Difference between revisions of "Projects:RegistrationLibrary:RegLib C05"

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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 Use Case Exampe #5: Inter-subject Knee 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 05: <br>  Inter-subject Knee MRI  =
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|[[Image:RegLib C05 KneeMRI1.png|200px|lleft|this is the fixed reference image. All images are aligned into this space]]  
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=== Input ===
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|[[Image:Arrow_left_gray.jpg|100px|lleft]]  
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{| style="color:#bbbbbb; " cellpadding="10" cellspacing="0" border="0"
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|[[Image:RegLib C05 KneeMRI2.png|200px|lleft|this is the moving image. The transform is calculated by matching this to the reference image]]
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|[[Image:RegLib C05 KneeMRI1.png|150px|lleft|this is the main fixed reference image. All images are ev. aligned into this space]]  
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|align="left"|LEGEND<br>
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|[[Image:RegArrow_Affine.png|70px|lleft]]  
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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:RegLib C05 KneeMRI2.png|150px|lleft|this is the moving image]]  
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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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|[[Image:Button_red_fixed.jpg|40px|lleft]]  T1 SPGR
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|subject 1
 
|
 
|
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|[[Image:Button_green_moving.jpg|40px|lleft]] T1 SPGR
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|subject 1
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|-
 
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|0.9375 x 0.9375 x 1.4 mm<br> 256 x 256 x 112<br>RAS
 
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|
 
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|0.9375 x 0.9375 x 1.2 mm<br> 256 x 256 x 130<br>RAS
 
 
|}
 
|}
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=== Modules ===
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*'''Slicer 3.6.1 recommended modules:  [https://www.slicer.org/wiki/Modules:PythonSurfaceICPRegistration-Documentation-3.6 Surface Registration]
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===Objective / Background ===
 
===Objective / Background ===
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The final goal is to align a segmentation prior model to aid in cartilage segmentation.
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The final goal is to align a segmentation prior model/atlas to aid in cartilage segmentation.
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=== Keywords ===
 
=== Keywords ===
 
MRI, knee, inter-subject, segmentation
 
MRI, knee, inter-subject, segmentation
  
 
===Input Data===
 
===Input Data===
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*[[Image:Button_red_fixed_white.jpg|20px]]reference/fixed : T1 SPGR , 0.9375 x 0.9375 x 1.4 mm voxel size, axial, RAS orientation.  
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*fixed: T1 SPGR , 0.9375 x 0.9375 x 1.4 mm voxel size, axial.  + surface model from femur & tibia segmentation.
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*[[Image:Button_green_moving_white.jpg|20px]] moving: T1 SPGR , 0.9375 x 0.9375 x 1.2 mm voxel size, sagittal, RAS orientation.
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*moving: T1 SPGR , 0.9375 x 0.9375 x 1.2 mm voxel size, sagittal. + surface model from femur & tibia segmentation.
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*Content preview: Have a quick look before downloading: Does your data look like this?  [[Media:RegUC2_lightbox.png]]
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=== Procedure / Pipeline ===
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*download dataset to load into slicer (~17 MB zip archive)
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#Open case scene file or import image data + surface models
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#Open ''Registration / Surface Registration'' module
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##Input Surface: S02_femur
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##Target Surface: S01_femur
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##Output Transform: create new transform, rename to "Xf1_surf_S2-S1"
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##Select/check ''Mean distance mode: RMS''
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##Leave defaults: iterations: 50, landmarks: 200
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##Select/check ''Start by matching centroids''
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##Select/check ''Check mean distance''
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##Click: ''Apply''; process duration: ~ seconds
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#Go to the ''Data'' module
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##find the newly created transform node "Xf1_surf_S2-S1" and drag the "S02_femur" model node (and all other S02 entities) inside the transform.
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##for the 3D viewer turn on the visibility of the surface models (should be on already)
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##the surfaces for the two femur models should now align in basic position and orientation
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#for additional details see tutorial downloads below
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=== 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 C05 AGif reg.gif|200px|femur surface models before/after registration]]
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|}
 
  
 
===Download ===
 
===Download ===
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*'''[[Media:RegLib_Case_01_TumorGrowth.zip‎|download entire package  <small> (Data,Presets,Tutorial, Solution, zip file 33.7 MB) </small>]]'''
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*Data
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**[[Media:RegLib_C05_Data.zip‎|'''RegLib_C05_Data''': Example Dataset <small> (MRI images, surface models,Presets, Solution, zip file 9.5 MB) </small>]]
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<!--
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*Presets
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comment
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**[[Media:RegLib_C05_Presets.mrml‎|'''RegLib_C05_Presets.mrml''': registration parameter presets<small> (.mrml text file 12 kB) </small>]]
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-->
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*[[Projects:RegistrationDocumentation:ParameterPresetsTutorial|Link to User Guide: How to Load/Save Registration Parameter Presets]]
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*Documentation
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**[[Media:RegLib_C05_Tutorial.ppt|'''RegLib_C05_Tutorial''': Powerpoint file <small> (.ppt file 1 MB) </small>]]
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=== Discussion: Registration Challenges ===
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**[[Media:RegLib_C05_Tutorial.ppt|'''RegLib_C05_Tutorial''': PDF file <small> (.pdf file 0.5 MB) </small>]]
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*accuracy is the critical criterion here. We need the registration error (residual misalignment) to be smaller than the change we want to measure/detect. Agreement on what constitutes good alignment can therefore vary greatly.
 
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*the two images have strong differences in coil inhomogeneity. This affects less the registration quality but hampers evaluation. Most of the difference does not become apparent until after registration in direct juxtaposition. Bias field correction beforehand is recommended.
 
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*we have slightly different voxel sizes
 
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*if the pathology change is substantial it might affect the registration.
 
  
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=== Discussion: Key Strategies ===
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=== Acknowledgments ===
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*the two images have identical contrast, hence we consider "sharper" cost functions, such as NormCorr or MeanSqrd
 
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*general practice is to register the follow-up to the baseline. However here the follow-up has slightly higher resolution. From an image quality/data perspective it would be better to use the highest resolution image as your fixed/reference. But here follow the most common convention, i.e. fixed image is the baseline.
 
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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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*if the pathology change is soo large that it might affect the registration, we should mask it out. The simplest way to do this is to build a box ROI from the ROItool and feed it as input to the registration. Remember that masking does not mean that masked areas aren't matched, they just do not contribute to the cost function driving the registration, but move along passively.  Next more involved level would be to outline the tumor. If a segmentation is available, you can use that.
 
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*these two images are not too far apart initially, so we reduce the default of expected translational misalignment
 
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*because accuracy is more important than speed here, we increase the sampling rate from the default 2% to 15%.
 
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*we also expect minimal differences in scale & distortion: so we can either set the expected values to 0 or run a rigid registration
 
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*we test the result in areas with good anatomical detail and contrast, far away from the pathology. With rigid body motion a local measure of registration accuracy is representative and can give us a valid limit of detectable change.
 

Latest revision as of 17:06, 10 July 2017

Home < Projects:RegistrationLibrary:RegLib C05

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v3.6.1 Slicer3-6Announcement-v1.png Slicer Registration Library Case 05:
Inter-subject Knee MRI

Input

this is the main fixed reference image. All images are ev. aligned into this space lleft this is the moving image
subject 1 subject 1

Modules

Objective / Background

The final goal is to align a segmentation prior model/atlas to aid in cartilage segmentation.

Keywords

MRI, knee, inter-subject, segmentation

Input Data

  • fixed: T1 SPGR , 0.9375 x 0.9375 x 1.4 mm voxel size, axial. + surface model from femur & tibia segmentation.
  • moving: T1 SPGR , 0.9375 x 0.9375 x 1.2 mm voxel size, sagittal. + surface model from femur & tibia segmentation.

Procedure / Pipeline

  1. Open case scene file or import image data + surface models
  2. Open Registration / Surface Registration module
    1. Input Surface: S02_femur
    2. Target Surface: S01_femur
    3. Output Transform: create new transform, rename to "Xf1_surf_S2-S1"
    4. Select/check Mean distance mode: RMS
    5. Leave defaults: iterations: 50, landmarks: 200
    6. Select/check Start by matching centroids
    7. Select/check Check mean distance
    8. Click: Apply; process duration: ~ seconds
  3. Go to the Data module
    1. find the newly created transform node "Xf1_surf_S2-S1" and drag the "S02_femur" model node (and all other S02 entities) inside the transform.
    2. for the 3D viewer turn on the visibility of the surface models (should be on already)
    3. the surfaces for the two femur models should now align in basic position and orientation
  4. for additional details see tutorial downloads below

Registration Results

femur surface models before/after registration

Download

Acknowledgments

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