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[[Projects:RegistrationDocumentation:UseCaseInventory|Back to Registration Use-case Inventory]] <br>
 
[[Projects:RegistrationDocumentation:UseCaseInventory|Back to Registration Use-case Inventory]] <br>
  
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==Slicer Registration Library Exampe #19: Multi-contrast group analysis: intra- and inter-subject registration of multi-contrast MRI ==
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{| style="color:#bbbbbb; background-color:#333333;" cellpadding="10" cellspacing="0" border="0"
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== <small>v3.6.1</small> [[Image:Slicer3-6Announcement-v1.png‎|150px]] Slicer Registration Library Case #19: Multi-contrast group analysis: intra- and inter-subject registration of multi-contrast MRI   ==
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=== Input ===
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{| style="color:#bbbbbb; " cellpadding="2" cellspacing="0" border="0"
 
|[[Image:RegLib_C19_thumb_N2_T1.png|150px|this is the main reference image. All images are aligned into this space]]  
 
|[[Image:RegLib_C19_thumb_N2_T1.png|150px|this is the main reference image. All images are aligned into this space]]  
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|[[Image:RegArrow_Affine.png|70px|lleft]]
 
|[[Image:RegLib_C19_thumb_N2_T2.png|150px|this is the 1st intra-subject moving target,  to be aligned with the main reference directly]]
 
|[[Image:RegLib_C19_thumb_N2_T2.png|150px|this is the 1st intra-subject moving target,  to be aligned with the main reference directly]]
 
|[[Image:RegLib_C19_thumb_N2_MRA.png|150px|this is the 2nd intra-subject moving target,  to be aligned with the main reference directly]]
 
|[[Image:RegLib_C19_thumb_N2_MRA.png|150px|this is the 2nd intra-subject moving target,  to be aligned with the main reference directly]]
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|[[Image:Arrow_left_gray.jpg|100px|lleft]]  
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|-
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|subj 1: T1
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|
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|subj 1: T2
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|subj 1: MRA
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|-
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|[[Image:RegArrow_NonRigidVert.png|70px|lleft]]  
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|
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|
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|
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|-
 
|[[Image:RegLib_C19_thumb_N4_T1.png|150px|this is  both the fixed target for the 2nd subject and the moving image for inter-subject registration]]
 
|[[Image:RegLib_C19_thumb_N4_T1.png|150px|this is  both the fixed target for the 2nd subject and the moving image for inter-subject registration]]
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|[[Image:RegArrow_Affine.png|70px|lleft]]
 
|[[Image:RegLib_C19_thumb_N4_T2.png|150px|this is a moving image to be aligned indirectly to the main ref]]
 
|[[Image:RegLib_C19_thumb_N4_T2.png|150px|this is a moving image to be aligned indirectly to the main ref]]
 
|[[Image:RegLib_C19_thumb_N4_MRA.png|150px|his is a moving image to be aligned indirectly to the main ref ]]
 
|[[Image:RegLib_C19_thumb_N4_MRA.png|150px|his is a moving image to be aligned indirectly to the main ref ]]
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|align="left"|LEGEND<br><small><small>
 
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[[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>
 
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[[Image:Button_green_moving.jpg|20px|lleft]]  this indicates the moving image that determines the registration transform.  <br>
 
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</small></small>
 
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|-
 
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|[[Image:Button_red_fixed.jpg|40px|lleft]]  reference T1, subject 1
 
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|[[Image:Button_green_moving.jpg|40px|lleft]]  moving T2, subject 1
 
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|[[Image:Button_green_moving.jpg|40px|lleft]]  moving MRA, subject 1
 
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|
 
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|[[Image:Button_green_moving.jpg|40px|lleft]][[Image:Button_red_fixed.jpg|40px|lleft]]  reference T1, subject 2
 
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|[[Image:Button_green_moving.jpg|40px|lleft]]  moving T2, subject 2
 
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|[[Image:Button_green_moving.jpg|40px|lleft]]  moving MRA, subject 2
 
 
|-
 
|-
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|1mm isotropic<br> 176 x 256 x 176<br>PA
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|subj 2: T1
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|1mm isotropic<br> 192 x 256 x 128<br>PA
 
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|0.51 x 0.51 x 0.8 mm <br> 448 x 448 x 128<br>PA
 
 
|
 
|
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|1mm isotropic<br> 176 x 256 x 176<br>PA
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|subj 2: T2
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|1mm isotropic<br> 192 x 256 x 128<br>PA
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|subj 2: MRA
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|0.51 x 0.51 x 0.8 mm <br> 448 x 448 x 128<br>PA
 
 
|}
 
|}
  
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*T1: 1mm isotropic;  176 x 256 x 176
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*T2: 1mm isotropic;  192 x 256 x 128
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*MRA: 0.51 x 0.51 x 0.8 mm; 448 x 448 x 128
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=== Modules ===
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*'''Slicer 3.6.1 recommended modules:  [https://www.slicer.org/wiki/Modules:BRAINSFit BrainsFit]'''
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*For the inter-subject nonrigid portion: [https://www.slicer.org/wiki/Modules:DeformableB-SplineRegistration-Documentation-3.6 Fast nonrigid BSpline]
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*For combining the transforms: copy and paste in a Text Editor
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*For applying the transforms and resampling: [https://www.slicer.org/wiki/Modules:ResampleScalarVectorDWIVolume-Documentation-3.6 Resample Scalar/Vector/DWI Volume]
  
 
===Objective / Background ===
 
===Objective / Background ===
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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.
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This is an example of multi-stage registration containing both intra- and inter-subject alignment. We have two sets of multi-sequence MRI for two subjects, each comprised of a T1, T2 and perfusion MRA scan. We ant everything aligned to a single space to enable regional or voxel-based group comparison.  
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=== Keywords ===
 
=== Keywords ===
 
multi-stage registration, MRI, brain, head, multi-contrast,  inter-subject,  group analysis,  MRA, T2
 
multi-stage registration, MRI, brain, head, multi-contrast,  inter-subject,  group analysis,  MRA, T2
  
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===Input Data===
 
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*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : T1w coronal, 1mm isotropic. Called ''A1_gray''
 
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*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : labelmap , aligned with above. Called ''A1_label''
 
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*[[Image:Button_blue_tag_white.jpg|20px]] moving: T1w coronal, 0.9 inplane, 1.5mm coronal slices. Called ''A0_gray''
 
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*[[Image:Button_green_moving_white.jpg|20px]] moving: labelmap , aligned with above. Called ''A0_label''
 
 
=== Methods ===
 
=== Methods ===
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#Visualize & browse A0 data: determine label range of thalamic nuclei labels in A0_label: 500-526
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#align T2 and MRA of each subject to their respective T1 via an affine transform
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#Visualize & browse A1 data: determine label range of thalamus lables in A1_label: 10 and 49
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#align the T1 of subject 2 to the T1 of subject 1 via a non-rigid transform, this establishes the inter-subject mapping
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#Build label mask of thalamus for A0: '''Editor''' module
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#combine the affine transforms of subject 2 T2 and MRA with the above nonrigid and resample the two images into the reference space
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##Create Labelmap From”: ''A0_labels''
 
  
 
=== Registration Results===
 
=== Registration Results===
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{| style="color:#bbbbbb; background-color:#333333;" cellpadding="10" cellspacing="0" border="0"
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[[Image:RegLib_C19_unreg.gif|300px|unregistered]] unregistered (left)
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|[[Image:RegLib_C19_unreg.gif|200px|left|unregistered]] [[Image:RegLib_C19_reg.gif|200px|left|registered]]  
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[[Image:RegLib_C19_reg.gif|300px|registered]]  registered series : Affine + Fast BSpline (3x3x3). Note the animated gif cycles through both subjects and intra-subject scans.  <br>
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|}
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[[Image:RegLib_C19_AGif_Intra_N2.gif|300px|registered]] registered intra-subject N2 [[Image:RegLib_C19_AGif_Intra_N4.gif|300px|registered]] registered intra-subject N4 (right): Affine only<br>
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[[Image:RegLib_C19_AGif_N4-N2_T1.gif|300px|registered]] registered N4-N2 (Affine+BSpline BRAINSfit 7x7x7)<br>
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[[Image:RegLib_C19_AGif_N4-N2_T2.gif|300px|registered]]  registered N4-N2 (Affine+BSpline BRAINSfit 7x7x7)<br>
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[[Image:RegLib_C19_AGif_N4-N2_MRA.gif|300px|registered]] registered N4-N2 (Affine+BSpline BRAINSfit 7x7x7)<br>
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[[Image:RegLib_C19_AGif_BSplDef.gif|300px|registered]]  registered N4-N2 BSpline deformation only applied to N4<br>
  
 
===Download ===
 
===Download ===
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*[[Media:RegLib_C11_AtlasMerge_TutorialSet.zip‎‎|download entire '''tutorial package''' <small> (Original Data, Intermediate Results, Solution, zip file 74 MB) </small>]]
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*Data
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*[[Media:RegLib_C11_AtlasMerge_OldAtlas.zip‎‎|download '''old atlas dataset'''  <small> (MRI+labelmap,thalamic nuclei models, list of labels+names), zip file 5.4 MB) </small>]]
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**[[Media:RegLib_C19_DATA.zip‎‎|download '''image data'''<small> (Original Data, Result transforms, parameter presets, zip file 81 MB) </small>]]
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*[[Media:RegLib_C11_AtlasMerge_NewAtlas.zip‎‎|download '''new atlas dataset'''  <small> (MRI T1+T2, labelmap, thalamus model), zip file 126MB) </small>]]
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*Presets
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*[[Media:RegLib_C11_AtlasMerge_NewAtlasMerged.zip|download '''result merged new atlas dataset'''  <small> (MRI T1+T2, new merged labelmaps (2x)  incl. thalamic nuclei, colormap file with label names), zip file 126 MB) </small>]]
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**[[Media:RegLib_C19_RegPresets.mrml‎‎|download '''registration parameter presets'''  <small> (mrml file, 12 kB) </small>]]
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*[[Media:RegLib_C11_Tutorial_AtlasMerging.ppt.zip|download full '''step-by-step tutorial'''<small> (PowerPoint, zip file, 2.5 MB) </small>]]
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*Documentation
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*obsolete: [[Media:RegLib_C11_CompareSet.zip|download compare set<small>  (New Atlas, 2 merged versions (full & clipped), thalamus models, Xform, Merged colormap, zip file, 67 MB) </small>]]
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**[[Media:RegLib_C19_Tutorial.ppt‎‎|download '''guided tutorial''' <small> (power point file, 1.5 MB) </small>]]
  
 
[[Projects:RegistrationDocumentation:ParameterPresetsTutorial|Link to User Guide: How to Load/Save Registration Parameter Presets]]
 
[[Projects:RegistrationDocumentation:ParameterPresetsTutorial|Link to User Guide: How to Load/Save Registration Parameter Presets]]
  
 
=== Discussion: Registration Challenges ===
 
=== Discussion: Registration Challenges ===
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*Because the structures of interest are a very small subset of the image without distinct grayscale contrast
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*We have two separate sets of registrations to combine. While theoretically possible to simply align every scan with the reference directly, this is likely to produce inferior results. Basic rule of thumb is to register to the image that is closest in anatomy and contrast, in that order.
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*the two atlases represent different anatomies and hence some residual misalignment is inevitable
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*the MRA has a strongly clipped FOV and low tissue contrast
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*the two labelmaps have different resolutions and different smoothness of structure outlines. Some need filtering to remove spurious surface details that would distract the registration algorithm
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*Currently (Slicer 3.6) the concatenation of linear and nonlinear transforms is not (yet) supported directly in slicer. However this is an easy and fast procedure that can be done with any text editor. We merely copy the affine portion from one transform file and paste it into the nonrigid transform file.
  
 
=== Discussion: Key Strategies ===
 
=== Discussion: Key Strategies ===
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*Because the structures of interest are a very small subset of the image without distinct grayscale contrast, we co-register surfaces rather than intensity volumes
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#For the intra-subject affine portion: [https://www.slicer.org/wiki/Modules:BRAINSFit BRAINSfit]
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#For the inter-subject nonrigid portion: [https://www.slicer.org/wiki/Modules:DeformableB-SplineRegistration-Documentation-3.6 Fast nonrigid BSpline]
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#For combining the transforms: copy and paste in a Text Editor
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#For applying the transforms and resampling: [https://www.slicer.org/wiki/Modules:ResampleScalarVectorDWIVolume-Documentation-3.6 Resample Scalar/Vector/DWI Volume]
  
 
=== Acknowledgments ===
 
=== Acknowledgments ===
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*dataset provided by Ron Kikinis, M.D. and Florin Talos, M.D.
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*dataset provided by the [http://ij.itk.org/midas/community/view/21 UNC Midas Database of healthy volunteers]

Latest revision as of 18:07, 10 July 2017

Home < Projects:RegistrationLibrary:RegLib C19

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Back to Registration Use-case Inventory


v3.6.1 Slicer3-6Announcement-v1.png Slicer Registration Library Case #19: Multi-contrast group analysis: intra- and inter-subject registration of multi-contrast MRI

Input

this is the main reference image. All images are aligned into this space lleft this is the 1st intra-subject moving target, to be aligned with the main reference directly this is the 2nd intra-subject moving target, to be aligned with the main reference directly
subj 1: T1 subj 1: T2 subj 1: MRA
lleft
this is both the fixed target for the 2nd subject and the moving image for inter-subject registration lleft this is a moving image to be aligned indirectly to the main ref his is a moving image to be aligned indirectly to the main ref
subj 2: T1 subj 2: T2 subj 2: MRA


  • T1: 1mm isotropic; 176 x 256 x 176
  • T2: 1mm isotropic; 192 x 256 x 128
  • MRA: 0.51 x 0.51 x 0.8 mm; 448 x 448 x 128

Modules

Objective / Background

This is an example of multi-stage registration containing both intra- and inter-subject alignment. We have two sets of multi-sequence MRI for two subjects, each comprised of a T1, T2 and perfusion MRA scan. We ant everything aligned to a single space to enable regional or voxel-based group comparison.

Keywords

multi-stage registration, MRI, brain, head, multi-contrast, inter-subject, group analysis, MRA, T2

Methods

  1. align T2 and MRA of each subject to their respective T1 via an affine transform
  2. align the T1 of subject 2 to the T1 of subject 1 via a non-rigid transform, this establishes the inter-subject mapping
  3. combine the affine transforms of subject 2 T2 and MRA with the above nonrigid and resample the two images into the reference space

Registration Results

unregistered unregistered (left) registered registered series : Affine + Fast BSpline (3x3x3). Note the animated gif cycles through both subjects and intra-subject scans.

registered registered intra-subject N2 registered registered intra-subject N4 (right): Affine only
registered registered N4-N2 (Affine+BSpline BRAINSfit 7x7x7)
registered registered N4-N2 (Affine+BSpline BRAINSfit 7x7x7)
registered registered N4-N2 (Affine+BSpline BRAINSfit 7x7x7)
registered registered N4-N2 BSpline deformation only applied to N4

Download

Link to User Guide: How to Load/Save Registration Parameter Presets

Discussion: Registration Challenges

  • We have two separate sets of registrations to combine. While theoretically possible to simply align every scan with the reference directly, this is likely to produce inferior results. Basic rule of thumb is to register to the image that is closest in anatomy and contrast, in that order.
  • the MRA has a strongly clipped FOV and low tissue contrast
  • Currently (Slicer 3.6) the concatenation of linear and nonlinear transforms is not (yet) supported directly in slicer. However this is an easy and fast procedure that can be done with any text editor. We merely copy the affine portion from one transform file and paste it into the nonrigid transform file.

Discussion: Key Strategies

  1. For the intra-subject affine portion: BRAINSfit
  2. For the inter-subject nonrigid portion: Fast nonrigid BSpline
  3. For combining the transforms: copy and paste in a Text Editor
  4. For applying the transforms and resampling: Resample Scalar/Vector/DWI Volume

Acknowledgments

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