1. 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

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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.