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Revision as of 19:20, 20 February 2013
Home < Documentation < Nightly < Modules < SlicerModuleCardiacRegistrationIntroduction and Acknowledgements
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Authors: Salma Bengali, Alan Morris, CARMA Center, University of Utah | |
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Module Description
This module implements case-specific registration for cardiac LGE-MRI, MRA and CT images acquired at different time points or across different patients. The types of registration scenarios are described here. The inputs to this module are two image volumes: image volume one is the fixed volume, and image volume two is the moving volume. The goal of registration is to find a spatial mapping that will align the moving volume with the fixed volume. The user selects the imaging modalities based on the types of input image volumes. Registration parameters are tuned for the registration cases. Input image volumes that are cropped to the area around the left atrium can be used as well, which would improve the final registration result. Advanced registration parameters can be changed to check if the registration result shows improvement.
Use Cases
Tutorials
The image volumes in this dataset: CARMA Registration Tutorial Data can be used as inputs to the module. The dataset contains anonymized LGE-MRI, MRA and CT images.
Panels and their use
The following are the inputs and outputs:
- Image Volume One (Fixed): Select the LGE-MRI, MRA or CT Image
- Image Volume Two (Moving): Select the LGE-MRI or MRA image
- Output Image Volume : The registration result
- Imaging modality: The types of image volumes that are being registered (e.g LGE-MRI, MRA, CT, Acute Scar)
The advanced registration parameters are:
- Registration Type
- Metric Type
- Initialization Type
- Interpolation Type
- Maximum Number of Iterations
- Sampling Ratio
- Sample from Fixed/Moving Image Overlap
The details about the advanced registration parameters can be found at http://www.slicer.org/slicerWiki/index.php/Modules:RegisterImages-Documentation-3.6.
Similar Modules
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References
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Information for Developers
The source code for this module can be found at the CARMA GitHub repository.
