Download ppt - Using Plastimatch for Deformable Registration

-1-Massachusetts General Hospital National Alliance for Medical Image Computing

Using Plastimatch forDeformable Registration

Gregory C. Sharp

Department of Radiation Oncology

Massachusetts General Hospital

3D Slicer Training Compendium

Tutorial Version 1.0, Apr 26, 2010


Learning Objective

This tutorial is a step-by-step guide, and includes:

1) Downloading the Plastimatch extension to 3D Slicer

2) Loading the sample images

3) Running deformable registration on the CPU

4) Running deformable registration on the GPU

5) Inspecting registration quality in 3D Slicer

The plastimatch web site is: http://plastimatch.org


Prerequisites

This tutorial assumes that you have already downloaded the sample data. You can get the data from here:

http://forge.abcd.harvard.edu/gf/download/frsrelease/85/1004/rider-lung-images.tar.gz


Part 1: Downloading the Plastimatch Extension


(This part of the tutorial might not workcorrectly, pending the Slicer 3.6 release)


Start up 3D Slicer

Choose “Extension Manager”from the “View” menu


Click “Next”


Find the plastimatch plugin,and click “Select”Then, click “Download and Install”


The “Status” should become green

Click “Next”


Restart 3D Slicer


Part 2: Loading the example data


Start up 3D Slicer


Choose “Add data” from the menu


Choose “Add files” in dialog box


Select (highlight) both example files:fix.nrrd and mov.nrrd

Then click “Open”


Click “Apply”


The images are now loaded


Part 3: Visualizing the example data


We want to look at how well the images are aligned before we start

3D Slicer can view a “foreground” (F) and “background” (B) image at the same time. After loading, (F) is set to “None” in all views.


Click, and select “fix” as the foreground image.Repeat for all three views.


Use the “Manipulate Slice Views” slider to blend between foreground and background


We can now see the alignment of the images. To see it better, we need to increase the viewport size.

Click on the layout chooser button


Choose “Red slice only”


Much better! Next we're going to try color blending. Choose the “Volumes module.


We're going to modify the color of the moving volume. Choose “mov”as the active volume.


Set it to “Warm Tint 1”



Part 4: Running Plastimatch


Go back to the module selector


Choose “B-spline deformableregistration” from the “Plastimatch” section


Set “Fixed Volume” to “fix”Set “Moving Volume” to “mov”

Set “Output Volume” to “Create New Volume”


Click “Apply”

(You might need to scroll down)


Check the status in the status bar

With a Tesla C1060 GPU, the registration takes 6 seconds

A laptop might take 1 or 2 minutes


When the registration is complete, the warped image is automatically displayed


You have to set the foreground view again to see the registration quality


Your results should look like this.


Part 5: Optimizing Your Registration


We're going to try to improve the registration result.


Click on “Enable Stage 2”Then click “Apply”

This takes 12 seconds on the Tesla C1060. Might be 3-4 minutes on a laptop.


Like before, the output is automatically loaded.


Your results should look like this.

Note improvement in the alignment of the mediastinum


Part 6: Advanced Plastimatch Options


By default, plastimatch optimizes Mean-squared error (MSE).

But you can choose Mutual Information (MI) instead


By default, plastimatch uses the GPU. But you can choose to use the CPU instead.

Plastimatch CPU uses OpenMP to take advantage of modern multi-core systems

However, in Plastimatch 1.4, mutual information does not take advantage of the GPU, nor is it multi-threaded.


In our tutorial, the images were sufficiently well aligned that we could use B-spline registration.

But if they are not well aligned, you can do a “rough alignment” using translation, rigid, or affine registration.

Click “Enable Stage 0” to enable the rough alignment.


For each stage, you can modify the subsampling rate, grid size, and maximum iterations

Decreasing the subsampling rate improves accuracy

Increasing the subsampling rate improves reliability


Decreasing max iterations improves registration speed

Increasing max iterations improves registration accuracy


Decreasing the grid spacing improves accuracy

Increasing the grid spacing improves reliability


Conclusion

Congratulations! You have completed the tutorial.

Please send corrections or suggestions to:

Greg [email protected]

Or visit the web page at:

http://plastimatch.org


National Alliance for Medical Image ComputingNIH U54EB005149

Acknowledgements

National Institutes of HealthNIH / NCI 6-PO1 CA 21239Federal share of program income earned by MGH on C06CA059267

Progetto Rocca FoundationA collaboration between MIT and Politecnico di Milano