This Bone Analysis tutorial provides step-by-step instructions for segmenting a proximal femur and for computing vector-based fields of anisotropy and scalar-based maps of volume fraction. Additional topics in this tutorial describe how to compute high-resolution maps from data sub-volumes and how to evaluate the computed maps.
Screen capture of the completed tutorial
Before you start the tutorial, you should download and unzip the tutorial dataset — Proximal Femur. This dataset, as well as the session file of the tutorial, are available on the ORS website at: http://www.theobjects.com/dragonfly/learn-sample-datasets.html.
In this section, you will learn how to extract the required region of interest from a threshold range. Instructions for refining the initial segmentation are also included here.
The dataset appears in the Data Properties and Settings panel.




A new region of interest is added to the Data Properties and Settings panel. Information about the new ROI is displayed in the lower section of the Data Properties and Settings (see ROI Properties and Settings).

All unconnected objects that are smaller than the largest object in the region of interest are removed from the ROI (see Processing Islands for more information about this feature).
NOTE With this initial bone segmentation you can separate the cortical shell from the trabecular interior automatically and then compute morphometric indices (see Computing Cortical and Trabecular Measurements).
This section provides step-by-step instructions for computing a 3D vector field-based map of anisotropy.

A new Box shape appears in the Data Properties and Settings panel. Click the Eye icon to show the box in the workspace views.

You can resize and rotate the box, as well as change its position with the available control points (see Editing Shapes).


NOTE Although the highest resolution, or lowest spacing, of the tutorial dataset is 0.5 mm, this should be increased to 2.0 mm if the entire 3D shape of the femur will be mapped.
NOTE The radius of influence defines the kernel size, or elementary volume, within which anisotropy will be evaluated. You should note that a too small radius of influence may result in a low signal-to-noise ratio, while a too high radius can result in averaging and edge effects.


Two new items — a vector field and scalar-based anisotropy dataset — appear in the Data Properties and Settings panel after all computations are complete.

This section describes the different settings that can be applied to the vector field-based anisotropy map that was computed in this tutorial.
Below are some examples of vector field-based anisotropy maps shown from different aspects. The top row includes visualizations of anisotropy magnitude, while the bottom row shows vector fields colored by orientation. The pair of images on the right show maps that are clipped.
The 3D vector field-based anisotropy map appears in the 3D view at the default settings, with the vectors corresponding to the highest surface anisotropy colored yellow and those corresponding to the lowest, or isotropy, colored blue.
Information and settings related to the selected vector-based field appear in the bottom section of the Data Properties and Settings panel.

NOTE In most cases, you should hide the rendering of the dataset if it is visible in the 3D view.


NOTE The Jet LUT is often a good color scheme choice. In this LUT, vectors corresponding to the highest surface anisotropy are colored red, while those corresponding to the lowest, or are isotropic, are colored blue.

The thresholded map shown below shows only low anisotropy areas located near articulating surfaces.
Checking this option will re-code the vector map in accordance with the orientation of the vectors — red for the X axis, green for the Y axis, and blue for the Z axis.
This section of the proximal femur tutorial describes how to compute high-resolution anisotropy maps in different orientations. You should note that when computing high-resolution maps, you should limit the volume of operation to regions that enclose only part of the region of interest. This can be done by creating a series of boxes that describe a particular orientation.
The images below (from left to right) correspond to XZ, XY, and oblique orientations. The computed vector fields are colored by magnitude.
High-definition vector field-based surface anisotropy maps
You can resize and rotate the boxes, as well as change their position within a 2D or 3D view with the control points (see Editing Shapes).

NOTE You can decrease the sampling spacing and radius of influence to 0.0005 m and 0.0015 m, respectively, since you will be computing maps within a sub-volume.
This section of the tutorial describes how to use the Bone Analysis module for 3D volume fraction plotting. You should note that volume fractions are scalar maps.
Comparison of volume fraction scalar map (on left) with vector-based field of anisotropy (on right)
NOTE You can also use any of the boxes you created previously in this tutorial to map volume fraction.


When processing is complete, the volume fraction dataset appears on the Data Properties and Settings panel.
NOTE In most cases, the Jet LUT provides good visualizations of volume fraction.