PURPOSE: Validation of image registration algorithms is frequently accomplished by the visual inspection of the resulting linear or deformable transformation due to the lack of ground truth information. Visualization of transformations produced by image registration algorithms during image-guided interventions allows for a clinician to evaluate the accuracy of the result transformation. Software packages that perform the visualization of transformations exist, but are not part of a clinically usable software application. We present a tool that visualizes both linear and deformable transformations and is integrated in an open-source software application framework suited for intraoperative use and general evaluation of registration algorithms. METHODS: A choice of six different modes are available for visualization of a transform. Glyph visualization mode uses oriented and scaled glyphs, such as arrows, to represent the displacement field in 3D whereas glyph slice visualization mode creates arrows that can be seen as a 2D vector field. Grid visualization mode creates deformed grids shown in 3D whereas grid slice visualization mode creates a series of 2D grids. Block visualization mode creates a deformed bounding box of the warped volume. Finally, contour visualization mode creates isosurfaces and isolines that visualize the magnitude of displacement across a volume. The application 3D Slicer was chosen as the platform for the transform visualizer tool. 3D Slicer is a comprehensive open-source application framework developed for medical image computing and used for intra-operative registration. RESULTS: The transform visualizer tool fulfilled the requirements for quick evaluation of intraoperative image registrations. Visualizations were generated in 3D Slicer with little computation time on realistic datasets. It is freely available as an extension for 3D Slicer. CONCLUSION: A tool for the visualization of displacement fields was created and integrated into 3D Slicer, facilitating the validation of image registration algorithms within a comprehensive application framework suited for intraoperative use.
[1]
Simon R. Arridge,et al.
A survey of hierarchical non-linear medical image registration
,
1999,
Pattern Recognit..
[2]
Andras Lasso,et al.
SlicerRT: radiation therapy research toolkit for 3D Slicer.
,
2012,
Medical physics.
[3]
Kemal Tuncali,et al.
Image registration for targeted MRI‐guided transperineal prostate biopsy
,
2012,
Journal of magnetic resonance imaging : JMRI.
[4]
M. R. Cheung,et al.
Using manual prostate contours to enhance deformable registration of endorectal MRI
,
2012,
Comput. Methods Programs Biomed..
[5]
Karthik Krishnan,et al.
Interactive deformation registration of endorectal prostate MRI using ITK thin plate splines.
,
2009,
Academic radiology.
[6]
Milan Sonka,et al.
3D Slicer as an image computing platform for the Quantitative Imaging Network.
,
2012,
Magnetic resonance imaging.
[7]
D. Hill,et al.
Medical image registration
,
2001,
Physics in medicine and biology.
[8]
Max A. Viergever,et al.
A survey of medical image registration
,
1998,
Medical Image Anal..
[9]
Ron Kikinis,et al.
3D Slicer
,
2012,
2004 2nd IEEE International Symposium on Biomedical Imaging: Nano to Macro (IEEE Cat No. 04EX821).
[10]
Joseph O Deasy,et al.
CERR: a computational environment for radiotherapy research.
,
2003,
Medical physics.