Engineering, 2013, 5, 570-574 Published Online October 2013 (
Using Ultr as onic Spectrometry to Estim ate the Stability
of a Dental Implant Phantom
Hamed Hamid Muhammed, Satya V. V. N. Kothapalli
School of Technology and Health STH, Royal Institute of Technology KTH Alfred Nobels Alle 10,
Stockholm, Sweden
Email: ha med.,
Received 2013
A challenging problem in dental implant surgery is to evaluate the stability of the implant. In this simulation study, an
experimental phantom is used to represent a jawbone with a dental implant. It is made of a little pool filled with
soft-tissue equivalent material and a disc of fresh Oakwood with a metal screw. Varying levels of contact between
screw and wood are simulated by screwing in or out the screw. Initially, the screw is screwed in and fixed firmly in
wood. Thereafter, the screw is screwed out, a half turn each time, to increase the gap gradually between wood and
screw. Pulse-echo ultrasound is used and the power spectra of the received echo-signals are computed. These spectra
are normalized then analyzed by using the partial least squares method to estimate the corresponding implant stiffness
grade in terms of number of turns when beginning from the initial tight-screw state then screwing out the screw. A
coefficient of determination R2 of 96.4% and a mean absolute error of ±0.23 turns are achieved when comparing real
and estimated values of stiffness grades, indicating the efficiency of this approach.
Keywords: Dental Implant Stability; Partial Least Squares; Ultrasonic Spectrometry; Spectral Analysis
1. Introduction
Nowadays, dental implant surgeries are common among
female, male, young and elderly patients. In general,
these surgeries are performed in four different phases.
The most critical phase is the second one, called os-
seointegration [1], where the integration of implant into
living jawbone occurs. Depending on many factors, in-
cluding age, gender, bone tissue density and pathological
condition of the patient, the osseointegration process may
take a longer or a shorter time period.
Therefore, it is important to have a non-destructive,
risk-free and mobile clinical routine to measure the fix a-
tion of the implant. Fitting and Adler [2] suggested the
use of ultrasonic spectral analysis for non-destructive
testing. By using this technique, which can be optimized
to fulfill the requirements mentioned above, it sounds
promising to test the hypothesis that ultrasonic spectral
measurements can be performed to evaluate the biome-
chanical stability or the stiffness of the bone-implant
interface which is proportional to the osseointegration
Recently, Shiu-Fen Lin et al. [3] as well as Pan and
Ying [4] proposed and used resonance frequency analysis
(RFA) to evaluate the grade of osseointegration. They
found that the resonance frequency of the bone-implant
interface or structure was proportional to implant me-
chanical stability. The frequencies considered were in the
range of 60 - 120 Hz to be able to study the vibration of
the whole implant imbedded into Bakelite (a gypsum
model). The results presented were rather binary, only
showing if the grade of stiffness was high or low.
Valderrama et al. [5] showed that the recently intro-
duced magnetic RFA device could give comparable re-
sults as the original electron ic variant. However, Pattijnet
al. [6] showed that the energy of the signal measured by
the RFA technique was angle and displacement depen-
dent and could change considerably when performing the
measurements at different parts of the implants and from
different direction s.
De Almeida et al. [7] introduced a new approach,
called quantitive u ltrasound (QUS), wher e a transmission
ultrasound technique was used to inspect a phantom
made of a metal thread ed piece (representin g the implant)
imbedded into a metal block (corresponding to the bone).
A 1 MHz central frequency ultrasonic transducer was
used in this study. The results showed that the mean val-
ue of detected time signal was proportional to the stiff-
ness of the st r ucture of thi s p hantom.
Mathieu et al. [8] performed an ex vivo study and
showed that the QUS technique could be used to com-
pute a quantitative parameter which was significantly
Copyright © 2013 SciRes. ENG
sensitive to the amount of bone (rabbit femur was used)
in contact with a cylindr ical titanium dental implant. The
transducer used in this study had a central frequency of
10 MHz to allow for distinguishing different echoes ori-
ginating from different i mplant inter faces. Furthermore, a
long-enough signal duration (25 times longer that used
by de Almeida et al. [7]) was used to retrieve more in-
formation from the implant and compute the indicator.
The new approach, proposed by this work, aims at
evaluating the stability of the dental implant screw in
bone and soft tissues. For this purpose, a proper phantom
is designed and used. Ultrasonic spectral analysis is uti-
lized to detect differences or changes in the shape of the
curve of the power spectrum of the ultrasonic echo-sig-
nals reflected from the phantom representing an implant
imbedded into a jawbone. An automatic statistical-mod-
eling method is developed and utilized to estimate (based
on analyzing the shape of ultrasonic power spectra) the
contact and the stiffness grade between the implant and
the jawbone.
However, in statistical modeling, two common prob-
lems are usually encountered: 1) Large number of va-
riables and few observations. 2) Explanatory and depen-
dent variables are collinear. These two problems can be
solved by, at first, preprocessing and normalizing the
data in an efficient way, then using a suitable Partial
Least Squares (PLS) algorithm to achieve the desired
2. Materials and Methods
2.1. Phantom and Experimental Setup
A disc of fresh Oakwood was used to simulate a jawbone.
The speed of sound in Oakwood is about 3800 m/s [9],
which is close to the speed of sound in bone tissue,
which is about 3750 ± 250 m/s. The spongy microstruc-
ture of wood tissue is also similar to bone tissue making
it efficient to produce and use wood-based bone-implants
[10]. A metal screw was screwed into this wooden disc to
simulate an implant, as shown in Figure 1(a). This disc
was immersed into a pool filled with water and a
soft-tissue equivalent material; a black-colored mixture
composed of 4% graphite, 3% agar and 93% water [11].
An ultrasonic transducer was mounted at 21 mm distance
from the edge of the wooden disc as shown in Figure
1(b). The central or fundamental frequency of this single-
crystal piezoelectric ultrasonic transducer was 2 MHz,
and the frequency band was ranging from 1.8 MHz to 2.2
A pulse generator with an amplifier was used to excite
the transducer to make it emit an ultrasonic pulse, then
receive the echo signal and transfer it through the am-
plifier to an oscilloscope. A power spectrum of the re-
ceived signal was generated by the oscilloscope by using
the fast Fourier transform (FFT). This spectrum was
transferred to a personal computer (PC), where it was
2.2. Dataset
A set of 30 power spectra was ac quir ed by the pulse-echo
ultrasound system described previously. Each power
spectrum corresponded to a certain contact level and a
certain stiffness grade between the metal screw and the
wooden disc. The contact level was measured in number-
of-turns when screwing the screw out or into the disc.
Initially, the screw was screwed in firmly in the wooden
disc. Thereafter, it was screwed out, a half turn each time,
to increase the gap between wood and screw gradually
until reaching 5 turns. Then it was screwed into the disc
again with a half turn each time until reaching the tight-
screw state again.
Finally, it was screwed out again exactly as before un-
til reaching 5 turn s. By this way, 10 different contact and
stiffness grades (linearly distributed between 0.5 and 5
turns) were simulated three times and the corresponding
power spectra were measured. Hence, the dataset availa-
ble for this work consisted of 30 power spectra and the
corresponding numbers of turns.
2.3. Methodology
1) Partial Least Squares (PLS): PLS is a multivariate
Figure 1. (a) Metal screw screwed into a wooden disc. (b) The experimental setup where the phantom, labeled with (1) and
presented i n Figure 1(a), is immersed into a pool filled with water and a soft-tissue equivalent material, labeled with (3). An
ultrasonic transducer, labeled with (2), is mounted 21 mm away from the edge of the phantom.
Copyright © 2013 SciRes. ENG
statistical framework which includes a wide class of me-
thods and is used for processing, interpreting and ana-
lyzing data, measurements and observations in a wide
range of fields and applications. An overview of PLS is
presented by Rosipal and Kramer [12].
The pioneering work of introducing PLS was mainly
done by Herman Wold in 1966 [13] and 1975 [14]. Since
then, PLS has received great attention in many research
fields. The basic principle of this method is to find and
use a small number of uncorrelated variables (known as
components or latent variables) to explain as much cova-
riance as possible between the two blocks of explanatory
X and dependent Y variables; where each column of the
X- and Y-matrices contains one variable.
At first, the X- and Y-variables are preprocessed to
make their distributions fairly symmetrical. The nth root
transformation (where n is a real number) can be used to
compress the dynamic range of these variables so that the
result of dividing the mean value by the standard devia-
tion (of this variable) will be around one. After that, a nor-
malization technique called whitening [15] is performed
to scale the data into zero-mean and unit-variance.
The general PLS model is described as follows:
where X is an nxm matrix of predictors, Y is an nxp ma-
trix of responses, T is an nxl matrix of factors, P and Q
are mxl and p xl loading matrices (of w eight coefficients),
respectively, and matrices E and F contain error terms.
There exist a number of PLS algorithms to estimate
the factor and loading matrices T, P and Q. Most of these
algorithms estimate the linear regression between X and
Y as:
Y = XB + N (2)
where Y contains n cases and m dependent variables, X
contains n cases and p independent variables, B contains
pxm regression coefficients (reflecting the covariance
structure between Y and X), and N is a noise term of the
same size as Y.
2) Using PLS analysis: There exist many different
ways of performing PLS analysis. In this work the
non-linear iterative partial least squares algorithm (NIP-
ALS; [14]) is used. The first step of the NIPALS algo-
rithm is to create two matrices E = X and F = Y, where
the columns of X contain the measured ultrasonic power
spectra (which are our independent variables), while Y is
a vector contains the corresponding dependent variables
that are desired to be estimated.
The second step is to prepro cess and normalize both X
and Y. Each element of vector Y is transformed (by
choosing an appropriate power value) so that the result of
dividing its mean value by its standard deviation will be
around one. After that, whitening is applied to Y. Matrix
X is also whitened, but by employing two iterative nor-
malisation approaches, where a number of alternating
spectral-wise (denoted as Sw and performed row-wise in
matrix X) and band-wise (denoted as Bw and performed
column-wise in matrix X) whitening operations are per-
formed, as described in [16]. When performing Sw-whi-
tening, each spectrum (which corresponds to one row in
matrix X) is whitened, while each column of X (which
corresponds to one spectral band) is whitened when Bw-
whitening is performed. In the first iterative normalisa-
tion approach, a series of alternating Sw- and Bw-whi-
tening operations, beginning and ending with Sw-whi-
tening operations, were performed. On the other hand,
the second iterative approach started with a Bw-whiten-
ing operat i on and ended with a Sw-whitening opera t ion.
After that, the PLS algorithm starts and a series of
iterative operations are performed until convergence of
the result is achieved. At this point, the whole set of la-
tent variables are calculated, as explained by Abdi [17].
In order to avoid over modeling, the number of latent
variables to be included in the PLS model should be de-
termined. A rule of thumb is that each latent variable
used in the final model corresponds to five or six inde-
pendent observations in the training dataset. (Rhiel et al.
By this way, an upper limit for how many latent va-
riables to include has been defined. The remaining ques-
tion is how many of these latent variables are enough to
include. A popular way to know that is by calculating the
relative error value which is zero when perfect prediction
is achieved. Otherwise, it is always a positive number.
The PLS model is improved as long as adding more la-
tent variables lowers the relative erro r value. The optimal
number of latent variables is found when the relative
error value begins to increase [17].
3) Cross validation: To evaluate the usefulness and ef-
ficiency of the PLS model, cross validation is necessary.
Leave-one-out cross validation is an efficient evaluation
method when few observations are available. Only one
pair of variables at a time is removed from the reference
dataset and the excluded dependent parameter (which is
desired to be estimated) is considered as unknown. The
rest of the data samples are considered as a training da-
taset and the excluded measured spectrum is fed into the
trained model to estimate the excluded dependent para-
The estimated parameter values are finally compared
to the real values, to evaluate the performance of the PLS
model. Both of the relative error and the coefficient of
determination R2 can be used as evaluation measure.
3. Experimental Results
The gap between the jawbone and the dental implant may
Copyright © 2013 SciRes. ENG
vary between zero and 0.1 mm, which is the initial gap
size when inserting a screw-type dental implant [19]. The
gap-size variation corresponds to a variation of the con-
tact and the stiffness grade between the implant and the
jawbone. However, what is more interesting and impor-
tant to know is if the osseointegration process is com-
plete or not, and if this process is progressing towards
obtaining a better intimate contact, between the bone and
the implant, or not. An efficient way to simulate varying
grades of osseointegration is to use the experimental se-
tup and the phantom described previously.
The nonlinear propagation of the ultrasonic waves
through the wooden disc, the metal screw and the wa-
ter-filled gap between them will gradually deform the
shape of the waves. Therefore, in addition to the effect of
Fourier transforming the time-signal received by the
transducer, higher harmonic frequencies (defined as in-
teger multiples of the fundamental frequency of the ul-
trasonic transducer) appear in the power spectrum. The
frequency of the peak of the first harmonic was shifted
from 2 MHz down to 1.92 MHz. The second harmonic
peak was at around 3.84 MHz, the third one was at
around 5.76 MHz and so on, as shown in Figure 2. Low
pass filtering was used to suppress the noise and get
smoother spectra.
It is essential to ensure the repeatability of the experi-
ments to validate the capability of an ultrasonic mea-
surement system. Therefore, the experiments were re-
peated using different initial conditions and the measured
power spectra were compared. The resulting standard
deviations , when comparing spectra correspo nding to the
same contact level, were mainly limited to 1%.
Figure 2 shows a comparison between two power
spectra; one corresponds to a tight screw and the other
one corresponds to a loose screw. It is possible to ob-
serve differences between these spectra, at the higher
harmonics. These differences are automatically utilized
by the PLS algorithm to be able to estimate the corres-
ponding stiffness grade which is measured in num-
ber-of-turns when screwing the metal screw out or into
the wooden disc. Before applying the PLS algorithm, the
power spectra were pr eprocessed according to the guide-
lines m e nt ioned previously.
Figure 3 presents a comparison between real and es-
timated stiffness grades (measured in number-of-turns),
with a coefficient of determination R2 of 96.4% and a
mean absolute error of ±0.23 turns.
4. Discussion and Conclusions
Although the obtained results indicate the usefulness and
efficiency of the used approach, the transducer was at-
tached to the phantom at approximately the same posi-
tion, angle and direction during the whole experiment.
Figure 2. A comparison between two power spectra; a
power spectrum of a tight screw and another one of a loose
Figure 3. A comparison between real and estimated num-
ber-of-tums when screwing the screw out, then in, then out
again. Various number-of-turns correspond to various stiff-
ness grades be tween the screw and wood.
Minor changes occurred when trying to screw in or out
the screw. The spectral measurements will change con-
siderably when measuring at different parts of the phan-
tom and from different directions or angles. However, it
is possible to normalize these spectra and make them
useable, since the method proposed and used in this work
doesn’t rely on comparing amplitudes of one or several
peaks found at certain frequency intervals. The new me-
thod makes instead use of the shape of the whole spec-
trum which makes it more efficient and practical so that
it can be used outside the laboratory without the need for
controlled measuring conditions. Furthermore, it is not
necessary to manually identify the most useful spectral
regions where most variations among the spectra are vis-
ible. The significance of the new method is that it is ob-
jective, non-invasive, fast, accurate, automatic and re-
[1] P. I. Branemark, U. Breine, R. Adell, B. O. Hansson, J.
Lindstrom and A. Ohlsson, “Intra-Osseous Anchorage of
Dental Prostheses: I. Experimental Studies,” Journal of
Plastic and Reconstructive Surgery and Hand Surgery,
Informa Healthcare, Vol. 3, No. 2, 1969, pp. 81-100.
Copyright © 2013 SciRes. ENG
[2] D. W. Fitting and L. Adler, Ultrasonic Spectral Analysis
for Nondestructive Evaluation,” Plenu m Press, New York,
[3] S. F. Lin, L. C. Pan, S. Y. Lee, Y. H. Peng and T. C.
Hsiao, “Resonance Frequency Analysis for Osseoi ntegra-
tion in Four Surgical Conditions of Dental Implants,”
Proceedings of the 23rd Annual International Conference
of the IEEE Engineering in Medicine and Biology Society,
Vol. 3, 2001, pp. 2998-3001.
[4] R. L. C. Pan and S. H. Ying, “Mechanical Properties of
Bone-Implant Interface: An in Vitro Model for the Com-
parison of Stability Parameters Affecting Various Stages
during Osseointegration for Dental Implant,” IEMBS’04
26th Annual International Conference of the IEEE Engi-
neering in Medicine and Biology Society, Vol. 2, 2005, pp.
[5] P. Valderrama, T. W. Oates, A. A. Jones, J. Simpson, J. D.
Schoolfield and D. L. Cochran, Evaluation of Two Dif-
ferent Resonance Frequency Devices to Detect Implant
Stability: A Clinical Trial,” Journal of Periodontology,
American Academy of Periodontology, Vol. 78, No. 2,
2007, pp. 262-272.
[6] V. Pattijn, S. V. N. Jaecques, E. De Smet, L. Muraru, C.
Van Lierde, G. Van der Perre, I. Naert and J. V. Sloten,
“Resonance Frequency Analysis of Implants in the Gui-
nea Pig Model: Influence of Boundary Conditions and
Orientation of the Transducer,” Medical Engineering &
Physics, Vol. 29, No. 2, 2007, pp. 182-190.
[7] M. S. De Almeida, C. D. Maciel and J. C. Pereira, Pro-
posal for an Ultrasonic Tool to Monit or the Osseointegra-
tion of Dental Implants,” Sensors, Molecular Diversity
Preservation International, Vol. 7, No. 7, 2007, pp. 1224-
[8] V. Mathieu, F. Anagnostou, E. Soffer and G. Haiat, “Ul-
trasonic Evaluation of Dental Implant Biomechanical Sta-
bility: An in Vitro Study,” Ult rasound in Medicine & Bio-
logy, Vol. 37, No. 2, 2011, pp. 262-270.
[9] A. Walker, The Encyclopedia of Wood,” Quatro Pub-
lishing, London 2005, p. 192.
[10] A. Tampieri, S. Sprio, A. Rufini, I. G. Lesci and N. Ro-
veri, “From Wood to Bone: Multi-Step Process to Con-
vert Hierarchical Structures into Biomimetic Hydroxya-
patite Scaffolds for Bone Tissue Engineering,” Journal of
Materials Chemistry, Vol. 19, No. 28, 2009, pp. 4973-
[11] E. L. Madse n, J. A. Zagzebski, R. A. Banjavie and R. E.
Jutila, “Tissue Mimicking Materials for Ultrasound Phan-
toms,” Medical Physics , Vol. 5, 1978, p. 391.
[12] R. Rosipal and N. Kramer, Overview and Recent Ad-
vances in Partial Least Squares,” Subspace, Latent Struc-
ture and Feature Selection: Statistical and Optimization
Perspectives Workshop, SLSFS 2005; Revised Selected
Papers, Springer-Verlag Inc., New York, 2006, pp. 34-51.
[13] H. Wold , Nonlinear Estimation by Iterative Least Square s
Procedures,” In: F. N. David, Ed., Festschrift for J. Ney-
man, Wiley, New York, 1966, p. 411.
[14] H. Wold, Path Models with Latent Variables: The NIP-
ALS Approach,” Quantitative Sociology: International
Perspectives on Mathematical and Statistical Modeling,
1975, pp. 307-357.
[15] Y. C. Eldar and A. V. Oppenheim, MMSE Whitening
and Subspace Whitening,” IEEE Transactions on Infor-
mation Theory, Vol. 49, No. 7, 2003, pp. 1846-1851.
[16] H. Hamid Muhammed, Hyperspectral Crop Reflectance
Data for Characterising and Estimating Fungal Disease
Severity in Wheat,” Biosystems Engineeri n g, Vol. 9 1, No.
1, 2005, pp. 9-20.
[17] H. Abdi, Partial Least Square Regression,” Encyclopedia
for Research Methods for the Social Sciences, 2003.
[18] M. Rhiel, M. B. Cohen, D. W. Murhammer and M. A.
Arnold, “Nondestructive Near-Infrared Spec trosco pic Mea-
surement of Multiple Analytes in Undiluted Samples of
Serum-Based Cell Culture Media,” University Of Iowa,
[19] R. K. Schenk and D. Buser, Osseointegration: A Reality,”
Periodontology, Vol. 17, No. 1, 1998, pp. 22-35.
Copyright © 2013 SciRes. ENG