J. Biomedical Science and Engineering, 2013, 6, 175-184 JBiSE
http://dx.doi.org/10.4236/jbise.2013.62021 Published Online February 2013 (http://www.scirp.org/journal/jbise/)
Micro-computed tomography assessment of human
femoral trabecular bone for two disease groups (fragility
fracture and coxarthrosis): Age and gender related effects
on the microstructure
Ana Catarina Vale1,2, Manuel F. C. Pereira3, António Maurício3, Bruno Vidal2, Ana Rodrigues2,4,
Joana Caetano-Lopes2, Ara Nazarian5, João E. Fonseca2,4, Helena Canhão2,4, Maria Fátima Vaz1,6*
1Institute of Materials and Surface Science and Engineering, Lisbon, Portugal
2Rheumatology Research Unit, Instituto de Medicina Molecular, Faculdade de Medicina de Lisboa, Lisbon, Portugal
3Center of Petrology and Geochemistry, Instituto Superior Técnico, Universidade Técnica de Lisboa, Lisbon, Portugal
4Rheumatology and Metabolic Bone Diseases Department, Hospital de Santa Maria, Lisbon, Portugal
5Orthopedic Biomechanics Laboratory, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, USA
6Mechanical Engineering Department, Instituto Superior Técnico, Universidade Técnica de Lisboa, Lisbon, Portugal
Email: *fatima.vaz@ist.utl.pt
Received 16 November 2012; revised 15 December 2012; accepted 22 December 2012
The aim of this study was to identify three-dimen-
sional microstructural changes of trabecular bone with
age and gender, using micro-computed tomography.
Human trabecular bone from two disease groups,
osteoporosis and osteoarthritis was analyzed. A prior
analysis of the effects of some procedure variables on
the micro-CT results was performed. Preliminary
micro-CT scans were performed with three voxel
resolutions and two acquisition conditions. On the
reconstruction step, the image segmentation was per-
formed with three different threshold values. Samples
were collected from patients, with coxarthrosis (os-
teoarthritis) or fragility fracture (osteoporosis). The
specimens of the coxarthrosis group include twenty
females and fifteen males, while the fragility fracture
group was composed by twenty three females and
seven males. The mean age of the population was 69 ±
11 (females) and 67 ± 10 years (males), in the cox-
arthrosis group, while in the fragility fracture group
was 81 ± 6 (females) and 78 ± 6 (males) years. The 30
μm voxel size provided lower percentage difference
for the microarchitecture parameters. Acquisition
conditions with 160 µA and 60 kV permit the evalua-
tion of all the volume’s sample, with low average val-
ues of the coefficients of variation of the microstruc-
tural parameters. No statistically significant differ-
ences were found between the two diseases groups,
neither between genders. However, with aging, there
is a decrease of bone volume fraction, trabecular
number and fractal dimension, and an increase of
structural model index and trabecular separation, for
both disease groups and genders. The parameters
bone specific surface, trabecular thickness and degree
of anisotropy have different behaviors with age, de-
pending on the type of disease. While in coxarthrosis
patients, trabecular thickness increases with age, in
the fragility fracture group, there is a decrease of
trabecular thickness with increasing age. Our find-
ings indicate that disease, age and gender do not pro-
vide significant differences in trabecular microstruc-
ture. With aging, some parameters exhibit different
trends which are possibly related to different mecha-
nisms for different diseases.
Keywords: Trabecular Bone; Micro-Computed
Tomography; Coxarthrosis; Fragility Fracture; Age;
As for other materials, the mechanical properties of bone
depend on its structural characteristics. Trabecular bone
is formed by an interconnected network of rods and
plates and can be found at the epiphysis of long bones
and in the vertebral body. Almost all fragility fractures
occur at regions with trabecular bone, for which the tra-
becular microarchitecture was affected by a disease mecha-
nism. In this sense, the bone structural characterization,
in particular of trabecular bone, is fundamental to assess
the risk of fracture and to help in the prevention of bone
*Corresponding author.
A. C. Vale et al. / J. Biomedical Science and Engineering 6 (2013) 175-184
Osteoarthritis (OA) and osteoporosis (OP) are pa-
thologies that affect the quality of life of patients. Os-
teoporosis occurs due to the discrepancy between bone
formation and bone resorption, which may lead to bone
loss, associated to an increase on the risk of bone failure.
In this sense, the trabecular microarchitecture is an im-
portant determinant of osteoporosis. Osteoarthritis is a
chronic inflammatory disease which mainly affects the
cartilage of the joint compromising the bone properties,
whose influence on the microstructure of bone is not
very well known.
Micro-computed tomography (micro-CT) has become
an important tool to the visualization and quantification
of the three-dimensional (3D) structure of bone [1-15]
instead of inferring the properties from 2D measurements,
which is the case of histomorphometric evaluation. Nev-
ertheless, there are very good correlations between his-
tomorphometric and microtomographic analysis [16,17].
Micro-CT has been used to evaluate differences of tra-
becular microstructure, e.g. between several age groups
[18-21] at different anatomical locations [18,22,23]. As
osteoporosis generates morphological alterations of the
bone structure, an accurate study of trabecular bone mi-
croarchitecture can be performed by micro-CT to study
the origin of the disease, as well as, to evaluate its evolu-
tion [24-34]. Additionally, some studies have been fo-
cused on trabecular bone samples from animals or hu-
mans with disorders associated to osteoarthritis [30,34-
38]. Micro-CT is also used to assess microarchitectural
changes during mechanical testing [39-42].
Micro-CT is widely used to evaluate the microstruc-
ture of trabecular bone and is regarded to be a gold stan-
dard technique. Depicted the number of papers on this
subject, some aspects may be missing. Although some
works refer to the comparison between diseases as os-
teoporosis and osteoarthritis [30,35], while others men-
tion the age and gender effect on the structure [20,21] a
survey of the literature did not allow to find any combi-
nation of the age, gender and disease.
Although, there are studies on the micro-computed
tomography applied to trabecular bone, which deal with
the impact of the procedure variables on the accuracy of
the measurements, each case is a different case with its
own particular characteristics depending on the apparatus
properties, process variables and sample properties [2,43,
44]. Prior to the application of the micro-CT technique to
a large number of samples, a study on the effects of some
procedure variables is advantageous. In this work, an
optimization of the micro-CT parameters with emphasis
to the voxel size, voltage, intensity and threshold values
was performed.
The aim of this study was to identify three-dimen-
sional (3D) microarchitectural changes of trabecular bone
simultaneously with age, gender, and two different pa-
thologies, coxarthrosis (CA) and fragility fracture (F)
using micro-computed tomography (micro-CT). We hy-
pothesized that, in the diseased samples, there would be
age-related changes in the trabecular bone microarchi-
tecture, in similitude to age effects in healthy bone.
2.1. Materials
Femoral epiphyses were collected from patients submit-
ted to hip replacement surgery in the Orthopedic De-
partment of Hospital de Santa Maria, Lisbon, Portugal.
Two pathologies were evaluated, namely coxarthrosis
which is a particular form of osteoarthritis, and fragility
fractures, which probably occurred due to osteoporosis.
Trabecular bone cylinders were drilled to extract cylin-
der-shaped samples of 5 mm in diameter and approxi-
mately 15 - 20 mm in length. This study was approved
by local Ethics Committee and followed the International
Guidelines stated by Declaration of Helsinki (Seoul,
2008). Patient’s agreement to these experiments was ob-
tained by written informed consent.
Bone cylinders were prepared following a procedure
which included five steps: fixation, dehydration, clearing,
impregnation and inclusion [45]. During the fixation phase,
the sample is placed in alcohol 70% for a minimum of 72
hours at 5˚C, after which they were dehydrated in etha-
nol 96% to 100%, over a period of 24 hours, at 5˚C. On
the third step, samples were cleared in order to replace
the alcohol by an intermediate solution of xylene for 24
hours at 5˚C. Then, the specimens were embedded in
methylmethacrylate (MMA) for a minimum of 72 hours
at 20˚C, after which they were included in the polymer
at a constant temperature between 5˚C and 10˚C, to po-
lymerize [46].
Initially, three trabecular bone samples from patients
with CA were studied, which belong to two male patients
with mean age of 64 ± 7 years, and one female with 61
years to establish imaging parameters to be used in this
study. Then, a total of sixty-five samples were analyzed
and two bone diseases were compared, using thirty-five
coxarthrosis specimens (20 females with mean age of 69
years, 5 males with mean age of 67 years) and thirty fra-
gility fracture specimens (23 females with mean age of
81 years, 7 males with mean age of 78 years).
2.2. Procedure Variables in the Micro-CT
The goal of the first part of the work was to establish an
optimized methodology to measure 3D trabecular bone
structural characteristics, in order to apply it to a wider
sample population. For this purpose a set of three sam-
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A. C. Vale et al. / J. Biomedical Science and Engineering 6 (2013) 175-184 177
ples was used. Measurements were determined three times
for each of the samples, under the same testing condi-
Micro-CT analysis involves three main steps: acquisi-
tion, reconstruction and image analysis [3]. In the acqui-
sition stage, the variables to be chosen are the X-ray tube
potential, voxel size, and location of the volume of inter-
est [3]. On the reconstruction phase, the binarization (or
segmentation) is carried out, separating bone from no
bone regions. Despite the number of methods found on
the literature, no completely reliable method of bone
segmentation has been established and an optimal thresh-
olding protocol should be proposed for each case studied.
In general, for bone samples, local threshold are pre-
ferred, especially for comparison studies with histomor-
phometric evaluation [16]. On the third phase, the recon-
structed image data can be interpreted with a 3D analysis
software that enables the quantification of the bone mi-
crostructure parameters. Although several parameters may
be used to characterize the 3D structure of trabecular
bone, the most widely used are [3,47,48]: Percent bone
volume or Bone volume fraction (BV/TV, %), Bone spe-
cific surface (BS/BV, mm1), Structure model index (SMI,
none), Trabecular thickness (Tb.Th, mm), Trabecular
number (Tb.N, mm1), Trabecular separation (Tb.Sp,
mm), Fractal dimension (FD, none), and Degree of ani-
sotropy (DA, none).
The micro-CT study was performed on a SkyScan
1172 device (SkyScan, Kontich, Belgium) [49]. The ap-
paratus is controlled by a computer programmed with the
SkyScan software package, which is based on Feldkamp
algorithm [1]. Figure 1 shows an example of the shadow
image (or projection image), the reconstructed slice, the
region of interest (ROI), the binarized region, and the 3D
rendering (i.e., manipulation in a virtual space) of a hu-
man trabecular bone sample.
The acquisition protocol involves several scan parame-
ters, some of which were changed to verify their influ-
ence in the results. The X-ray projection images were
collected under two conditions of X-ray voltage and X-
ray current denoted respectively by A) and B), where A)
corresponds to 160 µA and 60 kV and B) to 100 µA and
100 kV. Images were acquired over an angular range of
180˚ with an angular step of 0.45˚, with scanning times
around 30 min. The tested values of the image pixel size
were 10, 15 and 30 μm. During the micro-CT scan, each
Figure 1. (a) Projection image; (b) Reconstructed slice; (c)
Region of interest (ROI); (d) Binarized section; and (e) 3D
sample was entirely contained in the field of view (FOV)
and a stack of 1000, or more, cross-section images was
obtained with a slice to slice increment of 10 μm. The
projection images were stored in TIFF file format, as
16-bit shadow images, in a size of matrix around 640 ×
512 pixels.
The size and position of the volumes of interest were
chosen by the operator to obtain the maximum possible
volume. Samples were only repositioned between each
image acquisition and the analysis was conducted by the
same operator.
Following scanning, the projection images were re-
constructed, in about 500 slices along the ZZ axis, by
using the cone-beam reconstruction software NRecon
(SkyScan, Kontich, Belgium) [49]. The segmentation
method applied is based on the local minimum of the
grayscale bimodal histogram function of the stack of
slices images corresponding to the volume of interest.
Three different threshold values on the histogram func-
tion were tested which correspond to 1) the grey value of
the local minimum (gmin); 2) the minimum value minus 5
units of the grey level scale (gmin 5); and 3) the mini-
mum value plus 5 units (gmin + 5). Each sample has a
unique gmin and therefore the threshold values were dif-
ferent for distinct samples. An example is given in Fig-
ure 2.
On the third step, the reconstructed slice images were
processed, quantified and interpreted by means of 3D
image analysis software (CTAn and ANT software, Sky-
Scan, Kontich, Belgium). This 3D rendering enabled the
determination of the previously mentioned structural
parameters for the analysis of trabecular structure.
The reproducibility of measurements of the morpho-
logical parameters was assessed by determining these
parameters, making three scans for each sample, under
the same acquisition conditions, and calculating the co-
efficients of variation, CV(%) given by Eq. 1. To com-
pare the results of the two scanning conditions (p.A, p.B),
the percent difference, ΔDiff (%), was also evaluated
with the Eq. 2.
CV%Standard DeviationMean (1)
ΔDiff %200p.Ap.Bp.Ap.B (2)
A comparison between the two conditions A) and B)
for the three voxel sizes (10, 15 and 30 μm), is evaluated
by the ΔDiff (%) values, which are illustrated on Figure
In general, the percentage difference for the microar-
chitectural parameters is lower for the results obtained
with 30 µm voxel size, with the exceptions of BV/TV
and DA. Some works report that the scanning of large
specimens may require the use of special resolutions
correspondent to voxel sizes greater than 100 μm [2].
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A. C. Vale et al. / J. Biomedical Science and Engineering 6 (2013) 175-184
Copyright © 2013 SciRes.
Figure 2. Effect of the grayscale histogram thresholding value on the binarized ROI: (a) gmin 5; (b) gmin; and (c) gmin + 5, where
gmin is the grey level correspondent to the minimum (A scan conditions: 160 µA and 60 kV, 30 μm pixel size).
energy (50 to 90 kV) [3].
We determined the threshold influence on a set of
three samples, using three values of the global grayscale
histogram, gmin 5, gmin and gmin + 5. Table 2 quantifies
the threshold effect on the binarized BVI for one sample,
where the microarchitectural parameters determined un-
der different values of the grayscale histogram threshold,
are shown. The average of the three results is very simi-
lar to the results obtained with the gmin value. The CVs
associated with the measurements are in the interval of
0.5% to 8%. The higher CV, around 8%, was also ob-
tained for BV/TV, while the coefficient of variation for
BS/BV and Tb.Th varied between 6% to 7%. For SMI,
the CV value is 4.36% and the coefficients for BS/TV,
Tb.N, Tb.Sp, DA and FD are equal or lower than 2%.
Our values are in the range of a similar work performed
by Beaupied et al. [44]. We decided to use values of gmin
which provides results similar to the average of the re-
sults obtained with these three threshold values.
Figure 3. Percent difference, ΔDiff (%), between the mean
values from the two acquisition conditions, A and B, for
three voxel sizes (10, 15 and 30 μm).
However, for voxel sizes larger than 100 μm, the mi-
croarchitectural parameters are strongly dependent on the
voxel size [2]. As, when the voxel size decreases, the
resolution increases, it is convenient to choose a voxel
size lower than 100 μm, which allows scanning the entire
sample’s volume. The voxel size of 30 µm was the ideal
value for our samples. 2.3. Micro-CT Imaging
Table 1 presents the microarchitectural parameters,
for the three samples, determined under scan conditions
A and B, with a voxel size of 30 µm. The average values
of the coefficients of variation are almost the same either
for settings A or B conditions.
Taking into account the preliminary results obtained in
the study, the optimal parameters for scanning and re-
construction of trabecular bone were chosen as: acquisi-
tion conditions of voltage equal to 60 kV, intensity of
160 μA, 30 µm of voxel size and the threshold value
adjusted at the minimum of the global grayscale histo-
gram from each specimen evaluated.
As there is not much influence of the acquisition volt-
age and current on the results, A conditions were chosen
to evaluate the rest of the samples, as it allows to evalu-
ate all the volume’s sample and reduces the artifacts.
This is in accordance with authors that recommend the
use of micro-CT works at the medium range of X-ray
2.4. Statistical Analysis
First, a Shapiro Wilk test was conducted to evaluate the
A. C. Vale et al. / J. Biomedical Science and Engineering 6 (2013) 175-184 179
Table 1. Effect of the scan acquisition conditions A and B (A: 160 µA/60 kV, B: 100 µA/100 kV) on the microarchitectural parame-
ters for three different samples (Mean ± SD values and CV), for a voxel size of 30 μm.
Sample 1 Sample 2 Sample 3
Scan conditions Parameter Mean ± SD CV (%) Mean ± SD CV (%) Mean ± SD CV (%)
BV/TV(%) 27.12 ± 0.050.18 31.87 ± 0.060.19 25.80 ± 0.08 0.31
BS/BV (mm1) 13.60 ± 0.040.29 14.59 ± 0.050.34 18.55 ± 0.05 0.27
SMI 0.98 ± 0.04 4.08 1.05 ± 0.09 8.57 1.16 ± 0.07 6.03
Tb.Th (mm) 0.25 ± 0.03 12.00 0.24 ± 0.03 12.50 0.20 ± 0.03 15.00
Tb.N (mm1) 1.08 ± 0.02 1.85 1.30 ± 0.03 2.31 1.31 ± 0.05 3.82
Tb.Sp (mm) 0.64 ± 0.02 3.13 0.49 ± 0.01 2.04 0.51 ± 0.01 1.96
DA 2.04 ± 0.01 0.49 1.96 ± 0.01 0.51 2.12 ± 0.01 0.47
FD 2.25 ± 0.00 0.00 2.28 ± 0.00 0.00 2.25 ± 0.01 0.44
average 2.75 3.31 3.53
BV/TV (%) 27.29 ± 0.050.18 29.10 ± 0.070.24 27.22 ± 0.05 0.18
BS/BV (mm1) 13.52 ± 0.040.30 15.02 ± 0.050.33 17.30 ± 0.06 0.35
SMI 0.95 ± 0.04 4.21 1.17 ± 0.06 5.13 1.02 ± 0.12 11.76
Tb.Th (mm) 0.25 ± 0.03 12.00 0.24 ± 0.03 12.50 0.21 ± 0.04 19.05
Tb.N (mm1) 1.08 ± 0.02 1.85 1.21 ± 0.03 2.48 1.30 ± 0.05 3.85
Tb.Sp (mm) 0.63 ± 0.01 1.59 0.51 ± 0.02 3.92 0.50 ± 0.02 4.00
DA 2.25 ± 0.00 0.00 2.00 ± 0.00 0.00 2.18 ± 0.01 0.46
FD 2.25 ± 0.00 0.00 2.26 ± 0.00 0.00 2.27 ± 0.01 0.44
average 2.52 3.08 5.01
Table 2. Microarchitectural parameters determined under the
same acquisition conditions (15 μm voxel size, scan acquisition
condition A and with different values of the grayscale histo-
gram threshold, namely, gmin 5, gmin and gmin + 5, where gmin
is the grayscale minimum (Mean, standard deviation, SD, and
coefficient of variation, CV).
min 5 gmin g
min + 5 Mean SD CV (%)
BV/TV (%) 35.04 31.32 30.14 32.17 2.567.95
BS/BV (mm1) 13.70 15.23 15.68 14.87 1.046.98
SMI (none) 0.88 0.95 0.95 0.93 0.04 4.36
Tb.Th (mm) 0.24 0.24 0.22 0.24 0.016.24
Tb.N (mm1) 1.43 1.40 1.37 1.40 0.03 2.14
Tb.Sp (mm) 0.48 0.49 0.50 0.49 0.01 2.04
DA (none) 1.77 1.82 1.82 1.80 0.03 1.60
FD (none) 2.13 2.15 2.15 2.14 0.01 0.54
normality of the distributions. The test indicated that all
bone microarchitectural parameters had non-normal dis-
tributions. Therefore, the Mann-Whitney (Wilcoxon) test
was performed to assess comparisons between female
and male population, and also between the two bone
diseases groups.
In addition, the univariate correlation given by the
Spearman’s correlation coefficient of age as a continuous
variable for each bone microarchitectural parameter was
tested. Finally, for each bone disease group, Spearman’s
correlation coefficients were obtained for correlation
between each microarchitectural parameter.
Statistical analysis was performed using a statistical
software SAS (version 9.2, Institute Inc., Cary, NC, USA)
and differences were considered statistically significant
between groups for two-side p-value lower than 0.05,
and for p-value lower than 0.0001, it was considered that
those differences were highly statistically significant.
Table 3 presents the global results obtained from the
extended trabecular microarchitectural study, where a
comparative microarchitectural evaluation between two
bone diseases and both genders was made. The Mann-
Whitney (Wilcoxon) test revealed no significant differ-
ences between disease and gender. For both diseases,
specimens from male and female donors presented nearly
identical BV/TV values, and for these reasons the 3D
reconstruction of male and female samples did not pre-
sent great microarchitectural differences. Figure 4 shows
the 3D reconstruction for coxarthrosis samples and Fig-
ure 5 presents the fragility fracture samples.
In order to evaluate the effect of age on microarchi-
tectural properties, the Spearman correlation coefficients
were determined for all the samples from both groups of
coxarthrosis and fragility fracture (Table 4). Negative
coefficients were obtained for both CA and F groups,
meaning that the bone volume fraction, trabecular num-
ber and fractal dimension decrease with increasing age.
Trabecular separation and the Structure Model Index
increase with age, also for both disease groups. The pa-
rameters BS/BV, Tb.Th and DA show different Spear-
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A. C. Vale et al. / J. Biomedical Science and Engineering 6 (2013) 175-184
Table 3. Descriptive statistics of the micro-CT measurements for the two bone disease groups, CA (coxarthrosis) and F (osteoporosis
or fragility fracture).
Female (n = 20) Male (n = 15) Female (n = 23) Male (n = 7)
Parameter Mean ± SD Range Mean ± SD Range Mean ± SD Range Mean ± SD Range
Age (years) 69 ± 11 48 67 ± 10 35 81 ± 6 31 78 ± 6 14
BV/TV (%) 12.883 ± 5.047 19.211 17.002 ± 8.25726.665 13.094 ± 6.05722.117 13.100 ± 5.537 16.265
BS/BV (mm1) 13.539 ± 9.386 25.527 9.713 ± 8.121 20.034 11.768 ± 8.87222.437 12.959 ± 9.138 22.732
SMI (none) 1.838 ± 0.422 1.711 1.624 ± 0.528 1.594 1.736 ± 0.424 1.742 1.777 ± 0.247 0.808
Tb.Th (mm) 2.515 ± 3.696 9.506 2.844 ± 3.381 8.903 2.608 ± 3.351 8.199 1.951 ± 2.935 6.327
Tb.N (mm1) 0.398 ± 0.290 0.841 0.441 ± 0.384 0.970 0.365 ± 0.307 0.995 0.410 ± 0.279 0.707
Tb.Sp (mm) 8.378 ± 12.082 31.458 10.621 ± 12.86931.142 9.381 ± 12.18530.879 7.295 ± 11.166 23.007
DA (none) 0.898 ± 0.147 0.542 0.869 ± 0.199 0.685 0.870 ± 0.164 0.734 0.896 ± 0.108 0.315
FD (none) 2.094 ± 0.113 0.385 2.134 ± 0.121 0.414 2.123 ± 0.081 0.307 2.092 ± 0.079 0.240
Table 4. Spearman correlation coefficients between trabecular microarchitectural parameters and age for coxarthrosis (CA) and
fragility fracture (F) groups.
BV/TV (%) BS/BV (mm1) SMI (none) Tb.Th (mm) Tb.N (mm1)Tb.Sp (mm) DA (none) FD (none)
CA 0.213 0.030 0.311 0.057 0.166 0.172 0.113 0.268
F 0.305 0.034 0.136 0.058 0.182 0.133 0.104 0.209
Figure 4. Three-dimensional reconstruction of coxarthrosis
samples from a female with 59 years (a) and 75 years (b), and
from a male with 60 years (c) and 74 years (d).
Figure 5. Three-dimensional reconstruction of fragility fracture
samples from a female with 76 years (a) and 84 years (b), and
from a male with 70 years (c) and 84 years (d).
man coefficient signs, meaning that there are differences
between the coxarthrosis specimens and fragility fracture
groups. While BS/BV and DA decrease with age on the
CA group, an increase with age was found in the F group.
The trabecular thickness has a tendency to increase with
age on the coxarthrosis group, while it decreases in fra-
gility fracture group.
Additionally, to evaluate disease-related effects on the
relationship between each microarchitectural parameter,
the Spearman coefficients were determined for each dis-
ease group, and the results are presented in Tables 5 and
6. For the majority of the results, the Spearman statistics
revealed similar trends for both disease groups. In gen-
eral, the microarchitectural parameters are well corre-
lated with BV/TV, showing high values of the coeffi-
cients. The worst correlation values were obtained for
DA. Trabecular network complexity (FD) and trabecular
Structure Model Index are strongly correlated with
BV/TV and, consequently, with bone relative density.
A better understanding of the trabecular bone micro-
structure might be relevant for the evaluation of the
fracture risk of hip femoral bone. Several factors may
affect the structure of bone, as gender, age and disease.
The most important age-related change in trabecular struc-
ture is bone loss leading to an enhanced risk of fracture.
In our work, micro-CT evaluation revealed no significant
differences in microarchitectural parameters between
diseases groups and gender.
No significant differences were found from the Spear-
man coefficient determination for age effect, but both
diseases presented similar microstructural relationship
with age for the same parameters. This is the case of
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A. C. Vale et al. / J. Biomedical Science and Engineering 6 (2013) 175-184 181
Table 5. Spearman correlation coefficients between each trabecular microarchitectural parameter for
coxarthrosis (CA) group.
BV/TV (%) 0.382* 0.848** 0.356* 0.577* 0.460* 0.064 0.943**
BS/BV (mm1) 0.253 0.989** 0.453* 0.539* 0.061 0.307
SMI (none) 0.186 0.580* 0.445* 0.195 0.863**
Tb.Th (mm) 0.479* 0.555* 0.103 0.277
Tb.N (mm1) 0.949** 0.097 0.630**
Tb.Sp (mm) 0.141 0.501*
DA (none) 0.013
*p < 0.05; **p < 0.0001.
Table 6. Spearman correlation coefficients between each trabecular microarchitectural parameter for
fragility fracture (F) group.
BV/TV (%) 0.515* 0.804** 0.500* 0.511* 0.480* 0.129 0.890**
BS/BV (mm1) 0.384* 0.980**0.415* 0.447* 0.085 0.398*
SMI (none) 0.334 0.499* 0.463* 0.145 0.848**
Tb.Th (mm) 0.430* 0.453* 0.150 0.389*
Tb.N (mm1) 0.975** 0.012 0.544*
Tb.Sp (mm) 0.046 0.489*
DA (none) 0.144
*p < 0.05; **p < 0.0001.
BV/TV, Tb.N, FD, SMI and Tb.Sp. In healthy human
trabecular bone, BV/TV, Tb.Th and Tb.N, decrease,
while Tb.Sp and SMI increased with age [21]. With the
exception of Tb.Th, our findings are in accordance with
previous studies [20,21,50].
However, the parameters BS/BV, Tb.Th and DA show
different Spearman coefficient signs, for the two disease
groups, meaning that aging has different effects on some
microarchitectural parameters. This indicates different
aging mechanisms for different diseases. With aging, tra-
becular thickness increases in the CA group, but in the F
group, it decreases. The increase in trabecular thickness
can be attributed to a compensatory mechanism that tries
to maintain bone strength, even during bone loss [20].
An increase in the trabecular separation with age can
be achieved by an increase in the intertrabecular distance
or by the appearance of large areas with no trabeculae
[20]. This potentially happens in both CA and F groups.
Structure Model Index indicates the relative propor-
tion of rods and plates in a 3D structure such as trabecu-
lar bone. An ideal plate and an ideal cylinder have SMI
values of 0 and 3 respectively. Our SMI mean values
revealed that both groups present a mixture of plate- and
rod-like model microstructure. It is reported [21] that,
human healthy trabecular bone structure changes from a
plate-like to a more rod-like structure with age. The in-
crease of SMI with age is demonstrated on Table 4, for
our two disease groups.
The Degree of Anisotropy mean values are very close
to 1 affirming a rather homogeneous trabecular structure
in both groups. With aging, DA tends to decrease in the
CA group, while it tends to increase in the F group. It is
mentioned that in healthy bone, trabeculae with aging
seem to align to the direction of principal loading, which
means a decrease on DA. Probably the patients with CA
have a tendency to follow this mechanism, while the F
patients will maintain the bone anisotropy.
The fractal dimension, FD, which is an indicator of
surface complexity of an object that quantifies how the
object’s surface fills the space, tends to decrease with
age, which is explained by a decrease in the bone volume
The correlation between microarchitectural parameters,
determined by the Spearman statistics, shows that the
majority of the parameters are well correlated with BV/TV.
Moreover, our results are consistent with previous stud-
ies for which Tb.Th and Tb.N presented positive signifi-
cant coefficients with BV/TV, while Tb.Sp showed nega-
tive significant coefficients [35]. These findings confirm
the very important correlation between BV/TV and tra-
becular morphometric parameters (Tb.N, Tb.Th and Tb.Sp)
that influence porosity and, consequently trabecular bone
We emphasize the need for a preliminary study on mi-
Copyright © 2013 SciRes. OPEN ACCESS
A. C. Vale et al. / J. Biomedical Science and Engineering 6 (2013) 175-184
cro-CT variables prior to extending it to a large number
of samples. In fact, comparison with literature data is not
straightforward, even if the same device is used. Changes
in the parameters may bias the reconstructed images,
giving rise to a high variability of the 3D microarchitec-
tural parameters.
As on other works, bone volume fraction BV/TV mean
values and its good correlation with the other microar-
chitectural parameters demonstrated the great importance
of this parameter in trabecular structural prediction.
Gender, age and disease variations showed no signify-
cant effects on the microarchitectural parameters of tra-
becular bone.
As for healthy bone, there seems to be effects of aging
in the microarchitecture of bone. Age-related changes in
the microstructure of trabecular bone are not the same in
different pathologies, such as, coxarthrosis and fragility
fracture. With aging, bone specific surface, and the de-
gree of anisotropy decrease in the CA and increase in the
F group, while trabecular thickness increases in the CA
group and decreases in the F group. This means that,
depending on the disease, the age effects in the microbar-
chitecture of bone are different.
AC Vale would like to acknowledge the Portuguese research foundation
FCT (Fundação para a Ciência e Tecnologia) for providing financial
support (SFRH/BD/48100/2008). MFC Pereira and A Maurício acknowl-
edge FEDER Funds through Programa Operacional Factores de Com-
petitividade—COMPETE, and FCT Project PEst-OE/CTE/UI0098/2011.
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