1. Model-Based Registration of X-ray Mammograms and MR Images of the Female Breast N.V. Ruiter, T.O. Müller, R. Stotzka, H. Gemmeke, Forschungszentrum Karlsruhe, Germany

2. Motivation Locate lesion in complementary modality MR image X-ray mammogram Registration: Find geometric correspondence Support multimodal breast cancer diagnosis:

3. Registration Problem MR image: Volume image Undeformed breast Prone position X-ray mammography: 2D projection Large deformation Only one projection per deformation Images not directly comparable ! Add information using a biomechanical model

4. X-ray mammogram MR image Process of Registration Deformation process Biomechanical modelDeformed model Projection of artificial MR image

5. Problems of Deformation Model Large deformation of soft tissue Details of deformation process unknown: Exact patient position 3D shape of breast Thickness of deformed breast Compression force Tissues of the breast: Only large scale structures resolved in MR image Material models in literature: Inconclusive Not plausible for large deformation Plate compression L Fatty tissue Glandular tissue Skin

6. Specification of Deformation Model Adapt global parameters: Projection angle by non-linear scaling Volume is preserved estimate thickness Finite Element Model Large deformations (>>5%), (nearly) incompressible materials Material model Evaluation of models for breast tissue: Neo Hookean model, homogeneous tissues Two step modeling of deformation 1 st step: Mammographic deformation 2 nd step: Fine tuning using mammogram Mammographic deformation

7. Results with Clinical Datasets Six clinical data sets: Lesion position in X-ray mammograms and MR image known Smallest visible lesions in MR images: 5 mm X-ray MRI : Mean center distance: 3.9 mm (1.6 – 6.4 mm) Mean volume overlap: 91% Prediction of lesion position MRI X-ray: Mean center distance: 4.3 mm (2.3 – 6 mm) Mean area overlap: 81%

8. Results with Clinical Datasets MRI X-ray : craniocaudal example X-ray mammogramDirect MR projection MR projection after simulation Center lesion distance 2.3 mm Lesion overlap 100% 24mm

9. Conclusions Registration overcomes 3D deformation Successful first evaluation: Localization with approx. 5 mm deviation (smallest visible lesion) Clinical evaluation Possible applications: Support multimodal breast cancer diagnosis (also alternative 3D modalities) Simulation of breast deformation

10. Thank you !

11. Results with Clinical Datasets X-ray MRI: Center lesion distance 4.6 mm

12. 3D Localization in MR Image Two simulations necessary: Obl. compressed breast Cc. compressed breast Oblique X-ray mammogram Cranio-caudal X-ray mammogram Undeformed breast frontal view

13. Integration Deformationsmodell in Registrierung MRT patientenspezifische Geometrie FE Modell I. Simulation: Mammographische Deformation Projektionswinkel und Länge 2D Registrierung Dickenänderung aus Volumenerhaltung Registrierungskomponenten I FE Modell mit Platten D MRTFE Modell

14. Registrierungskomponenten II II.Simulation: Feinabstimmung Mammogramm 1.Ergebnis der I.Simulation: Näherung 3D deformierte Brust 2.Vergleich Konturen: Schätzung 3D Brust 3.II.Simulation mit korrigierten Randbedingungen Mammo- gramm I.Simu- lation II.Simu- lation Individuell deformierte 3D Brust Projektion

15. Objective and Problems MR Image: 3D volume, undeformed breast Additional information by model of deformation Register X-ray mammograms and MR volumes Locate lesion in complementary modality X-ray mammography: 2 projections, 2D, hugely deformed breast Only 1 projection per deformation: Images not directly comparable !

16. Motivation Fuse X-ray mammograms and MR volume Predict lesion position in complementary modality MR volume X-ray mammogram Registration: Find geometric correspondence Support multimodal breast cancer diagnosis