landmarks N=55 landmarks altogether marked in inhale and exhale CTs of 4 patients [Siemens Somatom Sensation Open RCCT datasets @ 1x1x3mm voxelsize]

14. Results Residuals of featurelet algorithm based on thorax phantom: before: 2.9±2.8mm after: 1.1±1.2mm [based on ~740.000 voxels] • Quantitative evaluation: Virtual phantom Virtual thorax phantom: known deformation field used to to deform real lung CT dataset courtesy D. Yan, Y. Chi (Beaumont Hospital)

15. Results • Computational performance • calculation time mainly depends on… • size of registered region • size of local search region • featurelet size

16. Results • Computational performance “online” deformable registration!

17. Conclusions Featurelet-based deformable registration: • fast, parallizable • model-independent, fully automatic • enables multi-modality registration due to use of mutual information • sub-voxel registration accuracyas shown by landmark-based evaluation and virtual thorax phantom ‘online’ multi-modality deformable registration within reach!