1. 1
Workflow Validation of Marker‐less Cervical Vertebrae
Tracking using X‐Ray Reconstruction of Moving Morphology
Hana Y. Boudlali | Arin M. Ellingson, Ph.D.
Minnesota Rehabilitation Biomechanics Lab, University of Minnesota
BMEn 4710 Undergraduate Directed Research, University of Minnesota Dept. of Biomedical Engineering
Project summary
This project aims to validate marker‐less cervical
vertebrae tracking workflow for the purpose of
streamlined and accurate acquisition of spinal
kinematics via X‐Ray Reconstruction of Moving
Morphology techniques. Three dimensional
anatomical models of each of the seven cervical
vertebrae were created from segmentation of a CT
scan in three planes. High speed biplane
radiograph images were collected during dynamic
movement of the cervical vertebrae. Correction of
the inherent distortion from fluoroscopy was
applied to the data, and a direct linear transform
was used to configure the vertebral bodies to a
three dimensional coordinate system based on
known calibration markers in the biplane. Image
registration between the 3D vertebrae model and
the high‐speed dynamic motion scans in two
planes allowed for three dimensional kinematic
tracking of the vertebral bodies. This workflow
was carried out and documented in order to
minimize error and streamline the data analysis
process for future studies. The biplane
fluoroscopy data analysis outlined in this paper
and in other project documentation can be applied
to enhance studies concerned with the movement,
form, and rehabilitative applications of spinal
biomechanics.
Background
X‐Ray Reconstruction of Moving Morphology, or
XROMM [2], is a three dimensional fluoroscopy
analysis process that combines computed
tomography (CT) scans and biplane dynamic
motion radiography to track and create
animations of skeletal movement in 3D space.
Accurate, noninvasive motion analysis would be a
beneficiary to the field of spinal biomechanics.
Before 2010, the majority of skeletal kinematic
data was acquired via markers attached to the skin
or tight clothing [5],[8]. This process can produce
poor motion analysis results due to skin‐slip,
which occurs when the marker on the skin does
not move cohesively with the bone below it. In
addition to skin‐slip, many bones are too deep to
obtain reasonable estimates their kinematics.
Radio‐opaque bone markers can be implanted
directly onto a bone of interest and tracked
through dynamic motion [9]. This procedure has
produced outcomes with great precision [2],
however a non‐invasive technique would be more
favorable in clinical applications. The emergence
of x‐ray motion analysis techniques addressed the
issue of external marker slip and invasive bead
tracking [1],[4],[7]. However, the majority x‐ray
motion studies have been limited to two
dimensions [2]. The development and application
of XROMM to spinal biomechanics as a method of
three dimensional kinematic tracking offers the
possibility of more accurate results due to the fact
that very few spinal movements occur in only two
dimensions.
Objectives
The goal of this project is to validate and create
workflow streamlines of marker‐less 3D motion
analysis procedures for future spinal
biomechanics studies. This paper will aim to
outline the XROMM analysis techniques that were
implemented and confirm its application to
tracking the dynamic motion of cervical vertebrae.

2. 2
Materials and methods
The application of XROMM techniques to cervical
vertebrae kinematics was validated in this project
via the collection of data from a cadaver model.
This report will outline each step of the methods
conducted to track dynamic cervical vertebrae
motion in three dimensional space.
3D Anatomical Modeling
A computer tomography (CT) scan of the cadaver
specimen of interest was obtained. The scan was
imported into the Mimics Innovation Suite, a
software that uses 2D anatomical image stacks to
create 3D models. Each of the seven vertebrae of
the cervical spine were isolated into individual
masks by thresholding and segmenting the CT
scan in the axial, coronal, and sagittal planes
(Figure 1).
A variety of functions were applied in order to
create anatomically correct masks of the vertebral
bodies. Thresholding was utilized to create an
“initial guess” for the bone structures. This
function made it possible to fill in bone anatomy
based on pixel brightness, where a low threshold
corresponds to dark pixels and a high threshold
corresponds to bright pixels. Manual editing via
Multiple Slice Edit in Mimics was employed to
make corrections that thresholding cannot
automatically detect. A single vertebral body can
be separated from the rest of the bone anatomy
using the ‘erase’ function on each image slice in
Multiple Slice Edit. This process was streamlined
by using the ‘interpolation’ function, which applies
an edit to image slices between two designated
edits. Corrective functions such as Cavity Fill,
Smoothing, and Wrap Mask were used as needed
to create anatomically correct three dimensional
models.
A Boolean function was applied to each vertebral
mask to create new masks which were completely
filled in black except for at the pixels at the bone of
interest. These segmentations were exported
from Mimics as DICOM files and uploaded to
ImageJ as a stack of images. This stack of images
was exported from ImageJ as a single Tiff file
which included each slice of the vertebral body of
interest.
Figure 1: Segmentation of the seven
cervical vertebrae in the sagittal
(top), coronal (middle), and axial
(bottom) planes from a CT scan
imported into Mimics.

3. 3
Biplane Fluoroscopy
A high‐speed biplane fluoroscopy system is set up
in the Minnesota Rehabilitation Biomechanics
Laboratory on the University of Minnesota
Riverside Medical Campus (Figure 2). Two x‐ray
sources with image intensifiers and high‐speed
cameras are configured to intersect planes (Figure
3). A specimen was positioned at the intersection
of the two x‐ray beams while high‐speed images
were collected in the two planes. Dynamic
movement trials including flexion‐extension,
circumduction, and lateral bending of the cervical
spine were collected using a cadaver specimen.
Flexion‐extension data was collected at from two
oblique perspectives in order to test for optimal
cervical vertebrae visibility throughout the range
of motion (Figure 4). Fluoroscopy images were
collected using a variety of photon energies to
determine optimal visibility while minimizing
radiation exposure to the specimen. The two 2D
coordinate systems from each camera can be
converted to a 3D global coordinate system via
data analysis techniques.
Imaging trials were also performed using a Ferlic
Wedge filter (Figure 5)[12], which was inserted
into the collimator rails of the x‐ray source. X‐ray
filters are used in clinical settings to improve
image quality and reduce radiation exposure to
the patient by attenuating low‐energy x‐rays [6].
Figure 4: Flexion‐extension motion data was
collected from two oblique angles as shown above.
The beam angle between the two x‐ray sources was
60 degrees.
Figure 2: Minnesota Rehabilitation Biomechanics
Laboratory’s biplane fluoroscopy system
Figure 3: Overhead schematic of the biplane
fluoroscopy system
Figure 5: Wedge x‐ray filter by Ferlic Filter Co., LLC [12]

4. 4
Undistortion and Calibration
Image distortion due to inherent fluoroscopy
error was corrected computationally using
XMALab [13]. XMALab was also used as a
calibration tool to relate the biplane images to a
three dimensional coordinate system.
Radiograph images of an undistortion grid and
calibration reference cube were collected at a
variety of photon energy levels in order to
determine the fluoroscopy settings that minimize
residual calibration error.
XMALab uses an algorithm that compares the
spaces between punctures on a linearly spaced
grid, and creates a transformation matrix that can
be applied to other images. Radiograph images
were collected in the biplane setup with a metal
‘undistortion grid’ containing circular punctures
over each image intensifier. These images were
uploaded into XMALab, which digitally computes
the centroid of each puncture (Figure 5). False
positive centroids detected along the edges of the
image were manually toggled. The centroids that
were removed from the undistortion calculation
include those that are not well defined or that are
cut off in the image. The red crosses in Figure 6
are examples of centroids that were toggled.
Because the circles around the periphery of the
image are cut off, the true centroid of the shape
should be shifted outward. Removing these false
centroids reduces error in the undistortion
calculation. Radiograph image undistortion can
be computationally corrected by correlating the
known puncture shape to the warped puncture on
the scan.
A calibration cube (Figure 7) constructed from
Plexiglas and 64 radio‐opaque beads separated by
known distances was used to determine the three
dimensional location of objects in the biplane. The
calibration cube contained four planes with 16
beads on each plane. Fluoroscopy images of the
cube were collected in the biplane setup, which
were then imported into XMALab (Figure 8).
Figure 5: Screenshot of XMALab undistortion interface after centroid detection has been
performed.
Figure 6: the red crosses are centroids that have
been removed due to cut‐off calculation error

5. 5
Imaging trials were repeated at different cube
orientations and with a calibration cube that had a
different Plexiglas thickness in order to determine
the parameters that minimize residual calibration
error. The 3D locations of the markers are
specified in a file that was uploaded into XMALab.
The 2D location of the calibration beads were
related to a global coordinate system using a
direct linear transform (DLT). XMALab semi‐
automates the calibration process by determining
DLT coefficients based on four reference points on
the calibration cube.
Distortion correction was applied to the
radiograph image sequences of the cadaver
specimen in XMALab. The undistorted images
were converted to 8‐bit resolution using a
MATLAB script. MayaCam files and a file
containing the 11 DLT coefficients were exported
from XMALab. The MayaCams specify the position
and orientation of the cameras, in addition to the
dimensions and position of the imaging plane.
Figure 7: Schematic of the calibration cube.
Figure 8: Radiograph images of the calibration
cube uploaded into XMALab. The extraneous
markers in the images were used as references to
locate the radio‐opaque beads

6. 6
3D Tracking and Animation
Three dimensional tracking of cervical spinal
kinematics was performed using Autoscoper [13].
The 3D vertebral body volume file that was
created in Mimics was uploaded into Autoscoper,
as well as the MayaCam files and the undistorted
8‐bit fluoroscopy sequences. Filter settings in
Autoscoper were chosen to optimize the visibility
of the 3D bone model. The bone model was then
shape‐matched to the fluoroscopy images over the
duration of the sequence using the translation and
rotation functions (Figure 9).
Results
3D Anatomical Modeling
The three‐plane cervical vertebrae masks that
were created in Mimics from a CT scan were
converted into a three dimensional model in order
to visualize anatomical correctness. Figure 10
displays a qualitative validation of the 3D
modeling techniques that were employed in this
project.
Undistortion and Calibration
Data was collected for eight calibration cube
orientations, and for the undistortion grid at three
different fluoroscopy settings. The error residuals
for each combination of calibration cube and
undistortion grid scan was calculated in order to
determine optimal calibration settings and
workflow, which can be found in Table 1. GridB
and GridC were obtained using the same x‐ray
photon energy, but the grid was rotated on the
image intensifier between trials. The lowest
residual error was calculated for the GridA –
CubeF combination. GridA was obtained using a
tube voltage of 50 kV, a tube current of 80 mA, and
an exposure time of 10 ms. CubeF was obtained
using a tube voltage of 42 kV, a tube current of 50
mA, and an exposure time of 20 ms.
Figure 10: The colorful spine model on the left is the 3D
anatomical model of the cervical spine that was created in
Mimics from a CT. The spine model on the right was used for
anatomical comparison [3].
Figure 9: The 3D bone model in the global
coordinate system (top), and the C4 bone
model in the 2D image plane in Camera 1
(middle) and Camera 2 (bottom).

7. 7
Two different calibration cubes were studied in
order to determine if the residual calibration error
was significantly different between the two
devices. CubeA through CubeB in Table 1
correspond with the “Thin” relative Plexiglas
thickness in Figure11, and CubeD through CubeH
correspond to the “Thick” relative Plexiglas
thickness. The residual error for the “Thin” cube
was 0.72 +/‐0.087, and the residual error for the
“Thick” cube was 0.66 +/‐0.056 (Figure 11).
The relationship between x‐ray tube voltage and
the residual calibration error is plotted in Figure
12. There is a strong linear relationship between
the two parameters, with an R squared value of
0.9826 for a linear fit. It is anticipated that this
linear relationship would not hold true for all
values; the image would eventually saturate and
the calibration residuals would no longer decrease
linearly.
Fluoroscopy Perspectives
The cadaver model was imaged from two different
oblique perspectives. These perspectives were
explored in order to determine parameters for
optimal cervical spine visibility. One frame from
trials in each perspective are displayed in Figure
13 and Figure 14.
Table 1: Residual calibration error for each undistortion grid ‐ calibration cube combination. The
first number in each cell corresponds with the error for Camera 1 in the biplane fluoroscopy
setup, and the second number corresponds with Camera 2.
y = ‐0.004x + 0.8508
R² = 0.9826
0.5
0.55
0.6
0.65
0.7
0.75
20 40 60 80 100
Residual Calibration Error
Tube Current [mA]
Effect of X‐Ray Tube Current for Cube
Imaging on Residual Error

8. 8
Discussion
Project Concepts
Fluoroscopy distortion correction is an important
process to ensure imaging accuracy. Inherent
fluoroscopy can occur due to a number of factors.
A common mode of fluoroscopy distortion is
pincushion distortion, which occurs when an x‐ray
bean is projected onto a curved surface. Another
form is s‐shaped, or spiral, distortion. This occurs
due to the presence of external magnetic fields,
either from the Earth’s natural fields or from other
electronic equipment [2]. Geometric
representations of these two forms of image
distortion are shown in Figure 15 [11]. Other
factors that may lead to image distortion include:
imprecision in the electronic focusing mechanism,
imperfections in the camera, or due to the system
temperature.
The calibration process is another important
concept in the workflow of this project. The
calibration cube is used as a reference to relate the
two dimensional fluoroscopy coordinate system
to a three dimensional global coordinate system
via a direct linear transform (DLT). The DLT
method correlates the known 3D location of
markers on the calibration cube to the location of
the markers on the fluoroscopy image. The
Figure 13: A radiograph frame
collected with the specimen facing
the x‐ray sources with beams coming
in at a 60 degree oblique angle.
Figure 14: A radiograph frame
collected with the specimen facing
lateral to the x‐ray sources with beams
coming in a 60 degree oblique angle.
Figure 15: Geometric representations of pincushion
distortion (left) and s‐shaped distortion (right) [11]

9. 9
transformation can be applied to any image,
therefore giving the ability to track spinal
kinematics in three dimensions based on 2D
radiograph images. The origin of the “object space
reference frame” coordinate system is marker 1
on the calibration cube, which functions as the
global coordinate system. The coordinate system
in the 2D “image plane reference frames” have an
origin at the principal point (Figure 16) [10].
Reduction of Residual Calibration Error
A variety of variables were altered throughout
the undistortion and calibration process in
order to determine parameters that would
minimize the residual calibration error. One
parameter that was observed was the effect of
rotating the undistortion grid. GridB and
GridC were obtained using the same x‐ray
photon energies, but the grids on both image
intensifiers were rotated between the two
trials. There was not a statistically significant
difference in the residual calibration error
between these two trials. This could be due to
the fact that the centroid calculation for
undistortion in XMALab does not change
based on rotational differences in position. If
an imperfection in the metal undistortion grid
existed, rotating the grid and moving the
position of the imperfection would not change
the undistortion calculation. Another
parameter that was observed was the
thickness of the Plexiglas planes on the
calibration cube. It was predicted that a
thicker plane would correspond with a lower
residual calibration error because the added
mechanical support would decrease the
likelihood of the plane warping. A student’s t‐
test was performed on the residual errors
between cubes with thick and thin planes, and
it is concluded that there is not a statistically
significant difference between the two
calibration cubes at a 95% confidence interval.
The high variability in the residual values is
due to the changing x‐ray energies between
trails. If the cube devices had been the only
variable altered between trials, a difference in
the residual error might have been found.
Conclusions and future directions
The objective of this project was to validate
the application of X‐Ray Reconstruction of
Moving Morphology techniques to three
dimensional dynamic tracking of the cervical
vertebrae. Throughout the project, workflow
documents were created in order to
streamline each step of the process for future
studies and applications. In the future, a
shape‐matching program called Joint Track
will be considered to replace Autoscoper. The
algorithms in Joint Track could be a more
precise way to shape‐match and minimize
human error. The project will also be
expanded to include lumbar vertebrae
kinematics. The application of XROMM
techniques to spinal biomechanics will be used
to understand how specific vertebrae move in
relation to each other. In order to do this, the
three dimensional global coordinate system
will be converted into a three dimensional
local coordinate system with the origin on
specific markers on the bone of interest. A
method to precisely track the dynamic motion
of the spine will have extensive applications in
rehabilitative care and physical therapy.
Figure 16: The two dimensional and three
dimensional reference frames involved in the
calibration process [10].

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