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[[Projects:RegistrationDocumentation:UseCaseInventory|Back to Registration Use-case Inventory]] <br>
 
[[Projects:RegistrationDocumentation:UseCaseInventory|Back to Registration Use-case Inventory]] <br>
  
−
==Slicer Registration Library Case 04: Intra-subject Brain MR of Multiple Sclerosis: Multi-contrast series for lesion change assessment ==
+
= <small>updated for '''v4.1'''</small> [[Image:Slicer4_RegLibLogo.png|150px]] <br> Slicer Registration Library Case 04:   Multi-contrast brain MRI of Multiple Sclerosis =
−
{| style="color:#bbbbbb; background-color:#333333;" cellpadding="10" cellspacing="0" border="0"
+
=== Input ===
−
|[[Image:RLib02_SPGR.png|150px|lleft|this is the fixed reference image. All images are aligned into this space]]  
+
{| style="color:#bbbbbb; " cellpadding="10" cellspacing="0" border="0"
−
|[[Image:RLib02_SPGR+ICC.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]]
+
|[[Image:Reglib_C04_Thumb_PD1.jpg|100px|lleft|this is the main fixed reference image. All images are ev. aligned into this space]]  
−
|[[Image:Arrow_left_gray.jpg|100px|lleft]]
+
|[[Image:Reglib_C04_Thumb_T21.jpg|100px|lleft|this is the main fixed reference image. All images are ev. aligned into this space]]  
−
|[[Image:RLib02_FLAIR_150.png|150px|lleft|this is the moving image. The transform is calculated by matching this to the reference image]]
+
|[[Image:RegArrow_Affine.png|70px|lleft]]  
−
|[[Image:RLib02_FLAIR+LesionSeg_150.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]]
+
|[[Image:Reglib_C04_Thumb_Gd1.jpg|100px|lleft|this is the intra-subject moving image. ]]
−
|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_green_moving.jpg|20px|lleft]]  this indicates the moving image that determines the registration transform. <br>
 
−
[[Image:Button_purple_mask.jpg|20px|lleft]] this indicates images that serve as masks, i.e. they focus the active registration onto a specific area.<br>
 
−
[[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.
 
−
</small></small>
 
 
|-
 
|-
−
|[[Image:Button_red_fixed.jpg|40px|lleft]]  T1 SPGR
+
|exam 1: PD
−
|[[Image:Button_purple_mask.jpg|40px|lleft]]  mask
+
|exam 1: T2
 
|
 
|
−
|[[Image:Button_green_moving.jpg|40px|lleft]] T2 FLAIR
+
|exam 1: T1-Gd
−
|[[Image:Button_blue_tag.jpg|40px|lleft]] segmentation
 
 
|-
 
|-
−
|1mm isotropic<br> 256 x 256 x 146<br>RAS
+
|[[Image:RegArrow_AffineVert.png|70px|lleft]]
−
|1mm isotropic<br> 256 x 256 x 146<br>RAS
 
 
|
 
|
−
|1.2mm isotropic<br> 256 x 256 x 116<br>RAS
+
|
−
|1.2mm isotropic<br> 256 x 256 x 116<br>RAS
+
|
 +
|-
 +
|[[Image:Reglib_C04_Thumb_PD2.jpg|100px|lleft|this is the inter-subject moving image, but also the reference for exam 2]]
 +
|[[Image:Reglib_C04_Thumb_T22.jpg|100px|lleft|this is the inter-subject moving image, but also the reference for exam 2]]
 +
|[[Image:RegArrow_Affine.png|70px|lleft]]
 +
|[[Image:Reglib_C04_Thumb_Gd2.jpg|100px|lleft|this is the moving image. ]]
 +
|-
 +
|exam 2: PD
 +
|exam 2: T2
 +
|
 +
|exam 2: T1-Gd
 
|}
 
|}
 +
 +
=== Slicer 4.1 recommended Modules ===
 +
*[https://www.slicer.org/wiki/Documentation/4.1/Modules/BRAINSFit BrainsFit]
 +
*tutorials here are for the above module, but you may also use the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/AffineRegistration Affine Registration] or [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/ExpertAutomatedRegistration Expert Automated Registration] modules.
 +
 
===Objective / Background ===
 
===Objective / Background ===
−
This scenario occurs in many forms whenever we wish to align all the series from a single MRI exam/session into a common space. Alignment is necessary because the subject likely has moved in between series.  
+
This scenario occurs in many forms whenever we wish to assess change in a series of multi-contrast MRI. The follow-up scan(s) are to be aligned with the baseline, but also the different series within each exam need to be co-registered, since the subject may have moved between acquisitions. Hence we have a set of nested registrations. This particular exam features a dual echo scan (PD/T2), where the two structural scans are aligned by default. The post-contrast T1-GdDTPA scan however is not necessarily aligned with the dual echo. Also the post-contrast scan is taken with a clipped field of view (FOV) and a lower axial resolution, with 4mm slices and a 1mm gap (which we treat here as a de facto 5mm slice).
 +
[[Projects:RegistrationLibrary:RegLib_C04:About| read more about this dataset here]]
 +
 
 +
=== Download ===
 +
*Data:
 +
**[[Media:RegLib_C04_Data.zip‎|'''Registration Library Case 04: MS Multi-contrast series (PD,T2, T1-GdDTPA): Lesion change assessment''' <small> (Data & Solution Xforms, zip file 18 MB) </small>]]
 +
*Documentation
 +
**[[Media:RegLib_C04_Register_interExam.mov‎|ScreenCast Video: loading data & inter-exam registration: aligning exam 2 to exam 1 <small> (quicktime movie, 8 MB) </small>]]
 +
**[[Media:RegLib_C04_Register_intraExam.mov‎|ScreenCast Video: intra-exam registration: aligning T1Gd of exam 2 to PD of exam 2 <small> (quicktime movie, 8 MB) </small>]]
 +
**[[Media:RegLib_C04_Register_XformHierarchy.mov‎|ScreenCast Video: transform hierarchy: how to arrange the MRML data tree to properly reflect the mutual dependencies of transforms <small> (quicktime movie, 2 MB) </small>]]
 +
 
 
=== Keywords ===
 
=== Keywords ===
−
MRI, brain, head, intra-subject, FLAIR, T1, defacing, masking, labelmap, segmentation
+
MRI, brain, head, intra-subject, multiple sclerosis, MS, multi-contrast, change assessment, dual echo, nested registration
  
 
===Input Data===
 
===Input Data===
−
*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : T1 SPGR , 1x1x1 mm voxel size, sagittal, RAS orientation.  
+
*reference/fixed : PD.1 baseline exam , 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition, RAS orientation.  
−
*[[Image:Button_green_moving_white.jpg|20px]] moving: T2 FLAIR 1.2x1.2x1.2 mm voxel size, sagittal, RAS orientation.
+
*fixed  T2.1 baseline exam , 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition, RAS orientation. -> (aligned with PD.1, not used for registering)
−
*[[Image:Button_purple_mask_white.jpg|20px]]mask: skull stripping labelmap obtained from SPGR, serves as mask
+
*moving: T1.1 (GdDTPA contrast-enhanced scan)  baseline exam 0.9375 x 0.9375 x 5 mm voxel size, axial acquisition.
−
*[[Image:Button_blue_tag_white.jpg|20px]]tag: segmentation labelmap obtained from FLAIR.
+
*moving: PD.2 follow-up exam 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition.
−
*Content preview: Have a quick look before downloading: Does your data look like this?  [[Media:Lighbox_SPGR.jpg|SPGR Lighbox]] , [[Media:Lighbox_FLAIR.jpg|FLAIR Lighbox]]
+
*moving: T2.2 follow-up exam 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition. -> same orientation as PD2, will have same transform applied
 +
*moving:T1.2-GdDTPA  follow-up exam0.9375 x 0.9375 x 5 mm voxel size, axial acquisition. -> undergoes 2 transforms: first to PD.2, then to PD.1
  
 
===Registration Challenges===
 
===Registration Challenges===
−
*the amount of misalignment to be small. Subject did not leave the scanner in between the two acquisitions, but we have some head movement.
+
*we have multiple nested transforms: each exam is co-registered within itself, and then the exams are aligned to eachother
−
*we know the underlying structure/anatomy did not change, but the two distinct acquisition types may contain different amounts of distortion
+
*potential pathology change can affect the registration
−
*the T1 high-resolution had a "defacing" applied, i.e. part of the image containing facial features was removed to ensure anonymity. The FLAIR is lower resolution and contrast and did not need this. The sharp edges and missing information in part of the image may cause problems.
+
*anisotropic voxel size causes difficulty in rotational alignment
−
*we have a skull stripping label map of the fixed image (T1) that we can use to mask out the non-brain part of the image and prevent it from actively participating in the registration.
+
*clipped FOV and low tissue contrast of the post-contrast scan
−
*we have one or more label-maps attached to the moving image that we also want to align.
 
−
*the different series have different dimensions, voxel size and field of view. Hence the choice of which image to choose as the reference becomes important. The additional image data present in one image but not the other may distract the algorithm and require masking.
 
−
*hi-resolution datasets may have defacing applied to one or both sets, and the defacing-masks may not be available
 
−
*the different series have different contrast. The T1 contains good contrast between white (WM) and gray matter (GM) , and pathology appears as hypointense. The FLAIR on the other hand shows barely any WM/GM contrast and the pathology appears very dominantly as hyperintense.
 
  
 
===Key Strategies===
 
===Key Strategies===
−
*we choose the SPGR as the anatomical reference. Unless there are overriding reasons, always use the highest resolution image as your fixed/reference, to avoid loosing data through the registration.
+
*we first register the post-contrast scans within each exam to the PD
−
*the defacing of the SPGR image introduces sharp edges that can be detrimental. We apply a multiresolution scheme at least. If this fails we mask that area or better still the brain. As a general rulle, if you have the mask available, use it.
+
*second we register the follow-up PD scan to the baseline PD
−
*because of the contrast differences and the defacing we use '''Mutual Information''' as the cost function.
+
*we also move the T2 exam within the same Xform
−
*because of the combined effects of rotational misalignment, defacing, pathology and contrast differences, we use a multi-resolution approach (Register Images MultiRes).
+
*we then nest the first alignment within the second
 +
*because of the contrast differences and anisotropic resolution we use Mutual Information as cost function for better robustness
 +
=== Procedure ===
 +
*there is a brief movie in the download section that shows this procedure step by step. Guides below use the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/BRAINSFit General Registration (BRAINS)] module, but you may also use the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/AffineRegistration Affine Registration] or [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/ExpertAutomatedRegistration Expert Automated Registration] modules.
 +
*'''Phase 1''': register inter-exam exam 2 -> exam 1
 +
#open the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/BRAINSFit General Registration (BRAINS) module]
 +
##''Fixed Image Volume'': e1_PD
 +
##''Moving Image Volume'': e2_PD
 +
##Output Settings:
 +
###''Slicer BSpline Transform": none
 +
###''Slicer Linear Transform'': create & rename new transform: Xf1_e21_Affine.tfm
 +
###''Output Image Volume'': none
 +
##''Registration Phases'': check boxes for ''Rigid''  and ''Affine''
 +
##''Main Parameters''
 +
###'''Number of Samples''': 200,000
 +
##click: Apply; runtime < 1 min (MacPro QuadCore 2.4GHz)
 +
#note that the Slicer4.1 version of BRAINS registration does '''not''' automatically place the moving volume inside the result transform, as it did for Slicer 3.6. In order to see the result, you must either go to the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/Data ''Data module''] and drag the moving volume inside the transform node, or select to generate a resampled ''Output Volume'', as done above. The demo movie you can download here shows how to do this.
 +
*'''Phase 2''': register intra-examT1Gd to PD
 +
#open the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/BRAINSFit General Registration (BRAINS) module]
 +
##''Fixed Image Volume'': e1_PD
 +
##''Moving Image Volume'': e1_T1Gd
 +
##Output Settings:
 +
###''Slicer BSpline Transform": none
 +
###''Slicer Linear Transform'': create & rename new transform: Xf2_e1-T1Gd_Rigid.tfm
 +
###''Output Image Volume'': none
 +
##''Registration Phases'': check boxes for ''Rigid'' only
 +
##''Main Parameters''
 +
###'''Number of Samples''': 200,000
 +
##click: Apply; runtime < 1 min (MacPro QuadCore 2.4GHz)
 +
#note that the Slicer4.1 version of BRAINS registration does '''not''' automatically place the moving volume inside the result transform, as it did for Slicer 3.6. In order to see the result, you must either go to the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/Data ''Data module''] and drag the moving volume inside the transform node, or select to generate a resampled ''Output Volume'', as done above. The demo movie you can download here shows how to do this.
 +
#repeat for exam 2, i.e
 +
##''Fixed Image Volume'': e2_PD
 +
##''Moving Image Volume'': e2_T1Gd
 +
##''Slicer Linear Transform'': create & rename new transform: Xf3_e2-T1Gd_Rigid.tfm
 +
*'''Phase 3''': organize transform hierarchies in the ''Data'' tree
 +
#go to the [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/Data ''Data module'']
 +
##drag the volume node e1_T1Gd onto the transform  Xf2_e1-T1Gd_Rigid; let go of the mouse button when you see a hilight box appear around the transform node name
 +
##drag the volume node e2_T1Gd onto the transform  Xf3_e2-T1Gd_Rigid;
 +
##drag the volume node  e2_PD and e2_T2 onto the transform Xf1_e21_Affine.tfm
 +
##drag the transform node Xf3_e2_T1Gd_Rigid.tfm onto the transform Xf1_e21_Affine.tfm
 +
##your MRML data tree should look like the image on the right
 +
##different images into fore- and background, respectively, and use the fade slider to shift between fore- and background to view the registration quality. You can also use OPTION+CMD key and drag the mouse to fade between back- and foreground.
 +
#to apply the transform and lock in the new position, right click on the volume and select ''Harden Transform'' from the popup menu. The node will move back out to the main level. Note that this applies the transform to the physical orientation info in the image header and does not actually perform a resampling. It is recommended to not apply resampling until the very end of an analysis pipeline, to avoid interpolation-related data loss. To perform a resampling follow steps below:
 +
*'''Phase 4''': Resampling (optional)
 +
#go to [http://wiki.slicer.org/slicerWiki/index.php/Documentation/4.1/Modules/BRAINSResample ''Resample Image (BRAINS)'' module] (in the Registration menu)
 +
##''Image To Warp'': e2_PD
 +
##''Reference Image'': e1_PD
 +
##''Output Image'': create new & rename: e2_PD_Xf1
 +
##''Pixel Type'': ushort
 +
##''Warp By Transform'': Xf1_e21_Affine  created in Phase I above
 +
##''Interpolation Mode'': '''linear'''
 +
#note to avoid multiple resampling, combine the two nested transforms for the two volumes before performing the resampling step above. Alternatively you can execute the ''Harden Transform'' step above first and then resample the volume after, by giving no transform (i.e. resampling in place) but listing the fixed image (e.g. e1_PD) as reference. This will resample the moving image into the same representation as the reference.
 +
before registration: [[Image:RegLib_C04_DataTree.png|200px|Orig. MRML Data tree]]
 +
after registration: [[Image:RegLib_C04_DataTree2.png|200px|Registered. MRML Data tree: exam 2 is within nested affine transforms]]
  
 
=== Registration Results===
 
=== Registration Results===
−
[[Image:RegLib_C02_Unreg_AnimGif.gif||500px|Unregistered Data + segmentation labelmap]]
+
[[Image:RegLib_C04_T1Gd-PD_unreg_AnimGif.gif||500px|Unregistered baseline data: PD vs. T1Gd]] Unregistered baseline data: PD vs. T1Gd<br>
−
[[Image:RegLib_C02_Result_AnimGif.gif||500px|Registration Result: FLAIR + segmentation aligned with SPGR]]
+
[[Image:RegLib_C04_PDunreg_AnimGif.gif||500px|Unregistered followup data: PD exam 2 vs. exam 1]] Unregistered followup data: PD exam 2 vs. exam 1<br>
−
 
+
[[Image:RegLib_C04_T1Gd1-PD1_reg_AnimGif.gif|500px|Registered baseline data]] Registered baseline data <br>
−
=== Download ===
+
[[Image:RegLib_C04_PDreg_AnimGif.gif|500px|Registered followup data]] Registered followup data<br>
−
*[[Media:RegLib_C04_Data.zip‎|'''Registration Library Case 04: MS Multi-contrast series (PD,T2, T1-GdDTPA): Lesion change assessment''' <small> (Data,Presets, Solution, zip file 17 MB) </small>]]
+
[[Image:RegLib_C04_3DLesionChange_AnimGif.gif|200px|Lesion change visualization in 3D]]Lesion change visualization in 3D<br>
−
*download/view guided video tutorial
+
[[Image:RegLib_C04_PDSubtraction.png‎|500px|Lesion change via subtraction imaging of co-registered PD]]Lesion change via subtraction imaging of co-registered PD
−
*download power point tutorial
 
−
 
 
−
[[Projects:RegistrationDocumentation:ParameterPresetsTutorial|Link to User Guide: How to Load/Save Registration Parameter Presets]]
 

Latest revision as of 18:07, 10 July 2017

Home < Projects:RegistrationLibrary:RegLib C04

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updated for v4.1 Slicer4 RegLibLogo.png
Slicer Registration Library Case 04: Multi-contrast brain MRI of Multiple Sclerosis

Input

this is the main fixed reference image. All images are ev. aligned into this space this is the main fixed reference image. All images are ev. aligned into this space lleft this is the intra-subject moving image.
exam 1: PD exam 1: T2 exam 1: T1-Gd
lleft
this is the inter-subject moving image, but also the reference for exam 2 this is the inter-subject moving image, but also the reference for exam 2 lleft this is the moving image.
exam 2: PD exam 2: T2 exam 2: T1-Gd

Slicer 4.1 recommended Modules

Objective / Background

This scenario occurs in many forms whenever we wish to assess change in a series of multi-contrast MRI. The follow-up scan(s) are to be aligned with the baseline, but also the different series within each exam need to be co-registered, since the subject may have moved between acquisitions. Hence we have a set of nested registrations. This particular exam features a dual echo scan (PD/T2), where the two structural scans are aligned by default. The post-contrast T1-GdDTPA scan however is not necessarily aligned with the dual echo. Also the post-contrast scan is taken with a clipped field of view (FOV) and a lower axial resolution, with 4mm slices and a 1mm gap (which we treat here as a de facto 5mm slice). read more about this dataset here

Download

Keywords

MRI, brain, head, intra-subject, multiple sclerosis, MS, multi-contrast, change assessment, dual echo, nested registration

Input Data

  • reference/fixed : PD.1 baseline exam , 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition, RAS orientation.
  • fixed T2.1 baseline exam , 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition, RAS orientation. -> (aligned with PD.1, not used for registering)
  • moving: T1.1 (GdDTPA contrast-enhanced scan) baseline exam 0.9375 x 0.9375 x 5 mm voxel size, axial acquisition.
  • moving: PD.2 follow-up exam 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition.
  • moving: T2.2 follow-up exam 0.9375 x 0.9375 x 3 mm voxel size, axial acquisition. -> same orientation as PD2, will have same transform applied
  • moving:T1.2-GdDTPA follow-up exam0.9375 x 0.9375 x 5 mm voxel size, axial acquisition. -> undergoes 2 transforms: first to PD.2, then to PD.1

Registration Challenges

  • we have multiple nested transforms: each exam is co-registered within itself, and then the exams are aligned to eachother
  • potential pathology change can affect the registration
  • anisotropic voxel size causes difficulty in rotational alignment
  • clipped FOV and low tissue contrast of the post-contrast scan

Key Strategies

  • we first register the post-contrast scans within each exam to the PD
  • second we register the follow-up PD scan to the baseline PD
  • we also move the T2 exam within the same Xform
  • we then nest the first alignment within the second
  • because of the contrast differences and anisotropic resolution we use Mutual Information as cost function for better robustness

Procedure

  1. open the General Registration (BRAINS) module
    1. Fixed Image Volume: e1_PD
    2. Moving Image Volume: e2_PD
    3. Output Settings:
      1. Slicer BSpline Transform": none
      2. Slicer Linear Transform: create & rename new transform: Xf1_e21_Affine.tfm
      3. Output Image Volume: none
    4. Registration Phases: check boxes for Rigid and Affine
    5. Main Parameters
      1. Number of Samples: 200,000
    6. click: Apply; runtime < 1 min (MacPro QuadCore 2.4GHz)
  2. note that the Slicer4.1 version of BRAINS registration does not automatically place the moving volume inside the result transform, as it did for Slicer 3.6. In order to see the result, you must either go to the Data module and drag the moving volume inside the transform node, or select to generate a resampled Output Volume, as done above. The demo movie you can download here shows how to do this.
  • Phase 2: register intra-examT1Gd to PD
  1. open the General Registration (BRAINS) module
    1. Fixed Image Volume: e1_PD
    2. Moving Image Volume: e1_T1Gd
    3. Output Settings:
      1. Slicer BSpline Transform": none
      2. Slicer Linear Transform: create & rename new transform: Xf2_e1-T1Gd_Rigid.tfm
      3. Output Image Volume: none
    4. Registration Phases: check boxes for Rigid only
    5. Main Parameters
      1. Number of Samples: 200,000
    6. click: Apply; runtime < 1 min (MacPro QuadCore 2.4GHz)
  2. note that the Slicer4.1 version of BRAINS registration does not automatically place the moving volume inside the result transform, as it did for Slicer 3.6. In order to see the result, you must either go to the Data module and drag the moving volume inside the transform node, or select to generate a resampled Output Volume, as done above. The demo movie you can download here shows how to do this.
  3. repeat for exam 2, i.e
    1. Fixed Image Volume: e2_PD
    2. Moving Image Volume: e2_T1Gd
    3. Slicer Linear Transform: create & rename new transform: Xf3_e2-T1Gd_Rigid.tfm
  • Phase 3: organize transform hierarchies in the Data tree
  1. go to the Data module
    1. drag the volume node e1_T1Gd onto the transform Xf2_e1-T1Gd_Rigid; let go of the mouse button when you see a hilight box appear around the transform node name
    2. drag the volume node e2_T1Gd onto the transform Xf3_e2-T1Gd_Rigid;
    3. drag the volume node e2_PD and e2_T2 onto the transform Xf1_e21_Affine.tfm
    4. drag the transform node Xf3_e2_T1Gd_Rigid.tfm onto the transform Xf1_e21_Affine.tfm
    5. your MRML data tree should look like the image on the right
    6. different images into fore- and background, respectively, and use the fade slider to shift between fore- and background to view the registration quality. You can also use OPTION+CMD key and drag the mouse to fade between back- and foreground.
  2. to apply the transform and lock in the new position, right click on the volume and select Harden Transform from the popup menu. The node will move back out to the main level. Note that this applies the transform to the physical orientation info in the image header and does not actually perform a resampling. It is recommended to not apply resampling until the very end of an analysis pipeline, to avoid interpolation-related data loss. To perform a resampling follow steps below:
  • Phase 4: Resampling (optional)
  1. go to Resample Image (BRAINS) module (in the Registration menu)
    1. Image To Warp: e2_PD
    2. Reference Image: e1_PD
    3. Output Image: create new & rename: e2_PD_Xf1
    4. Pixel Type: ushort
    5. Warp By Transform: Xf1_e21_Affine created in Phase I above
    6. Interpolation Mode: linear
  2. note to avoid multiple resampling, combine the two nested transforms for the two volumes before performing the resampling step above. Alternatively you can execute the Harden Transform step above first and then resample the volume after, by giving no transform (i.e. resampling in place) but listing the fixed image (e.g. e1_PD) as reference. This will resample the moving image into the same representation as the reference.

before registration: Orig. MRML Data tree after registration: Registered. MRML Data tree: exam 2 is within nested affine transforms

Registration Results

Unregistered baseline data: PD vs. T1Gd Unregistered baseline data: PD vs. T1Gd
Unregistered followup data: PD exam 2 vs. exam 1 Unregistered followup data: PD exam 2 vs. exam 1
Registered baseline data Registered baseline data
Registered followup data Registered followup data
Lesion change visualization in 3DLesion change visualization in 3D
Lesion change via subtraction imaging of co-registered PDLesion change via subtraction imaging of co-registered PD

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