22. Coregister with AutoFocus

22.1. About Coregistration with AutoFocus

AutoFocus is a scheme that iteratively optimizes a voxel-based similarity measure between two images, such as mutual information. Coregistration with AutoFocus is a powerful and versatile coregistration method that can be used for coregistering images from different imaging sessions and different modalities. For images acquired in the same session, see the basic method for coregistration using DICOM tags.

This section describes AutoFocus coregistration for 3D images. FireVoxel also offers AutoFocus with motion correction for 4D coregistration.

22.2. Basics of AutoFocus

During coregistration, the source image is transformed and superimposed onto a fixed image called the target image, which remains unchanged.

Coregistration with AutoFocus may use a target ROI enclosing the organ or tissue of interest to restrict coregistration and speed up processing. Coregistration can be performed without the target ROI, but it may take much longer than with the ROI, because coregistration is a computationally intensive task.

The target ROI can be created on the target image manually (using the Paintbrush tool) or automatically, using ROI operations or segmentation tools, such as EdgeWave.

The coregistration algorithm searches for a transformation that best matches the source and the target volumes. This group of commands offers a choice of transformations ranging from simple translations to affine transform. The matching of the volumes is based on optimizing a similarity measure (with a selection for different scenarios).

The transformation is computed in two stages, AutoFocus and Fine-tuning:

1. AutoFocus. The algorithm constructs a variety of transformations with combinations of parameters that span a multidimensional grid. The transformations include translation, scaling, rotation, and shear. The transformations are ranked by how well they match the two volumes based on the similarity measure. A user-selected number of the best transformations is retained for the second, fine-tuning stage.

2. Fine-tuning. The algorithm performs iterative adjustment of the best transformation parameters until it finds a local optimum of the similarity measure. Finally, the transformed source image is interpolated and saved as a new layer in the target image window.

22.3. Using Coregistration with AutoFocus

Here we will describe coregistration with AutoFocus using Register > Mutual Information with AutoFocus. Other variants of AutoFocus commands are applied similarly.

1. Open the target window in another document window.

2. Open the target window in another document window.

3. Define a rough target ROI around the organ or tissue of interest. You may use manual or automatic segmentation tools (Fig. 22.1).

Manual ROI. Use Layer Control > New ROI 3D to create a new ROI layer. Use the Paintbrush tool (Ctrl+Left mouse) to draw a rough contour around the organ of interest. Define these contours on every few slices (e.g., on every 5th slice). Next, use ROI > Morphology > Fill 2D Contours and Morph Convex to fill the contours and extend the ROI across slices. The resulting ROI should fully enclose the organ or tissue of interest.

Target image with target ROI and source image

Fig. 22.1 Target image with target ROI (MR, right) and source image (CT, left).

4. Select the target window as the active window and select the base image as the active layer. Select Register > Mutual Information with AutoFocus.

5. If the active layer is an ROI layer, a warning will be shown alerting the user to this fact (Target is ROI. Proceed?).

6. If the active layer is the base image (acquired image), a dialog window will open to adjust parameters (3D Registration with AutoFocus).

7. Check Use Target ROI box (checked by default if a visible ROI is present). If no visible ROI is present, this box is grayed out.

8. Select suggested AutoFocus and Finetune parameter (Fig. 22.3). It is recommended to start with AutoFocus: Translation and Rotation only and Finetune: Transform > Rigid.

9. Click OK to start processing. Registration will commence. Its progress will be indicated in the bottom right corner of the software window.

10. When registration is completed, a new layer with coregistered image will be created in the target window (Fig. 22.2).

Coregistration with AutoFocus results

Fig. 22.2 Coregistration with AutoFocus results.

11. Check registration accuracy using Layer Control > Alpha slider to adjust the transparency of the coregistered layer.

12. If registration accuracy is unacceptable, repeat steps 5-11 and adjust the coregistration parameters. As the first step, increase Power before adjusting other parameters.

22.4. 3D Registration with AutoFocus Dialog

If suitable source and target images are present, Register > Mutual Information with AutoFocus command opens a dialog panel (3D Registration with AutoFocus, Fig. 22.3). The components of this dialog are described below.

If one of the volumes is missing, an error message is shown (No suitable source volume is found).

3D Registration with AutoFocus dialog

Fig. 22.3 3D Registration with AutoFocus dialog.

22.4.1. Load Initial Transformation

Loads transformation information from a previously saved Volume Transform File (*.VTF). Enter a path to the .VTF into the text box or click Browse to open browse-for-file dialog to select a previously saved transform file.

Dicom tagsADD DETAIL.

22.4.2. Save Final Transformation

Save the final transformation as *.VTF. Enter the path to the file or click Browse to open browse-for-file dialog to navigate to the destination directory and enter a file name.

22.4.3. ROI (option)

Use Target ROI (checkbox) – If checked, the target ROI is used for coregistration. If a visible ROI is present in the target window, by default its name will be displayed here. If the ROI is absent or invisible, this option is grayed out.

Inflate/units – Text box and drop-down menu to select the grow distance (in voxels OPTION: MILLIMETERS?) by which the target ROI should be inflated by the Grow command.

22.4.4. Measure (options)

This block contains a dropdown menu with a selection of similarity measures and related settings. The similarity measures range from simple to complex and powerful:

  • Signal Difference (simple)

  • Cross Correlation

  • Image Ratio Uniformity

  • Mutual Info

  • Mutual Info Normalized

  • URAL

  • URALTAU (advanced)

Related settings include:

  • MI bin number – Number of bins in the mutual information method

  • Source Noise – Noise level in the source image (default: measured automatically)

  • Target Noise – Noise level in the target image (default: measured automatically).

URAL Settings – Opens dialog (Multiscale Texture Gradient, Fig. 15.6) to adjust texture edge detector parameters for URAL and URALTAU methods.

22.4.5. AutoFocus (options)

Subsample [1, 8] – Default 3

Translation max {X, Y, Z} – Default: {30, 30, 30}

Scale Deformation matrix – Default: {0, 0, 0}, Grid: 1

Uniform scale (checkbox) – Default – unchecked

Rotation angle max (deg) – Default: {0, 0, 0}, Grid: 1

Shear Magnitude max [0, 10] – Default: 0, Grid: 1

22.4.6. Finetune (options)

Power [0, 1000] – Default: 20. Suggested starting value: 2-3

Multipass (checkbox) – Default: unchecked

Transform (dropdown menu) – Dropdown menu with a selection of transform types: Translation, Rigid (default), Affine, Quadratic, Polynomial Multipass.

22.4.7. Output (options)

Interpolation (dropdown menu) – Nearest neighbor, Tri-linear (default), Wsinc2, Wsinc3, Wsinc4

Reslice Target to Source (checkbox) – Default: unchecked.

22.5. Signal Difference with AutoFocus

Shortcut for coregistration with Similarity Measure: Signal Difference

22.6. Cross Correlation with AutoFocus

Shortcut for coregistration with Similarity Measure: Cross Correlation

22.7. URAL with AutoFocus

Shortcut for coregistration with Similarity Measure: URAL

22.8. Slice-by-slice with AutoFocus

Requires source and target images of the same dimensions.