Modules:MRIBiasFieldCorrection-Documentation-3.6

MRIBiasFieldCorrection corrects for the intensity inhomogeneity in 3D MRI images. Left image: input image with intensity inhomogeneity from lower corner to higher corner. Right image: output image where the intensity inhomogeneity has been corrected.

General Information

Module Type & Category

Authors, Collaborators & Contact

Sylvain Jaume: MIT CSAIL
Nicolas Rannou: ISEN IRISA
Nicholas Tustison: UPenn (N3 and N4)
Ron Kikinis: Harvard Medical School SPL
Contact: sylvain at csail.mit.edu

This work was presented at the MIT NAMIC Week in 2009.

Module Description

MRIBiasFieldCorrection corrects for the intensity inhomogeneity due to MRI gain field distortion. It first estimates the bias field and then 'subtract' it from the image. This module implements the so called methods 'N3' and 'N4' (see references at the bottom of this page).

Usage

Step 1/24. To open the input image, click on 'File' and then 'Add Volume':
- Input Image: input image
- Mask Image: label map providing an approximative segmentation of the ROI
- Output Volume: corrected image
Step 1/24. Add the input volumes
- Step 2/24. Select the input image and click Apply:
  - Input Image: input image
  - Mask Image: label map providing an approximative segmentation of the ROI
  - Output Volume: corrected image
  Step 2/24. Select the input image
  - Step 3/24. The input image appears in the axial-sagittal-coronal views:
    - Input Image: input image
    - Mask Image: label map providing an approximative segmentation of the ROI
    - Output Volume: corrected image
    Step 3/24. Image displayed in the orthogonal views
    - Step 4/24. Select the mask image. Check the box 'Label Map' and click Apply:
      - Input Image: input image
      - Mask Image: label map providing an approximative segmentation of the ROI
      - Output Volume: corrected image
      Step 4/24. Select the mask image as a label map
      - Step 5/24. The label map is overlaid on the axial-sagittal-coronal views:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 5/24. Label map overlaid on the input image
        
        Step 6/24. Click on Modules and scroll-down the menu:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 6/24. List of Slicer 3.6 modules
        
        Step 7/24. Select 'Filtering' and then 'MRIBiasFieldCorrection':
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 7/24. Select MRIBiasFieldCorrection in Filtering
        
        Step 8/24. The User Interface of MRIBiasFieldCorrection appears:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 8/24. User interface of MRIBiasFieldCorrection
        
        Step 9/24. Select the input image in the list of loaded volumes:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 9/24. Select input among loaded volumes
        
        Step 10/24. Select the mask image in the list of loaded volumes:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 10/24. Select mask among the loaded label maps
        
        Step 11/24. Create a new node for the output in Slicer MRML tree:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 11/24. Create node in MRML tree for output
        
        Step 12/24. Click 'Apply' to run the N3 algorithm (default):
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 12/24. Run the N3 algorithm
        
        Step 13/24. 'Status Running' means the algorithm is running. Note 'Running. ' in lower bar:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 13/24. Status info when algorithm is running
        
        Step 14/24. 'Status Completed' means the algorithm is done. Note 'Completed' in lower bar:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 14/24. Status info when algorithm is done
        
        Step 15/24. Click on the lower left tab to disable the overlay of the mask image:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 15/24 Remove the mask overlay
        
        Step 16/24. Switch to 'None' to remove the overlay. The output image appears in the orthogonal views:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 16/24. Switch to 'None'
        
        Step 17/24. Disable the overlay for the sagittal and coronal views:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 17/24. Disable all overlays
        
        Step 18/24. Click on the 'closed eye' icon to display the output image in the 3D rendering window:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 18/24. Click on the 'closed eye' icon
        
        Step 19/24. Click on the other 2 'closed eye' icons to display the 3 orthogonal slices in the 3D window:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 19/24. Click on the other 2 'closed eye' icons
        
        Step 20/24. Hold down your mouse in the 3D window and move to rotate and zoom out:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 20/24. Hold down and move your mouse in the 3D window
        
        Step 21/24. Go to 'File' and 'Save' to save the output volume:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 21/24. Click 'File' and then 'Save'
        
        Step 22/24. Select the files you want to save. By default the XML description and the output volume are selected:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 22/24. Select the files you want to save
        
        Step 23/24. Click on the output directory to save to a different directory:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 23/24. Click on the directory name
        
        Step 24/24. For more information, click on the Help tab. The blue link will send you to this page:
        
        Input Image: input image
        
        Mask Image: label map providing an approximative segmentation of the ROI
        
        Output Volume: corrected image
        
        Step 24/24. Click on the Help tab
        Cheat Sheet
        
        Load the input dataset (Main menu: Add Volume)
        
        Create a Mask Volume using the Editor (Modules > Editor) (the Threshold tool should give a good result)
        
        Select the MRIBiasFieldCorrection module (Modules > Filtering > MRIBiasFieldCorrection)
        
        In the left panel, select the Input Volume
        
        Select the Mask Volume
        
        In the Preview Volume menu , select 'Create New Volume'
        
        Do the same for the Output Volume menu
        
        Modify the parameter values if desired (default values gave good results during our experiments)
        
        Click on Apply.
        
        Close-up of the above screenshot showing the image before and after correction. Note that the intensity inhomogeneity visible in the input image (left) has been corrected in the output image (right).
        
        It took 32 min to process a 512x512x30 MRI volume on a MacBook laptop. To visualize the result:
        
        Deselect the Overlay volume,
        
        Select the input as Background volume and the output as Foreground volume.
        
        Go to Modules > Volumes
        
        Select the input volume, and write down the values for Window and Level
        
        Select the output volume, and apply the same values
        
        Move the lower left cursor in 'Manipulate Slice Views' between B (background) and F (foreground)
        
        Tests
        
        On the Dashboard, these tests verify that the module is working on various platforms:
        
        MyModuleTest1 MyModuleTest1.cxx
        
        MyModuleTest2 MyModuleTest2.cxx
        
        Known bugs
        
        Links to known bugs in the Slicer3 bug tracker
        
        Usability issues
        
        Follow this link to the Slicer3 bug tracker. Please select the usability issue category when browsing or contributing.
        
        Source code & documentation
        
        Links to the module's source code:
        
        More Information
        
        Acknowledgment
        
        This work is part of the National Alliance for Medical Image Computing (NAMIC), funded by the National Institutes of Health through the NIH Roadmap for Medical Research, Grant U54 EB005149 (PI: Ron Kikinis).
        
        References for algorithms implemented in this module
        
        A Nonparametric Method for Automatic Correction of Intensity Nonuniformity in MRI Data, J.G. Sled, A.P. Zijdenbos, and A.C. Evans, IEEE Transactions on Medical Imaging, 17(1):87–97, Feb 1998.
        
        N4ITK: Improved N3 Bias Correction, N.J. Tustison, B.B. Avants, P.A. Cook, Y. Zheng, A. Egan, P.A. Yushkevich, J.C. Gee, IEEE Transactions on Medical Imaging, Vol 99, April 2010.
        
        N4ITK: Nick's N3 ITK Implementation for MRI Bias Field Correction, N. Tustison, J. Gee, Insight Journal, 2009.
        
        References for related algorithms
        
        Parametric Estimate of Intensity Inhomogeneities Applied to MRI, M. Styner, C. Brechbhuler, G. Szekely, and G. Gerig, IEEE Transactions on Medical Imaging, 19(3):153–165, Mar 2000.

Modules:MRIBiasFieldCorrection-Documentation-3.6

General Information

Module Type & Category

Authors, Collaborators & Contact

Module Description

Usage

Cheat Sheet

Tests

Known bugs

Usability issues

Source code & documentation

More Information

Acknowledgment

References for algorithms implemented in this module

References for related algorithms