Fereshteh S. Bashiri Advisors: Zeyun Yu, Roshan M. D’souza

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Presentation transcript:

Partial to Full multi-modal medical image registration based on structural representation Fereshteh S. Bashiri Advisors: Zeyun Yu, Roshan M. D’souza College of Engineering and Applied Science, University of Wisconsin-Milwaukee April, 2017

Multi-Model image registration Challenges and a novel approach to the problem Manifold Learning Fixed Image Moving Image Manifold Alignment Registration Algorithm Spatial Transformation Parameters Construct Hi-D Space Figure 2. Multi-modal image registration pipeline Figure 1. Multi-modal images of brain and their variations in intensities, (left) CT, (Middle) T1-weighted MR and (right) T2-weighted MR

Laplacian Eigenmap for structural representaion Challenges and a novel approach to the problem Figure 1. Multi-modal images of brain and their variations in intensities, (left) CT, (Middle) T1-weighted MR and (right) T2-weighted MR Figure 3. Structural representation with Laplacian Eigenmap

Results (a) T1-Weighted (b) T2-Weighted (c) PD-Weighted (d) CT Scan Figure 4. (Top) Original CT/MR scans of human brain; (Bottom) Structural representation of same scans. (a) T1-Weighted (b) T2-Weighted (c) PD-Weighted (d) CT Scan Representation Structural Brain Scans Original

Results (a) CT-T1 (b) CT-T2 (c) CT-PD (d) CT-T2 (20°rot.) Figure 5. Pairwise display of CT/MR registration. (Top) Unregistered scans, (Middle) Conventional registration results, (Bottom) Registration with the proposed method. (a) CT-T1 (b) CT-T2 (c) CT-PD (d) CT-T2 (20°rot.) Original Scans Registered Scans (Conventional) (proposed method) Results

Metric: Mutual Information Fixed Image Moving Image MI (Raw data) (Conv. method) (Proposed method) P001 - CT T1 0.8607 1.1352 1.1721 T1_rectified 0.8529 1.1919 1.1972 T2 0.8060 1.0269 1.0476 T2_rectified 0.8615 1.0665 1.1362 PD 0.8946 1.1222 1.1653 T1_30Rot 0.7976 1.1698 1.1774 T2_30Rot 0.7388 1.0379 1.0634 P002 – CT 0.8819 1.0660 1.1741 0.8473 1.1582 1.2013 0.7687 1.0257 1.1250 0.8056 1.0826 1.1854 Fixed Image Moving Image MI (Raw data) (Conv. method) (Proposed method) P002 – CT PD 0.8276 1.1034 1.2407 T1_30Rot 0.8205 1.0728 1.1891 T2_30Rot 0.7148 1.0089 1.1298 P003 - CT T1 0.6865 1.0424 1.0581 T2 0.7108 1.0551 1.0698 T2_rectified 0.7372 1.1009 1.1291 0.6535 1.0445 1.0516 P003 – T1 1.0732 1.0747 1.0973 P101 – CT 0.9422 1.2251 1.3003 0.9304 1.1612 1.2460 0.9940 1.3090 1.3817 MI is based on the statistical relationship between both volumes to be registered. Mutual information (MI) has become the established intensity similarity measure in multimodal registration because it accommodates different intensities between the modalities provided that they are relatively consistent within each modality The mutual information of images A and B measures the degree of dependence of A and B as the distance between the joint distribution and the distribution associated to the case of complete independence MI estimates the statistical dependence between corresponding voxel intensities, which is assumed to be maximal when the images are correctly aligned

Results Full data: T2-Weighted MR Full data: P001- CT Scan Partial data: T1-Weighted MR Full data: P001- CT Scan Partial data: P001- T1-Weighted MR Figure 6. Pairwise display of multi-modal partial data registration. Each pair: (Left) Unregistered scans, (Right) Registered scans by employing proposed method.