X-ray Reconstruction of Moving Morphology (XROMM) is a computational process that combines two kinds of data: movement data from x-ray video and shape data from bone scans. There are two, fundamentally different ways to combine these data — marker-based XROMM and markerless XROMM.

Marker-based XROMM

At Brown University we have developed a set of software tools for marker-based XROMM (Brainerd et al., 2010). The new XMALab software (developed by Dr. Ben Knorlein), and instructions may be found in the XMALab bitbucket repository and XMALab bitbucket wiki. The Maya XROMM Tools and instructions (Maya Embedded Language (MEL) scripts) may be found on the XROMM Wiki.

In marker-based XROMM, three or more radio-opaque markers are surgically implanted into the bones of interest. The 3D movements of the radio-opaque markers are tracked in biplanar x-ray movies, and then the XYZ coordinates of the marker sets are used to calculate the rigid body motions of each bone. These rigid body transformations are then applied to 3D models of the bones to reconstruct the 3D movements of 3D bones in an animation environment (such as Maya).

The development of marker-based XROMM at Brown has built on previous work on dogs (Tashman and Anderst, 2003) and humans (Tashman et al., 2007). In the field of orthopedic biomechanics, marker-based methods are often called Dynamic Radiostereophotogrammetric Analysis (Dynamic RSA) (Papaioannou et al., 2009; Tashman et al., 2007).

Markerless XROMM

Bone movement data from x-ray video and shape data from bone scans can also be combined without implanted markers. The 3D pose of the bones is recovered from the biplanar videos either manually (by hand and by eye) or with autoregistration software. Scientific Rotoscoping is one type of markerless XROMM analysis, in which multi-bone, articulated models are manually aligned to x-ray video and standard (visible light spectrum) video of animal movement (Gatesy and Alenghat, 1999; Gatesy et al., 2010). More automated methods for aligning (registering) markerless bone models to biplanar x-ray movies are also emerging (Bey et al., 2008; Bey et al., 2006; You et al., 2001), with great promise for increasing the throughput of markerless XROMM analysis. These methods for markerless autoregistration build on previous work registering the movement of knee replacement devices in human patients to single plane x-ray movies (Banks and Hodge, 1996; Zuffi et al., 1999).

At Brown we have developed streamlined methods for Scientific Rotoscoping with biplanar x-ray videos in the animation program Maya. We have also developed AutoScoper for automated rotoscoping, i.e. automated alignment of models to x-ray videos. See GitHub Autoscoper page for Scientific Rotoscoping and AutoScoper information and downloads.


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