Comput Methods Biomech Biomed Engin. 2017 (Mar 15): 1-9
Christian Balkovec, Jim H. Veldhuis, John W. Baird,
G. Wayne Brodland & Stuart M. McGill
Department of Kinesiology,
University of Waterloo,
The motions of individual intervertebral joints can affect spine motion, injury risk, deterioration, pain, treatment strategies, and clinical outcomes. Since standard kinematic methods do not provide precise time-course details about individual vertebrae and intervertebral motions, information that could be useful for scientific advancement and clinical assessment, we developed an iterative template matching algorithm to obtain this data from videofluoroscopy images.
To assess the bias of our approach, vertebrae in an intact porcine spine were tracked and compared to the motions of high-contrast markers. To estimate precision under clinical conditions, motions of three human cervical spines were tracked independently ten times and vertebral and intervertebral motions associated with individual trials were compared to corresponding averages. Both tests produced errors in intervertebral angular and shear displacements no greater than 0.4° and 0.055 mm, respectively.
When applied to two patient cases, aberrant intervertebral motions in the cervical spine were typically found to correlate with patient-specific anatomical features such as disc height loss and osteophytes. The case studies suggest that intervertebral kinematic time-course data could have value in clinical assessments, lead to broader understanding of how specific anatomical features influence joint motions, and in due course inform clinical treatments.
Keywords: Vertebral tracking, fluoroscopy, cross-correlation, intervertebral motions, intervertebral shear
From the FULL TEXT Article:
Everyday activities rely on movements of the spine, especially
its cervical and lumbar regions. The intervertebral
joint motions through which these gross movements arise
can be affected by a variety of factors, including pain (de
Vries et al. 2015), muscle activation irregularities (Stokes
et al. 2011), and degeneration (Lao et al. 2015); and aberrant
segmental movements are a potential source of pain
(Breen et al. 1989; Kirkaldy-Willis & Farfan 1982). There
is evidence that irregular motions in one intervertebral
joint can affect adjacent joint motion (Rubertι et al. 2009),
motion irregularities in the lower lumbar spine can produce
compensatory effects in the upper lumbar spine (Lee
et al. 2015), and that induced motion irregularities can
contribute to joint deterioration (Kirkaldy-Willis & Farfan
Tools to measure these motions include X-rays, which
provide detailed information about the geometry, aberrant
features, and positions of individual vertebrae, but only
for select static postures. Radiation doses preclude taking
more than end-of-travel images, and information about
possible mid-range motion aberrations (Panjabi 1992) is
not available. Motion time-course data can be obtained
using skin-mounted markers, but in the context of the
spine, they provide mainly gross displacement information.
Videofluoroscopy, a less common approach, provides
similar information to X-rays but in real time. It allows
vertebral positions to be observed throughout their course
of motion though, compared to X-rays, the images provide
less detail about geometry and structural anomalies,
and they contain noise which is quantum in nature and
typically modeled as a Poisson-distribution (Cerciello
et al. 2012; Cesarelli et al. 2013). A typical videofluoroscopy
study can produce several hundred images, and it
becomes challenging to track the motions of individual
vertebrae through these manifold images accurately.
A variety of approaches have been used to track
vertebrae in videofluoroscopy movies. Manual methods
(Breen et al. 1989; Cholewicki et al. 1991) were the
first to be applied, but the resulting data depend on
the skill of the operator and the approach is not practical
for large data sets. Computer algorithms that used
template-matching techniques represented a significant
advancement (Muggleton & Allen 1997; Bifulco et al.
2001). However, vertebrae are surrounded by muscles and
other structured soft tissues, and motions of these tissues
relative to the vertebrae can affect the apparent shape of
the vertebrae (Wallace & Johnson 1981) leading to tracking
errors. Point feature tracking (Wong et al. 2009),
landmark-finding algorithms (Wong et al. 2006), and
automated segmentation (Zheng et al. 2004) can largely
overcome the optical reshaping problem and facilitate
vertebral tracking, but these techniques can be difficult
to program. Cross-correlation techniques (Bifulco et al.
2001; Cerciello et al. 2011) are easier to implement, but
the rigid templates they typically use can be particularly
subject to deflection by soft tissue.
Vertebrae can also be imaged using videofluoroscopy in multiple planes and
tracked in three dimensions (McDonald et al. 2010; Lin
et al. 2014). This work has shown that during typical flexion
and extension activities, motions are largely confined
to the sagittal plane, with peak out-of-plane movements
representing approximately 10% of the magnitude of the
desired in-plane sagittal motion (McDonald et al. 2010).
Since flexion and extension are the actions of greatest clinical
interest and since multi-planar imaging systems are
relatively rare, we chose to focus on sagittal-plane data,
noting that this approach allows retrospective analysis of
a large body of existing clinical and research data.
While we present anatomic abnormalities in this work,
cadaver investigations have examined instantaneous
center of rotation as a metric for identifying dysfunction
and damage in spinal soft tissue (Brown et al. 2005;
Subramanian et al. 2007; Hwang et al. 2008); this has also
been applied to fluoroscopy sequences in vivo (Bifulco
et al. 2012). Coupled with intervertebral angular displacement
measures, this technique could also serve to provide
enhanced clinical information in the future.
Dynamic motion analysis via videofluoroscopy could
bring new understanding of how anatomical aberrations,
intervertebral motions and dysfunction are related. The
purpose of this investigation was to develop, test, and validate
a multi-step template algorithm (Figure 1) for tracking
sagittal plane vertebrae motions in videofluorographs.
Materials and methods
Tracking individual vertebrae in videofluoroscopy images
is challenging due to image distortion, intensity non-linearities,
image noise, poor image contrast, lack of strong
natural fiducials, and soft-tissue-induced changes in vertebrae
appearance. Collectively, these effects cause the
appearance of vertebrae to change between images, making
automated tracking difficult. The multi-step tracking
approach outlined in Figure 1, makes it possible to overcome
Image acquisition and conditioning
All images were taken with a Digital Motion X-ray (DMX)
that provided continuous X-ray at 7090 kVp at 23.5 mA
at a 40-inch flange focal distance. A 9-inch image intensifier
(Precise Optics, Bay Shore, NY, USA) transferred
the signal to a 1024 by 1024 pixel digital CCD camera
outputting a 150dpi stream to a DICOM recorder (NAI
Tech Products, Auburn, CA, USA). Frames were recorded
at 30 Hz. Source to intensifier distance was 100 cm while
patient to intensifier distance was 20 cm, resulting in a
Fluoroscopy images typically contain significant pincushion
distortion (Wallace & Johnson 1981) (Figure 1(A));
a first step was to remove this distortion (Figure 1(B)). For
simplicity, new undistorted images were created rather
than using a geometric correction function to interpret
positions of pixels in the raw source image (Cholewicki &
McGill et al. 1991). Pincushion distortion was corrected
using the formula:
where s is the modified radial distance to a pixel that
was at radius r from the center of the raw image, R is the
radius to a corner of the image, and k is a radial distortion
To experimentally determine an appropriate radial
distortion coefficient k, a wire grid phantom consisting
of regularly-spaced 12.7 mm squares was placed in the
field of view of the fluoroscope equivalent conditions for
imaging patients. The coefficient k was then adjusted until
all squares on the grid appeared to be approximately the
same size (Figure 1(B)) and the lines across the image were
straight. This coefficient was then used to correct all subsequent
images. The radius r in the present images took on a
maximum value of 512√2 pixels and the k value used was
0.13. Although this approach removed nearly all image
distortion, small residual distortions could still be seen
(Figure 2(B)), likely because the image pixels were discretized
to the grid of the original image and Equation (1)
provides only first-order correction. Final corrected
images had a resolution of 1024 Χ 1024 pixels (0.17 mm
To improve the visual contrast of the geometrically-
corrected images, they were enhanced using a smart
sharpen filter (amount: 500%, radius: 20 pixels) in Adobe
Photoshop (Adobe, San Jose, CA, USA) followed by a
Gaussian blur (radius: 1 pixel) (Figures 1(C), 2(C)). This
technique produced favorable tracking outcomes for the
algorithm we present here, whether other techniques
(Cerciello et al. 2012) to address the quantum noise in
fluoroscopy images would also work, remains to be seen.
Procedural overview. |
(A) A raw image from a sagittal-plane sequence involving flexion and extension is shown.
Pincushion distortion is evident in the superimposed yellow grid lines obtained from an orthogonal phantom.
(B) Pincushion distortion is removed using an image distortion correction algorithm (Equation 1).
(C) The contrast of each image is enhanced as described in the text.
Tracking algorithms and parameters
To track the imaged vertebrae, a template-matching algorithm
was used (Velduis & Brodland 1999). The technique
is relatively straight-forward to implement, is well suited
to tracking of semi-rigid objects, and has proven useful
in a range of applications (Bootsma & Brodland 2005;
Wiebe & Brodland 2005) including vertebra tracking
(Muggleton & Allen 1997; Bifulco et al. 2001; Mellor
et al. 2009; Cerciello et al. 2011). The technique considers
a contiguous group of pixels or template in one image
and finds the best set of pixels in another image for it
to overlay. Quality of fit is assessed using a normalized
cross-correlation function. That function multiplies the
intensities of corresponding overlapping pixels, adds these
products and then normalizes the resulting score based
on the average pixel intensities of the template and of the
region it overlies (Velduis & Brodland 1999).
Correlation scores are calculated for each combination of location and
orientation, and the parameter combination that yields
the highest correlation is deemed the best match. Smaller
templates generally have the advantage of high tracking
precision, but if they lock on to a wrong match (i.e. get
lost) the errors can be large. Larger templates significantly
reduce the probability of a wrong match and have the
advantage of tracking average motions reliably, but they
may not follow specific points well, especially in a field
that contains real or apparent deformations of the object
being tracked. To realize both advantages, we developed
a multi-step procedure in which large templates are used
to reliably track bulk motions, followed by smaller, spatially-
constrained templates that can provide tracking
precision in the presence of soft-tissue-induced apparent
In preparation for the first tracking step, an operator
manually selects four widely-spaced points having distinct
visual features, generally the corners of each vertebral
body (Figure 1(D)). A trapezoidal template is constructed
(Figure 1(E)), and extended 20 pixels radially outward
from each corner of the minimal trapezoid that bounds
the four chosen points.
The first tracking step obtains the approximate position
of each vertebra over time. The non-rectangular shape
of the templates, tailored to the shape of each vertebra,
prevents them from overlapping adjacent vertebrae,
and the large size reduces errors associated with residual
image distortion and pixel intensity noise. Reference
templates (which the algorithm used to create matches in
subsequent images) were approximately 150 pixels long
on each side, and a new reference template was defined
every 50 images (new reference template was the previously
matched frame) to reduce tracking error caused by
changes in apparent vertebra shape, but not after every
image, as doing so would unnecessarily increase discretization-
related errors (Velduis & Brodland 1999). Search
parameters included whole-pixel template translations of
up to 31 pixels each way from the previous template position
and rotations in 1° steps up to 8° each way from the
previous template orientation. Visual inspection ensured
the templates matched the general motion of the vertebral
To refine the tracking, successively smaller templates
based on the original large template, were defined automatically
(Figure 1(F)). Briefly, a trapezoidal template is
defined around each individual coordinate and incorporates
the corner of the vertebral body; the coordinates are
free to move independently from one-another. To prevent
these templates from locking onto incorrect matches, they
were constrained to move only 2 pixels in each direction
in sub-pixel increments from the positions they would
be expected to have based on the larger templates in the
previous iteration. The reduced-size templates were not
allowed to rotate from the orientation predicted by the
larger template, and the reference template from the original
image was used throughout the full tracking; it was
not updated, contrary to the reference template update
strategy for the large templates used in the first tracking
step. Typically, three refinement passes were performed,
with the smallest templates nominally 71 by 71 pixels and
tracked to their best locations with 1/8-pixel bias. Raw
coordinate data was smoothed using a 2nd order dualpass
Butterworth filter with a cutoff frequency of 1 Hz as
revealed by residual analysis (Wells & Winter 1980).
The residual analysis revealed the optimal cutoff frequency
where the low-frequency motion signal component in the
data rises above the signal from the noise component.
Vertebrae translations and rotations were then calculated
from the template motions in the final tracking pass
(Figure 1(G)) using a least-squares approach (Veldhuis
et al. 2005); this approach assumed rigidity of the individual
vertebrae. From the computed rotation matrices
of each individual vertebra, the relative orientation
of one vertebra could be computed relative to another
to provide the intervertebral angular displacement at a
joint. Intervertebral joint shear was calculated using a
previously defined method (Frobin et al. 1996). Briefly,
a shear axis was defined which bisected two adjacent
vertebrae. Vectors from the midpoint of the shear axis
to the midpoint
of each vertebra were then defined and
from these, vectors perpendicular to the shear axis were
defined. The distance between these perpendicular vectors
(normalized to their initial distance) was defined as the
intervertebral joint shear.
Procedural overview. |
(D) An operator then selects one point at each of the 4 corners of each vertebral body to define the shape
of the large tracking template.
(E) A computer algorithm generates a large trapezoidal template around each vertebral body based on these
points and a first tracking pass determines the general motions of each vertebra.
(F) Three more refinement passes are performed using smaller templates around each of the original corner points.
The points move independently within a limited range.
(G) Angular and shear displacements are calculated and displayed.
Figure 1G. Closeup
The bias of the tracking algorithm was tested by imaging
a porcine spine specimen rigidly attached to a moving
mechanical arm (Figure 2(A)). The specimen consisted
of three vertebral bodies and two intervertebral discs
(Figure 2(B)). The skin and organs had been removed,
but all muscles and ligaments were left intact. Small surgical
incisions were made so that lead markers could be
rigidly mounted to the anterior aspect of the vertebral
body and the posterior aspect of the spinous process of
each vertebra using cyanoacrylate adhesive. The markers
offered sufficiently high contrast under videofluoroscopy
that a straight-forward cross-correlation function (Velduis
& Brodland 1999) could track them with sub-pixel bias,
providing rotation accuracies of 0.05° (based on two
markers tracked to the nearest 1/8-pixel and separated
by 200 pixels).
Algorithm validation using a porcine spine. |
(A) Three porcine vertebrae with high-contrast lead markers were attached to an arm and moved through the field of view.
(B) Shown, is a porcine spine specimen with pincushion distortion removed but before contrast enhancement. The lead markers used for the reference movements are highlighted as green points.
(C) This figure shows the specimen after contrast enhancement, and ready for machine tracking.
(D) The angular motions of the tracked vertebrae match well with the calculated motions of the lead markers. The maximum RMS error was 0.4° (Table 1).
The soft tissue on the spine specimen was left intact
to better mimic the in vivo situation, making the phantom
vertebrae challenging to track as the attached tissue
blurred the bone edges. Three trials were done, moving
the phantom from one edge of the field of view to the
other. The vertebrae in each of the trials were tracked
independently ten times to provide 30 tracking sets for
statistical analysis. Over the entire analysis, this created 90
independent vertebra tracking trials to assess the efficacy
of the algorithm.
The algorithm was also tested by applying it repeatedly
to videofluoroscopy image sets from human patients.
Three in vivo image sequences of sagittal plane cervical
spine flexion-extension were tracked using the algorithm.
Patients gave informed consent for their image sequences
to be used for research purposes, and all protocols were
approved by the University Research Ethics Committee.
Radiation exposure for a typical image sequence was
between 0.75 and 1.5 mSv. Each sequence was tracked
independently ten times, vertebral rotations and intervertebral
shears were calculated (Figure 3) and tracking consistency
was analyzed statistically.
Algorithm validation using human data.
(A) Angular movement of C2 over time for ten tracking trials .
(B) Similar data for shear displacements between C2 and C3. See Table 5.
To investigate the clinical potential of the approach outlined
here, the algorithm was applied to fluoroscopic image
sets from two additional patients. Both cases had been
assessed by a clinician qualified to make radiological exams,
who noted specific anatomical features and, based on clinical
impression, varying degrees of disc height loss. Each
set consisted of approximately 300 frames (Approximately
10 s of motion, See Supplementary Videos 1 and 2) and
the algorithms outlined in Figure 1 were used to calculate
vertebral motions and intervertebral rotations and shears.
Each patient was asked to start from a neutral position,
flex their cervical spine fully, and then extend it
before returning to the neutral position. To minimize
out-of-plane motions, patients were given the opportunity
to practice the desired motion prior to imaging, and
were supervised by a clinician throughout the process to
ensure that no deviations occurred. Their heads were also
placed close to the image intensifier, and there was a barrier
against their right shoulder to help act as a reference,
guide their movement, and restrict it to the sagittal plane
as closely as possible. In this way, we would expect outof-
plane movement to be lower than what 3D approaches
have previously reported (McDonald et al. 2010), as by
design, movement in these devices cannot be restricted
to the extent presented here while still allowing motion
across three planes.
The average RMS errors in the rotations of the superior,
middle, and inferior vertebrae across the three porcine
trials were 0.230°, 0.400°, and 0.225° compared to the
tracking of the lead markers (Table 1). The RMS errors
in the calculated intervertebral displacements, which
should have all been zero, but are the result of differences
between vertebral displacements and would thus
be expected to have higher errors than them, by a factor
of √2, are 0.376° and 0.355° for the superior and inferior
joints, respectively (Table 2), while the corresponding
RMS joint shears, which should also have been zero were
0.030 and 0.055 mm (Table 3). The differences between
the individual patient trials and the average across all ten
trials were smaller than the highest error in the porcine
spine tracking results (Tables 4 and 5).
Patient Case 1 (40 years, female) presented with no
anatomical abnormalities, but the individual had been in
a motor-vehicle accident and complained of neck pain and
headaches (Figure 4(A)). Figure 4(B) shows the angular
displacements that occurred at each of the intervertebral
joints versus the total degree of neck flexion or extension.
If all joints moved in synchrony and proportion with each
other, these curves would take the form of straight lines,
with slopes proportional to the relative contributions of
the specific joints. As the figure shows, three of the joints
follow this general pattern, while the lowest one (C5/
C6), for reasons that are not apparent, does not. Manual
examination of the associated video confirms the motions
reported in the figure. The curves in Figure 4(B) also show
hysteresis at each joint, a phenomenon shown in in vitro
(Barrey et al. 2015), and identified here for the first time
in vivo. Thus, the time course of the various intervertebral
motions associated with motions away from the neutral
position are not just the reverse of those associated with
returning to it.
Patient Case 2 (68 years, female) had also been in a
motor-vehicle accident and had complaints of neck pain
and headaches. She presented with pronounced anatomical
irregularities that included moderate disc height loss at
C4/C5, associated loss of cervical lordosis, and severe disc
height loss at C5/C6 and C6/C7 (Figure 4(C)). Osteophytic
lipping was also evident at all levels that showed disc
height loss. Supplementary Movie 2 shows the effect of
the loss of cervical lordosis at C4/C5, and how disc height
loss inhibits motion at C5/C6. During neck flexion, there
was no displacement about the C4/C5 joint, a joint that
contributed to the kyphotic deformity, as it was already in
full flexion. During neck extension, the disc height loss at
C5/C6 appeared to cause facet joint contact and prevent
further extension at that joint. Comparison of the motion
graph in Figure 4(D) with that in Figure 4(B) confirms
the profound joint displacement differences between this
patient and Case 1.
Analysis of two patient cases. |
(A) Case 1 (40 years, female) had no disc height loss or anatomical abnormalities, but had complaints of
neck pain and headaches. Tracking points were manually placed on key locations on each vertebra
(green dots) and large trapezoidal templates were built around them for purposes of the initial tracking
pass (yellow outlines). See Supplementary Movie 1.
Displacements of each intervertebral joint is plotted against the sum of the motions in all four joints
(cumulative angle) over a neutral to full flexion to full extension to neutral sequence. All joints demonstrate
some degree of hysteresis with the lowest joint (C5/C6) demonstrating the most hysteresis and difference
from the other joint motions.
Case 2 (68 years, female) presented with moderate height loss at C4/C5 and severe height loss at C5/C6
and C6/C7. As with Case 1, the patient had complaints of neck pain and headaches. Osteophytes were present
at all levels with height loss and there was a kyphotic deformity.
Less flexion motion was observed at the C4/C5 joint as it was already in full flexion due to the kyphosis
(see Supplementary Movie 2). Some flexion motion was observed at the C5/C6 joint, but that joint displaced very
little in extension due to facet joint interaction associated with disc height loss. The majority of motion occurred
about the C3/C4 joint, a level without height loss. All aberrant movements appear to be associated with
This study demonstrates that multi-step template matching
is practical to implement and that it can accurately
track individual vertebrae. Compared to most other
tracking approaches (Zheng et al. 2004; Reinartz et al.
2009; Wong et al. 2009, 2006), template matching is more
straightforward both in concept and implementation, and
although our version was coded in C++, it could be implemented
without difficulty in MATLAB.
The validation protocols were made as rigorous and
relevant to the clinical setting as practical. For example,
substantially-intact porcine cervical spines were used, to
avoid any possible advantages afforded by the clearer vertebral
edges found in excised vertebrae and non-moving
phantoms, or the high-contrast edges often inherent in
mechanical targets. In the porcine study, ground truth was
established using lead markers that could be tracked with
high angular precision (0.05°) to avoid any errors or bias
associated with manual tracking. Then, multiple, separate
motion time courses were determined so that reliable statistical
properties could be derived. In the second phase of
the validation step, human vertebrae were tracked multiple
times for a total of 150 different time courses under
clinical conditions that involved pincushion distortion,
optical nonlinearities, soft tissue motion and associated
variable blurring of the vertebral body edges, and low
image contrast and noise.
Even under these conditions, intervertebral angular
errors of the order of 0.4° and shear errors not larger
than 0.055 mm were realized. The in vivo tracking errors
(Tables 4 and 5) were smaller than in the porcine tests.
These error values compare favorably with tests on static
hard-tissue that produced vertebral angular errors of 0.8°
(Bifulco et al. 2001), 0.6° (Muggleton & Allen 1997), and
0.4° (Cerciello et al. 2011) and mechanical targets which
gave errors of 0.41° (Ahmadi et al. 2009). Additional comparisons
are difficult to carry out because, while we report
intervertebral angles, Wong and colleagues (Wong et al.
2006) reported angular velocities and associated errors,
others (Wong et al. 2009) reported spatial positions and
heir discrepancies relative to manually-identified feature
There are emerging techniques which have focused on
measuring soft-tissue displacements and strains within
the spine under MRI (Chan et al. 2014; Chan & Neu
2014). Further development of the algorithm presented
here could potentially be applied towards tracking these
features as well, given that the core algorithm is simply
matching an image template. In this same fashion, the
algorithm could also be applicable towards 3D fluoroscopy
measurements, expanding its potential use as research
and clinical setups become more sophisticated. Future
development of the algorithm we have outlined here will
hopefully enhance the breadth of its use.
Because patient fluorographs can now be more easily
and precisely analyzed, vertebral motions can be studied
with increased confidence, accuracy, and detail. The resulting
data (such as in Figure 4) has the potential to improve
understanding of the relationship between anatomical
irregularities and dysfunction. In time, we hope to further
understand the relationship between aberrant motions of
individual vertebrae and their relationship to injury risk,
degenerative change, and pain triggers. This could be used
to potentially enhance and inform future clinical practice.
The authors gratefully acknowledge Dr. Edward Cambridge
D.C. for his assistance in grading patient disc height loss. No
commercial relationships or conflicts of interest exist in this
work. Dr. John W. Baird holds the rights to the Digital Motion
X-ray (DMX) trademark in Canada.
This research was funded by Natural Sciences and Engineering
Research Council of Canada (NSERC) Discovery Grants
to SMM and GWB.
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