Difference between revisions of "Projects:RegistrationLibrary:RegLib C10"

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(Created page with 'Back to ARRA main page <br> Back to Registration main page <br> [[Projects:RegistrationDocumentation:UseCaseInv…')
 
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{| style="color:#bbbbbb; background-color:#333333;" cellpadding="10" cellspacing="0" border="0"
 
{| style="color:#bbbbbb; background-color:#333333;" cellpadding="10" cellspacing="0" border="0"
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|[[Image:RegLib_C03_Reference_axial.png|150px|lleft|this is the fixed reference image. All images are aligned into this space]]  
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|[[Image:RegLib_C10_EMAtlas1.png|150px|lleft|this is the fixed reference image. All images are aligned into this space]]  
 
|[[Image:Arrow_left_gray.jpg|100px|lleft]]  
 
|[[Image:Arrow_left_gray.jpg|100px|lleft]]  
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|[[Image:RegLib_C03_Baseline_axial.png|150px|lleft|this is the moving image. The transform is calculated by matching this to the reference image]]
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|[[Image:RegLib_C10_EMAtlas2.png|150px|lleft|this is the moving image. The transform is calculated by matching this to the reference image]]
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|[[Image:RegLib_C03_DTIVol_axial.png|150px|lleft|this is a passive image to which the calculated transform is applied. It is a label-map in the same space as the moving FLAIR image]]
 
 
|align="left"|LEGEND<br><small><small>
 
|align="left"|LEGEND<br><small><small>
 
[[Image:Button_red_fixed.jpg|20px|lleft]]  this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution<br>
 
[[Image:Button_red_fixed.jpg|20px|lleft]]  this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution<br>
 
[[Image:Button_green_moving.jpg|20px|lleft]]  this indicates the moving image that determines the registration transform.  <br>
 
[[Image:Button_green_moving.jpg|20px|lleft]]  this indicates the moving image that determines the registration transform.  <br>
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[[Image:Button_blue_tag.jpg|20px|lleft]] 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.
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[</small></small>
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</small></small>
 
 
|-
 
|-
 
|[[Image:Button_red_fixed.jpg|40px|lleft]]  Target Brain
 
|[[Image:Button_red_fixed.jpg|40px|lleft]]  Target Brain
 
|
 
|
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|[[Image:Button_green_moving.jpg|40px|lleft]]  
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|[[Image:Button_green_moving.jpg|40px|lleft]] Tissue Atlas
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|[[Image:Button_blue_tag.jpg|40px|lleft]] Probabilistic Atlas image (different subject)
 
 
|-
 
|-
 
|0.46 x 0.46 x 3.0 mm axial <br> 512 x 512 x 46<br>RAS
 
|0.46 x 0.46 x 3.0 mm axial <br> 512 x 512 x 46<br>RAS
 
|
 
|
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|1.0 x 1.0 x 3.3 mm <br> axial oblique<br> 256 x 256 x 36<br>RAS
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|1.0 x 1.0 x 3.3 mm <br> axial oblique<br> 256 x 256 x 36 <br>RAS
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|1.0 x 1.0 x 3.3 mm <br> axial oblique<br> 256 x 256 x 36 x 9 <br>RAS
 
 
|}
 
|}
  
 
===Objective / Background ===
 
===Objective / Background ===
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This is a typical example of DTI processing. Goal is to align the DTI image with a structural scan that provides accuracte anatomical reference. The DTI contains acquisition-related distortion and insufficient contrast to discern anatomical detail.
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This is an example of sparse atlas co-registration. Not all atlases have an associated reference image that can be used for registration. Because the atlas represents a map of a particular tissue class probability, its contrast differs significantly from the target image.
 
=== Keywords ===
 
=== Keywords ===
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MRI, brain, head, intra-subject, DTI, DWI
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MRI, brain, head, inter-subject, probabilistic atlas, atlas-based segmentation
  
 
===Input Data===
 
===Input Data===
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*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : T2w axial, 0.4mm resolution in plane, 3mm slices
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*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : T1w axial, 1mm resolution in plane, 3mm slices
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*[[Image:Button_green_moving_white.jpg|20px]] moving: Baseline image of acquired DTI volume, corresponds to T2w MRI , 0.9375 x 0.9375 x 1.4 mm voxel size, oblique
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*[[Image:Button_green_moving_white.jpg|20px]] moving: Probabilistic Tissue atlas,  
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*[[Image:Button_blue_tag_white.jpg|20px]] tag: Tensor data of DTI volume, oblique, same orientation as Baseline image. The result Xform will be applied to this volume. The original DWI has 26 directions, the extracted DTI volume has 9 scalars, i.e. 256 x 256 x 36 x 9
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x 36 x 9
  
 
=== Registration Results===
 
=== Registration Results===
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=== Discussion: Registration Challenges ===
 
=== Discussion: Registration Challenges ===
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*The DTI contains acquisition-related distortions (commonly EPI acquisitions) that can make automated registration difficult.
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*Because the atlas represents a map of a particular tissue class probability, its contrast differs significantly from the target image.
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*the two images often have strong differences in  voxel sizes and voxel anisotropy. If the orientation of the highest resolution is not the same in both images, finding a good match can be difficult.
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*The two images may have strong differences in  voxel sizes and voxel anisotropy. If the orientation of the highest resolution is not the same in both images, finding a good match can be difficult.
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*there may be widespread and extensive pathology  (e.g stroke, tumor) that might affect the registration if its contrast is different in the baseline and structural reference scan
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*The two images represent different anatomies, a non-rigid registration is required
  
 
=== Discussion: Key Strategies ===
 
=== Discussion: Key Strategies ===
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*the two images have identical contrast, hence we could consider "sharper" cost functions, such as NormCorr or MeanSqrd. But because of the strong distortions and lower resolution of the moving image, Mutual Information is recommended as the most robust metric.
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*Because of the strong differences in image contrast, Mutual Information is recommended as the most robust metric.
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*often anatomical labels are available from the reference scan. It would be less work to align the anatomical reference with the DTI, since that would circumvent having to resample the complex tensor data into a new orientation. However the strong distortions are better addressed by registering the other direction, i.e. move the DTI into the anatomical reference space.
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*masking (skull stripping) is highly recommended to obtain good results.  
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*because we seek to assess/quantify regional size change, we must use a rigid (6DOF) scheme, i.e. we must exclude scaling.
 
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*masking is likely necessary to obtain good results.  
 
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*in this example the initial alignment of the two scans is very poor. The strongly oblique orientation of the DTI makes an initial manual alignment step necessary.
 
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*these two images are not too far apart initially, so we reduce the default of expected translational misalignment
 
 
*because speed is not that critical, we increase the sampling rate from the default 2% to 15%.
 
*because speed is not that critical, we increase the sampling rate from the default 2% to 15%.
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*we also expect larger differences in scale & distortion than with regular structural scane: so we significantly  (2x-3x) increase the expected values for scale and skew from the defaults.  
+
*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.
 
*a good affine alignment is important before proceeding to non-rigid alignment to further correct for distortions.
  
 
=== Acknowledgments ===
 
=== Acknowledgments ===

Revision as of 22:44, 16 February 2010

Home < Projects:RegistrationLibrary:RegLib C10

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Back to Registration Use-case Inventory

Slicer Registration Library Exampe #10: Co-registration of probabilistic tissue atlas for subsequent EM segmentation

this is the fixed reference image. All images are aligned into this space lleft this is the moving image. The transform is calculated by matching this to the reference image LEGEND

lleft this indicates the reference image that is fixed and does not move. All other images are aligned into this space and resolution
lleft this indicates the moving image that determines the registration transform.
[

lleft Target Brain lleft Tissue Atlas
0.46 x 0.46 x 3.0 mm axial
512 x 512 x 46
RAS 		1.0 x 1.0 x 3.3 mm
axial oblique
256 x 256 x 36
RAS

Objective / Background

This is an example of sparse atlas co-registration. Not all atlases have an associated reference image that can be used for registration. Because the atlas represents a map of a particular tissue class probability, its contrast differs significantly from the target image.

Keywords

MRI, brain, head, inter-subject, probabilistic atlas, atlas-based segmentation

Input Data

  • Button red fixed white.jpgreference/fixed : T1w axial, 1mm resolution in plane, 3mm slices
  • Button green moving white.jpg moving: Probabilistic Tissue atlas,

x 36 x 9

Registration Results

after affine alignment

Download

Discussion: Registration Challenges

  • Because the atlas represents a map of a particular tissue class probability, its contrast differs significantly from the target image.
  • The two images may have strong differences in voxel sizes and voxel anisotropy. If the orientation of the highest resolution is not the same in both images, finding a good match can be difficult.
  • 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 from the default 2% to 15%.
  • 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

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