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Model-Based Registration of X-ray Mammograms and MR Images of the Female Breast

This research presents a model-based registration approach to locate lesions in X-ray mammograms and MR images, aiding in multimodal breast cancer diagnosis. By incorporating a biomechanical model to address issues like large deformation, the method achieves successful localization with approximately 5mm deviation, as demonstrated in clinical evaluations.

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Model-Based Registration of X-ray Mammograms and MR Images of the Female Breast

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

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

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

  5. Skin Fatty tissue Glandular tissue L‘ 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

  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 1st step: Mammographic deformation 2nd step: Fine tuning using mammogram Mammographicdeformation

  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 • Prediction of lesion position • MRI  X-ray: • Mean center distance: 4.3 mm (2.3 – 6 mm) • Mean area overlap: 81% • X-ray MRI: • Mean center distance: 3.9 mm (1.6 – 6.4 mm) • Mean volume overlap: 91%

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

  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 !

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