Projects:RegistrationLibrary:RegLib C11

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Slicer Registration Library Exampe #11: Co-registration of two anatomic brain atlases for merging of structure labels

					LEGEND this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution this indicates the moving image that determines the registration transform. this indicates images that passively move into the reference space, i.e. they have the transform applied but do not contribute to the calculation of the transform.
Target Atlas T1	Target Atlas Labelmap		Moving Atlas T1	Moving Atlas Labelmap
1mm isotropic 256 x 256 x 256 PA	1mm isotropic 256 x 256 x 256 PA		0.9375 x 0.9375 x 1.5mm isotropic 256 x 256 x 159 PA	0.9375 x 0.9375 x 1.5mm isotropic 256 x 256 x 159 PA

Objective / Background

This is an example of inter-subject registration via surface matching. The structures of interest are a small subset of the entire image, hence registration is not driven by image intensities but rather two model surfaces derived from the labelmaps.

Keywords

MRI, brain, head, inter-subject, atlas to atlas, surface-based segmentation, thalamic nuclei

Input Data

• reference/fixed : T1w coronal, 1mm isotropic. Called A1_gray
• reference/fixed : labelmap , aligned with above. Called A1_label
• moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called A0_gray
• moving: labelmap , aligned with above. Called A0_label

Methods

1. Visualize & browse A0 data: determine label range of thalamic nuclei labels in A0_label: 500-526
2. Visualize & browse A1 data: determine label range of thalamus lables in A1_label: 10 and 49
3. Build label mask of thalamus for A0: threshold A0_label from 500 t0 526
4. Build label mask of thalamus for A1: change label 10 to 49; threshold A1_label from 49 to 49
5. Smooth A1 thalamus labelmap: Median Filter
6. Build thalamus surface model of A0
7. Build thalamus surface model of A1
8. run Python Surface ICP Registration , Settings:

• Landmark transform mode: Affine
• Mean Distance Mode: RMS
• Maximum Number of Iterations: 500
• Maximum Nmber of Landmarks: 200
• Start by matching centroids: yes
• maximum mean distance: 0.01
• Initial Transform: none
• Input Surface: A0_ThalamusModel
• Target Surface: A1_ThalamusModel
• Output Surface: none
• Output Transform: Xform_A0Affine_ICP

1. Apply Registration Xform to labelmap: Resample Scalar/Vector/DWI Volume module. Settings:

• Input Volume : A0_labels
• Reference Volume : A1_labels
• Output Volume : “Create New Volume”, rename to “A0_labels_aff”
• Transform Node: Xform_A0Affine_ICP
• Interpolation Type: “nn”

1. 1. Change datatype of new volume A0_labels_aff to labelmap (Volumes module)
2. Apply Registration Xform to thalamus mask: Resample Scalar/Vector/DWI Volume module. Settings:

• Input Volume : A0_thalamus
• Reference Volume : A1_labels
• Output Volume : “Create New Volume”, rename to A0_thalamus_aff
• Transform Node: Xform_A0Affine_ICP
• Interpolation Type: “nn”

1. 1. Change datatype of new volume A0_labels_aff to labelmap (Volumes module)
2. Mask new labelmap with A0_thalamus_aff: Mask Image module

• Input Volume : A0_labels_aff
• Mask Volume : A0_thalamus_aff�
• Masked Volume: A0_labels aff

1. 1. Mask new labelmap with A1_thalamus: Mask Image module

• Input Volume : A0_labels_aff�
• Mask Volume : A1_thalamus_aff�
• Masked Volume: New volume, rename to: A0_labels aff_clip

1. Typecast A1_labelmap: Cast Image module

• Input Volume: A1_label
• Output Volume: A1_label
• Output Type: “short”

1. Merge Labelmaps:Image Label Combine module

• Input Label Map A: A0_label_aff_clip
• Input Label Map B: A1_labels
• Output Label Map: “Create New Volume”, rename to A1_labels_merged
• First label overwrites second: yes

Registration Results

Download

• download entire package (Data,Tutorial, Solution, zip file 14 MB)
• Presets
• Tutorial only
• Image Data only

Discussion: Registration Challenges

• Because the atlas represents a probabilistic image (i.e. contains blurring from combining multiple subjects), its contrast differs significantly from the target image.
• The atlas has strong rotational misalignment that can cause difficulty for automated affine registration.
• The two images represent different anatomies, a non-rigid registration is required

Discussion: Key Strategies

• Because of the strong differences in image contrast, Mutual Information is recommended as the most robust metric.
• masking (skull stripping) is highly recommended to obtain good results.
• because speed is not that critical, we increase the sampling rate for both affine and BSpline registration
• we also expect larger differences in scale & distortion than with regular structural scans: so we significantly (2x-3x) increase the expected values for scale and skew from the defaults.
• a good affine alignment is important before proceeding to non-rigid alignment to further correct for distortions.

Acknowledgments

• dataset provided by Killian Pohl, Ph.D.