Validation of XMALab software for marker-based XROMM

Abstract

Marker-based XROMM requires software tools for: (1) correcting fluoroscope distortion; (2) calibrating X-ray cameras; (3) tracking radio-opaque markers; and (4) calculating rigid body motion. In this paper we describe and validate XMALab, a new open-source software package for marker-based XROMM (C++ source and compiled versions on Bitbucket). Most marker-based XROMM studies to date have used XrayProject in MATLAB. XrayProject can produce results with excellent accuracy and precision, but it is somewhat cumbersome to use and requires a MATLAB license. We have designed XMALab to accelerate the XROMM process and to make it more accessible to new users. Features include the four XROMM steps (listed above) in one cohesive user interface, real-time plot windows for detecting errors, and integration with an online data management system, XMAPortal. Accuracy and precision of XMALab when tracking markers in a machined object are ±0.010 and ±0.043 mm, respectively. Mean precision for nine users tracking markers in a tutorial dataset of minipig feeding was ±0.062 mm in XMALab and ±0.14 mm in XrayProject. Reproducibility of 3D point locations across nine users was 10-fold greater in XMALab than in XrayProject, and six degree-of-freedom bone motions calculated with a joint coordinate system were 3-to 6-fold more reproducible in XMALab. XMALab is also suitable for tracking white or black markers in standard light videos with optional checkerboard calibration. We expect XMALab to increase both the quality and quantity of animal motion data available for comparative biomechanics research.

Dynamic Musculoskeletal Functional Morphology: Integrating diceCT and XROMM

“…This is particularly true for primate feeding—cycle‐to‐cycle differences account for a majority of the kinematic variation during mastication across species and food types, presumably due to changing bolus material properties over the course of the feeding sequence (Reed and Ross, , 2012). Continued advances in semi‐ and fully‐automated marker tracking (Knörlein et al, ) are necessary for these methods to be practically applied to orders of magnitude more trials, as has been done for skeletal kinematics and whole muscle length change with Vicon (Reed and Ross, ; Ross et al, ; Iriarte‐Diaz et al, ). Using more automated methods to segment fascicles from diceCT data sets may reduce post‐scanning processing time and would make the segmentation step of this method more replicable (Kupczik et al, ; Dickinson et al, 2018).…”

“…The biplanar X‐ray videos were saved as .avi files for storage and converted to .tiff stacks locally for digitization using ImageJ v1.51a (National Institutes of Health, Bethesda MD). All videos were undistorted, calibrated, and digitized using XMALab v1.4.0 (Knörlein et al, ), which generates both the XYZ coordinates of individual markers and rigid body transformations for markers assigned to the same bone. The XYZ coordinates and transformation matrices were filtered at 20 Hz after visually confirming in XMALab's built‐in plotting features that this frequency did not over‐smooth the data.…”

“…Measuring the length of muscles attaching to the hyoid required reconstructing hyoid rigid body kinematics. Operating under the assumption that the markers are rigidly fixed to that bone, the XMALab algorithm that produces the rigid body transformation matrices minimizes error among all markers within the same bone (Knörlein et al, ). However, the assumption of rigidity was violated in the hyoid of each animal because inter‐marker distances were observed to vary regularly during feeding.…”

Experimental determination of three-dimensional cervical joint mobility in the avian neck

“…XMA Lab 1.3.2 [61] was used for distortion correction, 3-D calibration, marker tracking, rigid body calculation, and filtering. A Butterworth filter with a frequency cutoff of 5 Hz was applied to rigid body animations to reduce noise.…”

In vivo fluoroscopic kinematography of dynamic radio-ulnar incongruence in dogs

“…The software measures marker trajectories representing motion of the implanted markers over time by tracking the tantalum beads in both x-ray videos. Together with the bead coordinates extracted from the CT data, 6-degree rigid-body motion (3 x translation and 3 x rotation) of radius and ulna in 3D space were calculated (Knorlein et al, 2016). Within Autodesk Maya® (Autodesk Inc., San Rafael, CA, USA), a 3D computer animation software, the calculated rigid-body motion was then applied to the previously generated CT bone models of radius and ulna, resulting in a 3D animation of radius and ulna which precisely mirrors the kinematics of both bones when the dog was walking on the treadmill.…”