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

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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.  
 
*[[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.  
 
*[[Image:Button_green_moving_white.jpg|20px]] moving: T1 SPGR , 0.9375 x 0.9375 x 1.2 mm voxel size, sagittal, RAS orientation.
 
*[[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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*Content preview: Have a quick look before downloading:  Does your data look like this?  [[Media:RegUC2_lightbox.png]]
 
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*download dataset to load into slicer (~17 MB zip archive)
 
  
 
=== Registration Results===
 
=== Registration Results===
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=== Discussion: Key Strategies ===
 
=== Discussion: Key Strategies ===
 
*the two images have identical contrast, hence we consider "sharper" cost functions, such as NormCorr or MeanSqrd
 
*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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*we have aliasing at the image margins that should be masked out
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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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*the two images are not too far apart initially
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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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*the bone appears largely as signal void, making it hard to distinguish from background
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*these two images are not too far apart initially, so we reduce the default of expected translational misalignment
 
 
*because accuracy is more important than speed here, we increase the sampling rate from the default 2% to 15%.
 
*because accuracy is more important than speed here, we increase the sampling rate from the default 2% to 15%.
 
*we also expect minimal differences in scale & distortion: so we can either set the expected values to 0 or run a rigid registration
 
*we also expect minimal differences in scale & distortion: so we can either set the expected values to 0 or run a rigid registration
 
*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.
 
*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.

Revision as of 22:00, 3 February 2010

Home < Projects:RegistrationLibrary:RegLib C05

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Slicer Registration Use Case Exampe #5: Inter-subject Knee MRI

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 T1 SPGR lleft T1 SPGR
0.9375 x 0.9375 x 1.4 mm
256 x 256 x 112
RAS 		0.9375 x 0.9375 x 1.2 mm
256 x 256 x 130
RAS

Objective / Background

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

Keywords

MRI, knee, inter-subject, segmentation

Input Data

  • Button red fixed white.jpgreference/fixed : T1 SPGR , 0.9375 x 0.9375 x 1.4 mm voxel size, axial, RAS orientation.
  • Button green moving white.jpg moving: T1 SPGR , 0.9375 x 0.9375 x 1.2 mm voxel size, sagittal, RAS orientation.

Registration Results

Download


Discussion: Registration Challenges

  • 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.
  • 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.
  • we have slightly different voxel sizes
  • if the pathology change is substantial it might affect the registration.

Discussion: Key Strategies

  • the two images have identical contrast, hence we consider "sharper" cost functions, such as NormCorr or MeanSqrd
  • we have aliasing at the image margins that should be masked out
  • the two images are not too far apart initially
  • the bone appears largely as signal void, making it hard to distinguish from background
  • because accuracy is more important than speed here, we increase the sampling rate from the default 2% to 15%.
  • we also expect minimal differences in scale & distortion: so we can either set the expected values to 0 or run a rigid registration
  • 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.
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