Wireless Magnetoelastic Monitoring of Biliary Stents,

Wireless Magnetoelastic Monitoring of Biliary Stents,
Wireless Magnetoelastic Monitoring of Biliary Stents
Scott R. Green and Yogesh B. Gianchandani
Abstract—This paper presents a system for wirelessly monitoring the accumulation of sludge in a biliary stent. Two generations
of the system are detailed. The first-generation system utilizes a
2 × 37.5-mm ribbon sensor with a mass of 18 mg, along with
0.8-mm-thick × 1.6-mm-diameter neodymium magnets to bias the
sensor. Both components are integrated with a 4-mm-diameter
stainless steel stent. The second-generation system comprises a
sensor and a magnetic layer [consisting of strontium ferrite particles suspended in polydimethylsiloxane (PDMS)] that conform to
the meshed topology and tubular curvature of a 5-mm-diameter
Elgiloy stent. The second-generation sensors have an active area
of 7.5 × 29 mm and a mass of 9.1 mg. The sensors in both
generations are fabricated from 28-μm-thick foils of magnetoelastic 2826MB Metglas, an amorphous Ni–Fe alloy. Analytical
and finite-element models that predict sensor response in the dynamic biological environment are presented. The response of each
system to viscosity changes that precede and accompany biliary
sludge accumulation is tested, with resonant frequency changes of
2.8% and 6.5% over a 10-cP range for each respective generation.
Sludge accumulation is simulated with successive coatings of either
paraffin or an acrylate terpolymer. Resonant frequency response
to this mass loading effect is similar for both generations of the
system, showing a 40% decrease after applying a mass load of
2.5× the mass of the sensor.
Index Terms—Elgiloy, magnetoelasticity, Metglas, microsensors, photochemical machining, resonant sensors.
TENTS are tubular structures used to impart and maintain
patency in a variety of vessels and ducts that have become
constricted as a result of stenotic pathology. Although the act
of implanting a stent relieves symptoms caused by the constriction, in-stent restenosis—a reappearance of the narrowing,
typically due to the reaction of the body to the presence of the
stent—is a risk associated with all stenting procedures.
An example of a stent application area—and the focus of
this paper—is the bile duct, which transports bile between the
liver, gall bladder, pancreas, and small intestine. Bile is used
in the intestinal tract for the emulsification and absorption of
fats. The constriction relieved by stent implantation is often due
to pancreatitis, cholangitis, tumors, or gallstones. Restenosis
Manuscript received June 16, 2008; revised September 12, 2008. First
published December 22, 2008; current version published February 4, 2009.
This work was supported in part by the NSF ERC for Wireless Integrated
Microsystems (WIMS) and in part by the University of Michigan. The work of
S. R. Green was supported by an NSF Graduate Research Fellowship. Subject
Editor A. J. Ricco.
S. R. Green is with the Department of Mechanical Engineering, University
of Michigan, Ann Arbor, MI 48109-2125 USA (e-mail: [email protected]).
Y. B. Gianchandani is with the Department of Electrical Engineering and
Computer Science, University of Michigan, Ann Arbor, MI 48109 USA, and
also with the Department of Mechanical Engineering, University of Michigan,
Ann Arbor, MI 48109-2125 USA (e-mail: [email protected]).
Color versions of one or more of the figures in this paper are available online
at http://ieeexplore.ieee.org.
Digital Object Identifier 10.1109/JMEMS.2008.2008568
Fig. 1. Pathology of biliary restenosis subsequent to stent placement. (A) Selfexpanding stent initially relieves duct constriction. (B) Protein layer forms on
the surfaces of the stent. (C) Protein layer allows bacteria to adhere to the
stent surfaces. (D) Bacteria generate a mucopolysaccharide matrix, commonly
termed “biliary sludge.” (E) Sludge accumulates, leading to a narrowed-duct
can occur in an average of four to five months via formation
of a bacterial matrix on and around the stent. This bacterial
matrix is known as biliary “sludge” [1]. The pathology of
sludge formation (Fig. 1) begins with the formation of a protein layer—including fibronectin and collagen—on the stent
surfaces. Bacteria present in the bile, including Escherichia
coli and Enterococcus, tend to adhere to the protein. As these
bacteria congregate, they produce an extracellular biofilm and
matrix. Additionally, the bacteria release enzymes that precipitate crystals of cholesterol and calcium bilirubinate (among
others) out of solution; these crystals become trapped in the
matrix. Collectively, the matrix, crystals, biofilm, and bacteria are termed “biliary sludge.” This sludge accumulates and
eventually leads to occlusion of the duct which, of course, is
the same situation that required the stent implantation. Current medical techniques for dealing with sludge accumulation
include either replacing the stent while clearing the sludge or
simply implanting a second stent inside of the first.
Various methods for the prevention of biliary stent clogging
have been investigated. These methods include using stent materials with different levels of hydrophilicity or even materials
impregnated with antimicrobial agents. Alternatively, patients
have been administered prophylactic antibiotics in an attempt to
kill bacteria before their attachment to the stent. Anticlogging
mechanical features such as side holes (or lack of side holes) or
a unidirectional valve mechanism (to prevent influx of bacterialaden fluids) have also been tested. To date, each of these
methods has achieved mixed results that are not yet at the level
of clinical relevance. Clearly, restenosis is a recalcitrant issue.
Since prevention of restenosis cannot be assured, the patient
must be monitored to ensure continued patency of the bile duct.
The time frame for clinically significant restenosis to occur
is highly variable from case to case. Current techniques for
diagnosing a blockage use a blood test to monitor enzymes such
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Fig. 2. Conceptual diagram of two generations of in vivo magnetoelastic
sensing of sludge accumulation for biliary stents. (a) External circuitry drives
the external interrogation coils to wirelessly measure the response of the
implanted sensor. (b) In the generation-1 system, the discrete neodymium
magnets bias the ribbon sensor. (c) In the generation-2 system, the distributed
SrFe permanent-magnet layer biases the wishbone-array sensor for optimal
signal and does not hinder the stent mechanical operation.
as bilirubin and alkaline phosphatase, among others. Imaging
of the duct—using either computed tomography or endoscopic
cholangiography—then confirms the presence of a blockage.
These techniques for diagnosing a blockage are indirect and
rely on detecting enzyme levels that may not increase until after
the blockage is significant. The combined effect of the unknown
time course for restenosis and the indirect testing methods
can result in either unnecessary prescheduled interventions or
untimely interventions after patients exhibit outward symptoms
of the blockage, such as jaundice or pruritus. As such, a direct
method of diagnosis would enable timely intervention and eliminate unnecessary procedures. The method outlined in Fig. 2,
highlighting two generations of an integrated system, provides
just such a direct measurement of sludge accumulation in a
biliary stent. A network analyzer controls an amplifier, driving
the transmit coils in an ac frequency sweep that produces a
corresponding magnetic field sweep. The magnetic field causes
a magnetoelastic sensor integrated with the stent to resonate
at a frequency that changes as local viscosity increases and
as sludge accumulates. The mechanical resonance generates an
oscillating magnetic flux that can be measured with an external
pickup coil. The frequency content of the induced voltage can
then be correlated to the local sensor environment.
We have previously reported a “smart stent” for cardiovascular applications [2] which utilizes variable capacitance pressure
sensors connected to an inductive “stentenna” to form a wireless resonant LC tank. The pressure drop measured across the
stent can then be correlated with the flow through the stent. For
coronary stents, flow rates of 100–200 mL/min are expected,
with average pressures of ∼100 mmHg. In contrast, flow rates
in biliary stents are 3–4 mL/min after meals and ∼1 mL/min
on average, while pressure differences and average pressures
are on the order of 10–30 mmHg [3], [4]. Measurement of
flow and pressure is thus a much more difficult task in biliary
stents than in cardiovascular stents, and pressure changes are
less correlated with disease states. Investigation into alternative
transduction methods for biliary applications is thus warranted.
Past work in magnetoelastic sensors has demonstrated the
feasibility of sensing mass loading, media viscosity, and other
properties in environmental/industrial applications [5]–[8]. An
early device utilizing the magnetoelastic transduction pathway
consisted of NiFe films sandwiched around a chemically sensitive polymer layer, the swelling of which affected the amplitude
and shape of the induced voltage pulse train [5]. The device was
not used as a resonant sensor.
Resonant sensors have the relative advantages of immunity
to noise and changes in the amplitude or orientation of the
interrogation field. Due to these advantages, later work in
magnetoelastic sensors used the measured resonant frequency
to correlate with properties of either the surrounding media or
with properties in the sensor material or an analyte layer on
the sensor. Examples of the former properties include density,
viscosity [6], [7], and pressure [8] in a surrounding fluid
layer, while examples of the latter properties include thermal
coefficients (to measure temperature) of the sensor material or
analyte layer stiffness or density (to measure the presence of a
target chemical species). Recent work with magnetoelastic sensors has explored acute measurement of gastroesophageal pH
with instrumented ingested capsules [9]. In each of these cases,
the stiffness, mass, or damping of the system is altered in some
way, and these general parameters directly govern the resonant
frequency. Reviews on magnetoelastic sensors were published
in 2002 [10] and more recently in 2007 [11].
An important feature of magnetoelastic sensors in the context
of implantable devices is the ability to be wirelessly interrogated. This paper1 presents an integrated system for wirelessly
monitoring the accumulation of sludge in a biliary stent. Two
1 Portions of this paper have appeared in conference abstract form in
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generations of the system are detailed, the first comprising a
magnetoelastic ribbon sensor and biasing discrete permanent
magnets, and the second comprising a sensor and biasing
permanent-magnet layer that conform to the meshed topology
and tubular curvature of a biliary stent. Modeling tools for
resonant magnetoelastic sensors are first described, followed by
specific design and fabrication information. Finally, experimental methods and results are delineated.
A. General Considerations
Magnetoelastic behavior is most prominent in materials with
elongated magnetic domains [15]. Under an applied magnetic
field, these domains tend to rotate and align with the field.
As the long axes of the domains rotate and align, the material experiences strain. The magnetization of the material also
responds to the applied field. For magnetoelastic materials used
as resonant sensors, the oscillating magnetic flux developed as
a result of oscillating strain in the sensor can then induce a
voltage on a suitably located pickup coil.
Although magnetoelastic materials are generally nonlinear, it
is prudent to use linearized constitutive equations describing the
coupling between flux density, field strength, stress, and strain
in a magnetostrictive material
σ = [C]ε −
[C][d]T B
μo μr
= − [d][C] ε + 1 B.
μo μr
μ0 μr
Equations (1) and (2) are versions of the so-called “piezomagnetic” equations [16], where σ is the vectorized stress tensor,
C is the stiffness matrix, ε is the vectorized strain tensor, d is the
magnetostrictivity matrix, B is the magnetic flux density vector,
H is the field strength vector, μ0 is the permeability of free
space, and μr is the relative permeability (assumed isotropic
Typical “transfer characteristics” relating the strain to the
applied field for zero prestress in three grades of amorphous
metals—primarily nickel–iron alloys—sold under the trade
name Metglas [17] are shown in Fig. 3 (using data provided in
[18]–[20]). Note that hysteresis effects that are present in these
curves are omitted for clarity. The derivative of the curve at the
chosen bias field gives the magnetostrictivity. The amorphous
nature of the materials results in isotropic magnetostrictivity.
Other important properties of these materials are listed in
Table I. The high permeability of these materials enhances the
antenna-like nature of the sensor by attracting flux lines and
directing them along the length of the sensor, which is desirable
in that the orientation of the interrogating signal is not required
to be exactly along the length of the sensor for good response.
However, the high permeability can also be a disadvantage in
that it limits how effectively the interrogative field can penetrate
the sensor and how effectively the sensor can emit flux. In
fact, both the analytical and finite-element models described in
Fig. 3. Magnetostriction versus applied field for various amorphous metals
(reproduced from [18]–[20]). The illustrated curves are from materials after
annealing in a transverse magnetic field. The derivative of these curves evaluated at a bias point leads to the small-signal magnetostrictivity (“d”) for the
material at that bias point. These plots can be helpful for choosing between
various alloys; however, they are generally established with low-frequency
(quasi-static) stimulating fields and thus provide incomplete information about
material behavior near resonance [19].
part B of this section predict that the signal output of the sensor
is inversely proportional to the permeability.
The choice of a bias field determines not only the smallsignal magnetostrictivity but also the apparent Young’s modulus of the material. The dependence of the Young’s modulus
on the applied field is termed the “ΔE effect.” The ΔE effect
is a direct result of the magnetomechanical coupling properties
of the material [21] and can be quite substantial in amorphous
metals. For example, the listed (i.e., unbiased) Young’s modulus
for the 2826MB alloy is 100–110 GPa. For bias fields near
1 Oe in a transversely annealed sample, the modulus can vary
by 21–31 GPa/Oe [22]. Thus, small changes in the bias field can
result in large changes in the Young’s modulus. For a resonant
sensor, this effect is particularly important, as the resonant
frequency is roughly proportional to the square root of the
Young’s modulus. As such, providing a consistent biasing field
to the sensor is crucial to minimizing a significant source of
repeatability error. In fact, minimizing the ΔE effect is the main
driver for integrating the biasing magnetic components with the
stent in our system, as will be further discussed in Section III.
Past studies of the magnetoelastic properties of amorphous
metals often include annealing the material (e.g., [18]–[20],
[22], and [23]). In general, this procedure improves the magnetomechanical coupling coefficient, which is a measure of the
efficiency of the material in converting between magnetic energy and elastic energy. In this paper, the response of the sensors
to thermal treatment is explored. Inclusion of a large transverse
dc magnetic field during the treatment may improve the signal
amplitude further but is outside the scope of this paper.
It should be noted that amorphous metals are not the only
materials that exhibit large magnetostriction. In fact, so-called
“giant magnetostrictive materials” (GMMs)—rare-earth–iron
alloys like Terfenol-D (terbium, iron, and dysprosium) and
Galfenol (gallium and iron)—have been recently explored
in transducer applications, e.g., in [15] and [24]. However,
when compared with amorphous metals in our application,
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the GMMs are less readily available in thin foils (important
for maintaining a low profile in situ) and require much larger
biasing fields for optimal operation (on the order of 100 Oe).
Preliminary experiments with Terfenol-D samples were unsatisfactory, although further development may prove the material
B. Analytical and Numerical Modeling
In our proposed application, the magnetoelastic sensor is
affected by the dynamic biological environment. Specifically,
the surrounding liquid medium along with sludge accumulation
has direct effects on the response of the sensor in terms of both
resonant frequency and signal amplitude. To better quantify
these effects and to provide insight into the operation of the
sensor, an analytical model is proposed. For the following
study, the sensor is assumed to be a ribbon (i.e., rectangular in
shape), and the length is assumed to be along the x-axis. Also,
the sensor is assumed to be fixed only at the exact midlength,
resulting in free–free end conditions.
Following a treatment in [15], (1) and (2) can be made 1-D
by replacing the magnetostrictivity and stiffness matrices with
the isotropic magnetostrictivity and apparent Young’s modulus,
respectively. Newton’s law is then applied to an infinitesimal
volume of the sensor loaded on both sides with sludge and
vibrating in a viscous medium. The strain in the sludge is
assumed to be the same as that in the sensor. The analysis results
in the following equation of motion relating the input magnetic
field to the sensor displacement:
tsens + 2Esludge
Eapp −
μ0 μr
Stiffness Terms
+ 2ρsludge tsludge
− ρsens tsens + 1.98ρfl
Mass Loading Terms
+ αρsens tsens
− 2μ
Viscous Damping Terms
+ β
Eapp −
μ0 μr
∂x2 ∂t
Hysteretic Damping Term
= Eapp dtsens · H · eiωt
Driving Term
The derivation of specific terms is described in more detail
subsequently. The parameters in (3) are defined as in Table II.
The stiffness of the sensor is modified by the coupling
between the strain and the magnetic field, as described in (1)
and (2). The sludge is assumed to be a viscoelastic material such
that it can be described with a complex modulus possessing a
storage aspect in-phase with the displacement and a loss aspect
out-of-phase with the displacement [25].
The first and third mass loading terms are simply the mass
per unit length of the sensor and sludge, respectively. This
assumes a uniform layer of sludge on both sides of the sensor.
The second term is an effective mass loading provided by the
surrounding viscous fluid, as described in [26]. Briefly, the
sensor (or sludge) surface and fluid interact, and a certain
amount of the fluid—with a characteristic length dependent on
the activation frequency and fluid viscosity—is activated and
contributes to the kinetic energy of the vibration.
The first viscous damping term reflects the damping mechanism provided by the surrounding medium due to viscous shear
stresses, an effect also described in [26]. The second viscous
damping term represents damping that is proportional to the
mass of the sensor. The hysteretic damping term represents
damping that is proportional to the stiffness of the sensor. The
choice of modeling the overall structural damping of the sensor
with terms proportional to the mass and stiffness of the sensor
is due to the direct comparison that can be made to the damping
assigned in the customized finite-element analysis (FEA) model
(described later in this section).
The coupling described by (1) and (2) gives rise to the driving
term. For this analysis, the interrogative magnetic field H
is assumed to be sinusoidal in time and uniform in space.
However, because the sensor has a large relative permeability,
the actual field within the sensor is not uniform along the length
of the sensor. The distribution of the field within the sensor
is described by the shape function ϕ. The shape function is
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Fig. 4. Model simulation of a 2 × 37.5-mm-long magnetoelastic sensor
displacement frequency response for various loading conditions.
dependent on permeability and, to a lesser extent, on sensor
aspect ratio and is determined for the purposes of this analysis
by fitting a curve to results determined from a magnetostatic
FEA with the desired sensor geometry.
The equation of motion and associated boundary conditions
can be solved via the method of eigenfunction expansion,
as described in [25]. The result is an infinite summation of
eigenfunctions multiplied by a term related to the mass loading
terms, damping terms, stiffness terms, and applied magnetic
field. Because we are generally concerned with only the first
resonant frequency of the system, a one-term approximation is
appropriate and justified according to a truncation analysis.
The one-term approximation is as follows:
u(x, t) =
Eapp dtsens H L2
π 2
L dϕ
πx 2L
dx eiωt
π 2
(keff )−ω 2 (meff )+iω cvisc,eff + 2L
πx (4)
× sin
keff =
Eapp −
μo μr
tsens + 2Esludge
+ 2ρsludge tsludge
meff = ρsens tsens + 1.98ρfl
cvisc,eff = 2μ
+ αρsens tsens
chyst,eff = β Eapp −
μo μr
and L is the half-length of the sensor, while x is measured
from the midlength of the sensor. Results for various loads on a
2 × 37.5-mm 2826MB ribbon sensor are shown in Fig. 4. Note
that the simulation uses values for paraffin as a load [27], rather
than values for sludge. This is to facilitate comparison with
experimental results in this paper, as described in Section VI.
Note that biofilms like sludge are likely to be less stiff than the
test materials used in this paper [28]. According to the model,
mass loads that are less stiff tend to have a smaller effect on the
amplitude of displacement, which is proportional to the induced
voltage on the receive coil in the wireless setup [16]. Thus, the
sludge simulants used in this paper are likely to have worst case
effects on signal amplitude and full-scale range.
It should be noted here that due to the coupling between
stress, strain, field strength, and flux, the sensor will exhibit not
only a mechanical resonant frequency, as can be determined
from (4), but also an electromagnetic antiresonant frequency
when measured with a pickup coil. This phenomenon is given
analytical treatment in [16].
The aforementioned analysis provides insight into the operation of ribbon sensors, which have a simple longitudinal
vibration mode shape. Because the wishbone-array sensor pattern that is used in the second-generation system presented in
this paper represents a significant departure from typical ribbon
sensors, we developed an FEA tool that is appropriate for
estimating mode shapes and expected signal amplitudes from
sensors with complicated structures. The primary component of
this tool is the use of (1) and (2) to establish coupling between
the magnetic and structural physics domains. For this work,
the FEA code is implemented in COMSOL Multiphysics. A
detailed look at an FEA implementation for magnetostrictive
materials is in [29]; the approach used in this work is modified
for specific application to resonant sensors by utilizing timeharmonic (frequency-response) structural and magnetic analysis modes.
The desired sensor geometry is modeled in the FEA program,
along with the geometry of the transmit coil. For the purposes
of this paper, the FE code was first verified using data from the
coaxial test setup (explained in Section V) and a 2 × 37.5-mm
2826MB ribbon sensor. The current in the transmit coil was
measured and applied in the model to generate the field at
the sensor. The flux from the sensor response was integrated
numerically over the volume of the receive coil to establish the
induced voltage. The frequency of the current was swept over
an appropriate range, and in this manner, the voltage frequency
response of the system could be calculated and compared to
experimentally obtained data. Values available in the literature
for apparent modulus, permeability, and magnetostrictivity of
the material were used, along with reasonable values for proportional damping. Small modifications to the literature values
for the material parameters resulted in excellent fit with experimental data, as shown in Fig. 5. These modified values were
then used to analyze the complex wishbone-array structure
that is used in the second-generation system presented in this
paper, with excellent agreement in predicted resonant frequency
(within 2% of experimentally measured values for all mode
shapes). Trends in the experimentally measured amplitude are
also predicted by the FEA (Fig. 5). Also note that displacements
calculated with the analytical model for the ribbon sensor
match those predicted by the FEA within 10%; the discrepancy
between the two is mainly due to a coupled shape function ϕ
that is slightly larger than the shape function calculated with a
magnetostatic analysis and used in the analytical model.
In Fig. 6, calculated mode shapes for planar wishbonearray sensors are shown. The mode shapes displayed are at
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Fig. 6. FEA-calculated mode shapes for a planar wishbone-array magnetoelastic sensor, occurring at the listed frequencies. Note the difference between
these shapes and the purely extensional mode of ribbon sensors.
A. Generation-1 System
Fig. 5. (a) FEA-calculated and FEA-measured electromotive force (EMF)
for a 2 × 37.5-mm 2826MB ribbon sensor. The measured data were used to
optimize parameters in the model (stiffness, damping, and magnetomechanical
coupling coefficient). (b) FEA-calculated and FEA-measured EMF for a planar
wishbone-array sensor, utilizing the optimal parameters from (a). Note that both
resonant and antiresonant behaviors are captured, and predicted frequencies
match the measured frequencies within 1.25%. General trends in signal amplitude are also predicted. The discrepancy between the simulated and measured
baseline values (far from resonance) may be due to coupling between the
transmit and receive coils that is not captured in the finite-element model or
due to frequency dependence of the sensor permeability.
frequencies corresponding to peaks in the calculated frequency
response for the planar wishbone-array sensors, with the mode
shape at 61.6 kHz resulting in the largest response amplitude.
Note that the mode shapes combine significant longitudinal and
transverse motions, whereas mode shapes of traditional ribbon
sensors are limited to longitudinal motion. It will be seen that
the transverse motion in these mode shapes leads to a higher
sensitivity response of the wishbone-array sensors to viscosity
A final tool used for the design of the wishbone-array sensors
was structural FEA to determine the amount of deformation
that the sensors could experience without resulting in plastic
strain. This is pertinent in our application because biliary stents
generally reach their final in situ diameter via an elastic selfexpansion after a catheter-based delivery. Thus, any sensor integrated with the biliary stent will need to withstand compression
onto the delivery system and any subsequent expansion without
performance degradation.
The first-generation system includes a ribbon sensor, a stent,
and discrete neodymium magnets that bias the sensor; refer
back to Fig. 2(b) for a conceptual sketch of the system. Most
commercial self-expanding biliary stents (e.g., those offered
by Boston Scientific) rely on elastic expansion of a braided
metal mesh. Producing the mesh by braiding filaments limits
the inclusion of features that allow the attachment of sensors for
enhanced functionality. An investigation into alternative stent
designs and fabrication approaches is thus warranted.
Previous work in our group has investigated the advantages
of a planar approach to fabrication of cardiovascular stents
[30]. In this biliary stent system, a planar approach to fabrication facilitates the inclusion of mechanical features for sensor
attachment and stent seam closure. The sensor attachment
points in this work take the form of hooks [Fig. 7(b)]. Prior
to attaching the sensor, the hooks are folded up so that they
stand out of plane. In this manner, the hooks act not only as
an attachment point but also as a standoff that provides an
operating gap between the sensor and the stent sidewall. The
basic pattern of the stent is a diamond mesh, similar to the mesh
formed after braiding filaments in the commercial design. The
side length of each diamond is nominally 1 mm.
As noted in Section II-A, the resonant frequency of the sensor
can be altered by a change in the biasing magnetic field as a
consequence of the ΔE effect. In our application, it is envisioned that all interrogative equipment remains external to the
patient. Because the orientation of the interrogation equipment
with respect to the sensor can vary on a test-to-test or patient-topatient basis, a dc biasing field emanating from this equipment
adds an uncertainty that is reduced by integrating the magnet.
Preliminary tests showed that the resonant frequency can shift
by 2% with a change in dc bias field orientation of ∼45◦ .
The permanent magnets must be integrated in such a way
as not only to provide a magnetic field of sufficient uniformity
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Fig. 7. (a) 2 × 25-mm μEDM’d ribbon sensor. (b) Ribbon sensor attachment to the stent with interlocking features. (c) Microfabricated wishbone-array pattern.
(d) Microfabricated self-expanding stent pattern, coated with polymer-suspended SrFe. Notice the similarity in the sensor and stent pattern; this allows for similar
flexibility and expansion capabilities in the stent and sensor. (e) Wishbone-array sensors with varying degrees of curvature achieved by thermally annealing the
sensor while placed in tubes of various radii. (f) Wishbone-array sensor integrated with SrFe-coated stent and coated with acrylate terpolymer (∼2.3× the sensor
mass). The stent seam is not bonded here for visual clarity.
and magnitude to properly bias the sensor, but also to minimally interfere with the expansion of the stent and allow
an open lumen to be maintained. For the 37.5-mm-long ×
2-mm-wide sensors used in this paper, a bias field strength of
2.5–3.5 Oe is necessary for optimal signal amplitude. A finiteelement simulation was performed (Ansoft Maxwell 11.1)
using two circumferential rings of six 0.8-mm-thick ×
1.6-mm-diameter neodymium magnets (K&J Magnetics).
Simulation results show that this configuration can achieve
sufficient field strength and uniformity for the needs of this
The majority of the reports on the use of rare-earth permanent magnets in biomedical implants are focused on intraoral
magnets for dentistry and orthodontic purposes. Although the
intraoral environment is not identical to that of the bile duct,
these investigations do allow some conclusions to be drawn regarding the biocompatibility of rare-earth permanent magnets.
Literature notes that although bare neodymium magnets are
susceptible to corrosion in saliva—particularly in the presence
of bacteria—a parylene coating is effective in protecting against
corrosion [31]. Additional research shows that the response of
buccal mucosa (the lining of the cheeks and lips) exposed to a
magnetized implant with 80–140-Oe field strength is negligibly
different from the response of the same tissue exposed to
a demagnetized implant [32]. These findings suggest that a
passivated rare-earth magnet of the size and strength that we
require does not pose a significant biocompatibility risk.
The basic sensor shape is a ribbon of constant cross section,
2.5 mm wide and 37.5 mm long. The attachment feature on the
sensor is an integrated clip at the ribbon midlength. In this work,
the clip consists of three beams coupled at the ends [Fig. 7(b)].
This clip is aligned over the hooks on the stent and then brought
into engagement with the hooks. As the engagement length
increases, the outer beams are deflected over the hooks and
eventually snap elastically back into place under the hooks. The
middle beam remains on top of the hook and keeps the other
beams engaged with the hooks.
B. Generation-2 System
The discrete approach to the magnet and sensor components
in the first-generation system is but one design option. Another approach is to use distributed components. Components
that conform to or mimic the open flexible structure of the
stent would lead to a system that is better able to withstand
and accommodate the deformations required during catheterbased delivery, as well as lead to a system that preserves
the structural functionality of the stent. With this viewpoint,
the second-generation system utilizes a stent coated with a
biasing permanent-magnet layer. The sensor used in the secondgeneration system also conforms to the meshed topology and
tubular curvature of the biliary stent.
To improve the elasticity of the stent, chrome–nickel Elgiloy
is used. This material has a much higher yield strain than stainless steel (∼1% for Elgiloy versus ∼ 0.15% for 316L stainless
steel [33]). Elgiloy is commonly used in self-expanding biliary
stents due to the high yield strain and low corrodibility. As
shown in Fig. 7(d), an elongated wishbone-array pattern is used;
this pattern allows good mechanical flexibility for the stent in
a one-piece planar design that can be batch fabricated. This
pattern is also used in the sensor, as will be explained later.
Sensor performance is generally improved when the bias
field is as uniform as possible. This uniformity is difficult
to achieve with integrated discrete magnets because the field
strength will necessarily decay as the distance from the magnets
increases. However, if the magnetized portion of the system
were to be continuously distributed, the field strength could be
maintained more uniformly. This improves the sensor performance and eradicates high magnetic field gradients that lead to
undesirable magnetic forces.
The distributed magnet is chosen in this paper to be
a layer of strontium ferrite (SrFe) particles (∼1-μm average diameter, Hoosier Magnetics, Inc.) suspended in PDMS
(Sylgard 184, Dow Corning). This choice is made again in
keeping with minimally altering the functionality and structure
of the biliary stent with the additional components. In this case,
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the polymer-suspended particles can be applied in a thin flexible
layer conforming exactly to the stent structure [Fig. 7(d)].
Other polymers have been used as a base for SrFe particles
in microfabricated magnets described elsewhere [34]. SrFe
particles have the advantages over other magnetizable candidate
materials of being chemically inert (owing to their ceramic
nature) and of being widely and inexpensively available in
very small particle sizes. The chemical inertness is particularly
valuable in our implantable application. PDMS is chosen as a
base material in this paper due to its generally accepted biocompatibility and due to processing ease. In fact, the entire polymersuspended magnet fabrication process (as will be described
later) is preferable in terms of ease compared with alternative
options such as sputtering or electrodeposition of a thin-film
magnetic layer. It is well known that PDMS tends to absorb
moisture [35]. However, this is not expected to significantly
affect the magnetic performance of the layer due to a number
of reasons. First, the inertness of the SrFe particles minimizes
the potential for the absorbed moisture to alter the magnetic
properties of the material. Second, the mechanical deformation
of PDMS due to solvent absorption is small (on the order of 2%
in common acids and bases [36]), so accompanying geometrical
changes in the magnetic field are expected to be insignificant in
this application. Finally, the SrFe–PDMS layer can be coated
with parylene to further enhance biocompatibility and reduce
moisture penetration.
In keeping with the philosophy of mimicking the design of
the stent with the design of the magnetoelastic sensor, we would
like to use a sensor material with superior elastic properties and
to shape the material in diamond-shaped patterns. Fortunately,
Metglas alloys are materials with excellent magnetostrictive
properties as well as excellent elastic properties. For instance,
the 2826MB alloy, as used in this paper, is reported to have a
yield strain of 1.6% [37]. This value is even higher than the
∼1% yield strains of cold-reduced Elgiloy [38]. Metglas is not,
however, readily available in filament form. It is also likely that
a resonant sensor fashioned from braided filaments would have
low structural coupling and high damping at braid crossover
points, limiting efficiency as a resonator. Again, an elongated
wishbone-array pattern is used which enhances flexibility while
maintaining mechanical coupling and minimizing joint damping for efficient resonant operation of the sensor [Fig. 7(c)].
To ensure that plastic strain in the sensor is avoided during
deformation that is required during catheter-based delivery, an
FEA model is utilized, with results shown in Fig. 8. With
the fabricated dimensions, FEA suggests that the wishbonearray sensor can undergo a 50% reduction in circumference
without plastic strain that may result in degradation of sensor
A. Generation-1 System
For this work, stents are batch fabricated from planar
316L stainless steel foil patterned by photochemical machining
(PCM). PCM, or chemical blanking, is a process that utilizes a
laser-defined mask to lithographically pattern a photoresist (PR)
Fig. 8. FEA-calculated strain in a single wishbone cell during cell deformation. The deformation shown corresponds to that required for a reduction
in diameter of 50% for the curved array. Only a small area of the wishbone
structure exceeds the yield strain with this deformation.
Fig. 9. (a) PCM process flow. (b) Second-generation system fabrication
process. (1) PCM patterning of Elgiloy (stent) and Metglas (sensor). (2) Stent
coated in SrFe–PDMS layer and magnetized. Sensor annealed in a tube.
(3) Sensor anchors bonded to the stent with PDMS. (4) Stent seam bonded
with PDMS.
layer covering the base metal layer [39]. The PR is developed,
and the unprotected metal is etched with a heated spray of
etchant. In this way, intricately patterned flat metal parts can
be produced, with thicknesses ranging from 10 μm to 1.6 mm
[Fig. 9(a)]. The base metal sheet is typically 300 × 450 mm,
although larger sheets can easily be processed.
PCM is derived from printed circuit board etching processes,
so the most common PRs and etchants are geared toward
processing copper. However, with appropriate surface treatment
(to promote PR adhesion) and modified etchants, many other
metals can be processed. The PR is typically laminated onto
both sides of the base metal layer, and both sides are exposed simultaneously. Typical etchants include ferric chloride,
cupric chloride, sodium hydroxide, hydrochloric acid, or even
hydrofluoric acid for particularly chemically resistant metals.
Among others, copper, aluminum, stainless steel, nickel alloys,
platinum, tungsten, and even titanium can be processed with
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an appropriate etchant. The etching is an isotropic process, so
masks are adjusted for undercut based on the thickness of the
part. Feature sizes are constrained by the workpiece thickness,
and the minimum feature size is approximately equal to the
thickness. For a 50-μm-thick foil, as used in this paper, lateral
feature sizes are as small as 33 μm, with external radii as small
as 22 μm. Tolerances of ±10% of the part thickness are typical. Material removal rates are approximately 10–50 μm/min,
although looser tolerances can allow for faster etching.
The main advantages of PCM include the ability of burr-free
processing of thin metal without distortion or other physical
changes to the workpiece. Additionally, very hard and brittle
metals are machined without difficulty. Of importance to this
work, magnetically soft materials can be processed with PCM
while retaining optimal permeability—indicating that PCM is
an option for processing the magnetoelastic sensor material.
Chief disadvantages of PCM include the limitations imposed
by the chemistry of the workpiece, as well as the fact that sharp
radii cannot be produced due to the undercut involved in the
isotropic process.
The thickness of the magnets can be controlled—as may be
necessitated by the required bias field—by utilizing a lapping
procedure with a diamond slurry. The permanent magnets can
be attached to appropriate portions of the stent using a biocompatible adhesive or epoxy. For this preliminary work, a quicksetting epoxy was used to affix the magnets. The magnetoelastic
sensors are patterned from 28-μm-thick planar Ni/Fe alloy foil
(Metglas 2826MB) by μEDM.
System Assembly: The sensor is attached and the permanent
magnets are affixed while the stent is in a planar state. In order
to prop open the bile duct as intended, the planar stent must be
shaped into a tube. The PCM process allows for deformable
mechanical features to be placed on the lateral edges of the
planar stent. When the stent is rolled into a tubular shape,
these mechanical features can be interlocked such that the
tubular shape is maintained. The mechanical features remain
inside the tubular profile of the stent, so they do no damage
to the duct wall. The stent in its final assembly state is shown
in Fig. 7(a).
B. Generation-2 System
The stent is batch fabricated using the PCM process, in this
case from a 100-μm-thick foil of Elgiloy. As intended, the
feature sizes and patterns are identical to those of the sensor
[Fig. 7(c) and (d)]. The overall stent size is 5 mm (diameter) ×
40 mm.
To form the conformal magnetic layer, the PDMS is first
mixed in a 10 : 1 base-to-curing-agent ratio. Subsequently, the
SrFe particles are introduced in 1 : 1, 3 : 1, or 1 : 3 SrFe-toPDMS by weight ratios and mixed in by hand until the mixture
is consistent (usually about 1 min of mixing time). The mixture
is then poured or spread into a mold containing the stent.
The stent is then peeled out of the mold, with a conformal
layer of the magnetic suspension adhered. The layer is then
cured for 30 min at 60 ◦ C. Thicker layers can be built up by
repeating the process, although thermally treating the sensor
reduces the field—and thus the thickness—required (as shown
in Section VI-A). Finally, the layer is magnetized uniformly
along the long axis of the stent using a benchtop pulse magnetizer. In general, the 1 : 1 SrFe:PDMS ratio offered the best
combination of workability and remnant strength of the ratios
The wishbone-array sensors for this work are batch fabricated from a 28-μm-thick foil of 2826MB Metglas utilizing the
PCM process. Feature sizes of the individual struts are 100 μm.
The overall size of the active portion of the sensor (not including the anchor areas to be discussed later) is 7.5 × 29 mm.
PCM is a planar process, so the as-fabricated sensors are
also planar. Because the stent application calls for a generally
tubular shape, and the lateral dimension of the sensor is larger
than the diameter of the stent, the sensor must be curved
into a tubular or semitubular shape to best match the stent
geometry. Initial attempts to add curvature to the sensor via
elastic bending (e.g., by rolling the sensor and stent into a tube)
resulted in a resonant frequency shift and a severe decrease in
the amplitude of the sensor signal. This effect is thought to be
the result of a combination of mechanical stress imposed in the
material as well as geometrical changes in the mode shapes.
Instead, the tubular shape is achieved in this work by placing the
sensor against the inner wall of a fixture tube and annealing at a
high temperature for 30 min [Fig. 7(e)]. By annealing the sensor
in the curved state, mechanical stress in the material is relieved
while the desired shape is maintained. Note that this thermal
treatment is applied to the sensor only, separate from the coated
stent. Various final radii can be achieved by either changing the
radius of the fixture tube or changing the anneal temperature.
For instance, a 4.6-mm radius results from annealing at 375 ◦ C
for 30 min inside a 3.6-mm-radius tube, while a 1.6-mm radius
results from annealing inside a 1.25-mm-radius tube. Lower
temperatures lead to lesser final curvature. The manner in which
these treatments affect the sensor performance is detailed in
Section VI-A.
System Assembly: Lateral portions of the wishbone-array
sensor are connected to the active area with single struts. These
portions act as anchors, and the thin flexible struts isolate the
vibrating active area from the anchors. The anchors are bonded
to the stent with a thin layer of PDMS. Mechanical changes
in the PDMS (due to solvent absorption) are not expected to
affect the resonant frequency of the sensor because the vibration
of the active sensor area is decoupled from the PDMS by the
struts. Subsequently, the stent is rolled into a tubular shape, and
the resulting seam where the edges of the stent adjoin is also
bonded with a thin layer of PDMS. The assembly process is
shown in Fig. 9(b), and an assembly is shown in Fig. 7(f).
Two different wireless coil configurations are used to evaluate the performance of the systems, as shown in Fig. 10. In each
case, the transmit and receive coils are driven and measured
by an HP 4395A Network Analyzer. The output signal from
the 4395A is amplified prior to reaching the transmit coils.
The extracorporeal configuration is intended for use in the final
application, after the system is implanted. In this setup, coils
are configured such that the transmit and receive coils can both
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Fig. 10. (a) Coaxial coil configuration. Dual Helmholtz coils (not shown)
on either end of the transmit and receive coils can be used to provide a
uniform and well-controlled bias field to isolated sensors. (b) Extracorporeal
coil configuration. In both configurations, a vial filled with viscous media
simulates bile, and in the extracorporeal setup, a 7.5-cm-thick package of
bovine tissue can be used to simulate the potential signal attenuation due to
the intervening tissue of the patient. Differences between the test setups in
the measured signal amplitude were consistent regardless of sensor geometry,
sensor material, or sensor environment.
couple to the sensor, but not with each other. The interrogative
field lines emanate from one transmit coil, loop through the
sensor position such that the field lines are longitudinally
aligned with the sensor, and terminate in the other transmit coil.
The response field lines emanate from the sensor and are in
essentially the same pattern as they would be if emanating from
a dipole magnet. Thus, the receive coils are located at a null
point of the transmitted signal, are oriented perpendicular to
the direction of the transmit field, and yet are aligned with the
resulting field from the sensor. This arrangement helps to decouple the transmit signal from the received signal, improving
the signal-to-noise ratio. The systems are tested in this setup,
both with and without a 7.5-cm-thick package of bovine tissue
surrounding the system. The bovine tissue is intended to mimic
the intervening tissue of a patient in which the system has been
implanted and to help assess any signal degradation due to such
The coaxial configuration is used for benchtop testing and
mainly for initial evaluation purposes. In this setup, transmit
and receive coils are concentrically oriented. The device under
test is also placed in the center of this setup. With this setup,
isolated sensors can be evaluated using a uniform but variable
bias field applied by Helmholtz coils located coaxially with the
long axis of the sensor, outside the transmit and receive coils.
The main advantages of this setup over the extracorporeal setup
are that it easily achieves a higher signal-to-noise ratio and that
recorded signal amplitudes are less dependent on precise sensor
positioning. However, this setup cannot be used to replicate
conditions when the sensor is fully implanted, as concentric
coils of the size used would lie inside the patient—which is
an obviously undesirable situation. The bile duct runs approximately vertically in an upright patient, so a coaxial wireless
setup could be used if the transmit and receive coils were large
enough to fully encircle the patient (see [9] for a potential configuration). Important parameters for both the extracorporeal
and coaxial test setups are listed in Table III.
When evaluating the field strength of the magnets or magnetic layer, two methods are used. First, a Hall probe (Analog
Devices, Inc. AD22151) gives an estimate of the field strength,
although the probe does average over a relatively large (0.5 ×
0.5-mm) area. For the first-generation system, this method is
sufficient to get a clear picture of the field in the sensor area, as
the sensor is located in a uniform portion of the field resulting
from the discrete magnets. For the second-generation system,
in which the sensor is located very near the magnetic layer,
the spatial averaging due to the size of the Hall probe is more
of an issue. To address this, the second method of evaluating
the field strength is to use a small (1-cm × 2-mm) ribbon
sensor, the performance of which has been characterized for
various uniform biasing fields. The small sensor can be placed
in different locations along the length of the stent and near the
magnetic layer, and the resulting frequency and amplitude of
the sensor can be correlated, using the characterized performance in a uniform field, with the local field provided by the
magnetic layer. In this way, the ΔE effect of the material is
used to evaluate the strength and uniformity of the fabricated
magnetic layer.
Bile viscosity changes are precursors to sludge accumulation
[40], and bile viscosity varies with sludge content from 1 to
14 cP [41], so sensor response to viscosity is evaluated. The
first-generation system is tested by placing the system in a
glass vial (2.5-cm inner diameter) filled with water with varying
sucrose content [42]. The glass vial is then placed in the
appropriate test setup. The viscosity response of the second
system and of isolated sensors is tested by using fluids of known
viscosities (Dow Corning) in a polycarbonate test vial (1.25-cm
inner diameter).
As described in Section II-B, accumulation of sludge
results in a mass loading effect on the sensor. For benchtop
testing purposes, this process is simulated by the application
of one of two different materials—paraffin and an acrylate
terpolymer—to as-cast and thermally treated wishbone-array
sensors, as well as to as-cast 2.5 × 37.5-mm ribbon sensors.
The paraffin is applied by repeated dip coating. The acrylate
terpolymer—commercially available dissolved in a solvent—is
either sprayed onto the sensor or brushed on. The two sludge
simulants allow a reasonable variation in mass distribution,
which is important because sludge accumulation in situ is
not likely to be perfectly uniform. Additionally, the simulants
possess different mechanical properties, so variation in sensor
performance due to these differences can be quantified.
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A. Isolated Sensors
Prior to integration, sensors were tested without the stent in
the coaxial test setup while biased with dual Helmholtz coils,
as described in Section V. Initial evaluation of four as-cast
planar wishbone-array sensors showed that the frequency and
amplitude responses versus bias field were similar across the
sensors for all important modes, indicating a repeatable PCM
fabrication process.
To evaluate the effects of thermal treatments that are used
to give the sensors curvature, the wishbone-array sensors were
thermally treated either above (375 ◦ C) or below (325 ◦ C) the
material Curie temperature (353 ◦ C) and either remained planar
or were given curvature. Posttreatment evaluation showed lower
optimal biasing field (∼1.5 Oe versus 5-Oe pretreatment) and
improved signal level (up to 13.5 mVp-p versus 9 mVp-p pretreatment). This important result shows that thermal treatment
allows the use of thinner SrFe–PDMS layers, which simplifies
fabrication and minimizes system size, and minimizes concerns
about large chronically implanted magnetic fields.
As-cast and thermally treated wishbone-array sensors were
compressed through 1.5-mm-diameter tubes—a circumferential deformation of at least 37%—without signal degradation.
The repeatable performance of this test across both as-cast and
thermally treated sensors implies that the thermal treatment
process does not lead to impaired mechanical properties. The
slight discrepancy with the FEA model predictions may be due
to an imperfect correlation between the onset of plastic strain
and the onset of strains that change the magnetomechanical
properties of the material.
As-cast and thermally treated wishbone-array sensors, as
well as as-cast 2.5 × 37.5-mm ribbon sensors, were evaluated
for response to mass loading with both paraffin and acrylate
terpolymer. As shown in Fig. 11, each of the sensor types
reacts similarly in terms of resonant frequency to both sludge
simulants. The variability in the frequency data is likely due
to changes in the distribution of the mass from run to run.
Repeated tests with a loaded sensor showed a signal amplitude
repeatability of 10%–20%, while the frequency repeatability
Fig. 11. As-cast planar and thermally treated curved wishbone-array sensors,
as well as as-cast ribbon sensors (2826MB, 2 × 37.5 mm), were loaded
with either paraffin or acrylate terpolymer layers to simulate sludge accumulation. The curve in the top graph is calculated by minimizing the sum of
squared errors between all points and the line using an equation of the form
shown. Results for the analytical model (utilizing literature values for paraffin
properties) are also superimposed. Lines in the bottom graph are guides to
the eye.
was 0.2%–0.5%. Furthermore, the full-scale range of each
sensor type extends into the “critical zone.” Based on the initial
mass of each sensor type, and assuming that the sensors are
integrated with 4-mm-diameter stents, the critical zone corresponds to at least 50% occlusion by a uniform layer of sludge.
Also, note that the initial amplitude of the ribbon sensors is
higher than the initial amplitude of the wishbone-array sensors.
However, the amplitude of the ribbon sensors decreases nearly
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in Section II-B. This mass load represents 2.5× the unloaded
mass of the sensor. If the mass is assumed to be distributed in
a uniform thickness on the sensor and if the sludge is also uniformly distributed on the stent with the same thickness, then this
mass load would represent a significant 29.7% occlusion of a
4-mm-diameter stent due to sludge accumulation. Additionally,
it can be seen from Fig. 12(b) that the frequency sensitivity of
the sensor has not saturated at this mass load, so the full-scale
range of the sensor figures to be even greater than that reached
in this test, as illustrated with isolated sensors in part A of this
Other important results for this system include the magnetic field delivered by the integrated neodymium magnets,
as well as the retention force that is provided by the sensor
attachment features. For two rings of six 1.6-mm-diameter ×
800-mm-thick neodymium magnets separated by 45 mm, the
field measured with the Hall probe was approximately 3 Oe.
To test the retention force of the sensor attachment features,
three sensors were attached to three different attachment points
on three different stents. The stents were held fixed while a
tensile load was applied to the sensor at the midlength of the
sensor. The tensile load was measured with a load cell (Imada,
Inc.), and peak values were recorded. The minimum tensile
force withstood for the three samples was 0.367 N, and the
attachment is robust enough to withstand simulated catheter
Fig. 12. (a) Frequency versus viscosity for an integrated generation-1 system.
(b) Frequency versus mass loading for an integrated generation-1 system. The
system is immersed in DI water (1-cP viscosity), and the sensor is loaded with
linearly with increased mass loads (with a significant slope
that is related to the properties of the loading layer), while
the amplitude of the wishbone-array sensors seems to quickly
decrease to a plateau and hold that level over nearly the entire
tested range. Note that the analytical model predicts that signal
amplitude for a ribbon sensor will be decreased most with mass
loads from stiffer materials with a high loss modulus; thus, it
may be reasoned that the acrylate terpolymer used in this paper
is stiffer and has a higher loss modulus than the paraffin.
B. Generation-1 System
As described in Section III-A, the first-generation system
includes a ribbon sensor, a stent, and discrete neodymium magnets that bias the sensor. The viscosity response of the system
was measured over a physiologically appropriate range using
the extracorporeal coil configuration. As shown in Fig. 12(a),
the resonant frequency of the magnetoelastic sensors shifted by
a total of about 1.7 kHz, or 2.8%, when the viscosity ranged
from 0 to 12 cP. This demonstrates sensitivity to viscosity
shifts that precede sludge accumulation. This also highlights
the importance of having an integrated magnetic field with a
constant orientation; as described previously, the ΔE effect can
easily result in a 2% frequency shift with a 45◦ change in fieldto-sensor orientation.
Paraffin loading resulted in resonant frequency shifts of
24 kHz, or 40.7%, after a mass of 45 mg has been added, which
is in very good agreement with the analytical model proposed
C. Generation-2 System
As described in Section III-B, the second-generation system
consists of a curved wishbone-array sensor and a conformal
SrFe–PDMS magnetic layer. To closely compare results for the
integrated system to the results obtained for the isolated sensors
as described in part A of this section, the coaxial test setup was
used. The external dual Helmholtz coils were not used, so the
sensor was biased only by the integrated magnetic layer.
The viscosity response of the system was measured over a
physiologically appropriate range even as mass was added. As
shown in Fig. 13(a), the resonant frequency of the wishbonearray sensors shifted by about 6.5% typically over this range of
viscosities and was not significantly affected by mass buildup.
Note that the sensitivity over this range is approximately double that of the ribbon sensors. This sensitivity difference is
likely due to the different effects that viscous damping has
on the transverse motion of the wishbone-array mode shapes.
Note that as mass builds on the sensor, the normalized signal
amplitude becomes less sensitive to viscosity. This is a trend
predicted for the ribbon sensor in the analytical model.
The system was also tested for response to mass loads by
successively coating the sensor in acrylate terpolymer. Because
of the close integration between the sensor and the stent, it
was difficult to apply mass directly to the sensor without also
applying some to the stent. As such, the actual load on the
sensor was back-calculated from the frequency response curve
calculated for the isolated sensors (shown in Fig. 11). Note
that the trend seen in Fig. 11 for the wishbone-array sensors
in which the amplitude quickly decreases to a plateau is also
seen for the integrated system in Fig. 13(b).
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Fig. 13. (a) Response of the generation-2 system to viscosity changes was
measured even as mass was added. The resonant frequency of the sensor
remains sensitive to physiologically appropriate viscosity changes even with
increasing mass loads. Note that as mass is added, the signal amplitude is less
sensitive to viscosity changes. (b) Mass was added to the generation-2 system
with acrylate terpolymer. The mass that was added directly to the sensor was
difficult to separate from the mass that was added to the stent, so the equation
shown in Fig. 11 was used to back-calculate the mass load. The signal amplitude
of the integrated sensor responds in a manner similar to that of the isolated
sensors in Fig. 11.
Both of the systems described in this paper exhibited satisfactory performance in critical benchtop tests of frequency
response to viscosity and mass loading. The analytical and
finite-element models developed for estimating the frequency
and amplitude of the response of the sensors, although linearized, proved to be predictive and offer useful insight into
the operation of the resonant sensors. Wherever possible, batch
fabrication techniques were used. In fact, the techniques used
for fabrication of all components in the second-generation
system are batch compatible.
Although this paper focused on the integration of sensors
with self-expanding metal stents, it is important to note that
these sensors can also be integrated with commonly used plastic
biliary stents. These plastic stents are essentially polyethylene
or polytetrafluoroethylene tubes, typically of 2.5–4-mm-inner
diameter and 8–10-cm length. To accommodate the smaller
inner diameter of a plastic stent, the sensor width should be reduced, which lowers the signal amplitude of the sensor. For instance, a 1-mm-wide × 37.5-mm-long ribbon sensor integrated
with a plastic stent exhibits signal amplitudes of approximately
half those of the 2 × 37.5-mm ribbon sensor for the same
normalized mass load. These reduced signal amplitudes are still
sufficient for detection at a 7.5-cm wireless range. Normalized
resonant frequency sensitivity to paraffin loads for the 1-mmwide sensor is indistinguishable from that of the 2-mm-wide
Three advantages of the wishbone-array sensor over typical
ribbon sensors in this application are evident from the results.
First, the fine feature sizes and large open area of the pattern facilitate bile flow, supporting the primary function of the biliary
stent. Second, these sensors are much more accommodating
of the large deformations required for catheter-based delivery. Third, these sensors have a higher sensitivity to viscosity
changes, which is a clinically relevant parameter in many pathological conditions. A limitation of the wishbone-array sensor, at
least with the present design, is the smaller signal amplitude.
However, since the readout involves a frequency shift, this
does not have a direct impact on the utility of the instrument.
Preliminary results show that the signal amplitude tends to scale
with the overall sensor length, so this disadvantage may be
mitigated with a longer sensor design, within the constraints
of the length of a typical biliary stent (≥ 40 mm).
Although this paper has shown that wireless magnetoelastic sensing holds fundamental promise for monitoring critical
parameters in biliary stents, some secondary issues associated with the application have yet to be formally addressed.
One secondary issue is that the amorphous metal has a tendency to corrode. It is not yet clear whether the corrosion products are cytotoxic; however, accelerated testing has
shown that the signal amplitude of 6 × 12.5-mm ribbon
sensors decreased by 90.7%, while the resonant frequency
decreased by 14%—accompanied by visual evidence of extensive corrosion—after a simulated 12-month immersion in
saline. In comparison, sensors coated with 8 μm of parylene
and subjected to the same testing showed an average resonant
frequency increase of 0.2% and an average signal amplitude
increase of 8%—both similar to the repeatability of the sensors.
The act of coating the sensor with parylene reduced the signal
amplitude by 35%, but thinner coatings may provide sufficient
passivation with a smaller effect on the signal amplitude. Another secondary issue is that local curvature of the bile duct
may result in contact between the sensor and stent, which may
degrade the signal. For instance, imparting a longitudinal radius
of curvature of 75 mm to a 37.5-mm-long ribbon sensor via
contact resulted in an amplitude decrease of 30%–40%. Future
testing should also include quantifying the flexibility and selfexpandability of the system, both on the delivery device and
after deployment. In vivo testing will confirm the useful signal
range in an implanted situation, and the response of the sensor
to biologically accurate conditions will be exposed.
Systems that integrate biliary stents with magnetoelastic sensors and permanent magnets are investigated for wireless monitoring of direct indicators of restenosis, including shifts in bile
viscosity and accumulation of biliary sludge. Two generations
of the system are focused on in this paper. The first-generation
system integrates a 37.5 × 2-mm ribbon sensor and neodymium
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magnets with a batch-fabricated biliary stent. Paraffin mass
loads up to 45 mg simulated sludge accumulation, resulting in
a 40.7% resonant frequency shift. As viscosity is varied from
that of healthy bile to that of diseased bile, a resonant frequency
shift of 2.8% was measured.
The second-generation system integrates a flexible
wishbone-array magnetoelastic sensor and a conformal magnetic layer with a batch-fabricated biliary stent. The system
is sensitive to physiologically appropriate viscosity changes,
showing a 6.5% decrease in resonant frequency in 10-cP fluid.
The system is also capable of measuring mass buildup that is
associated with sludge accumulation, showing a 38% decrease
in the resonant frequency after an applied mass load of 2.3×
the mass of the sensor. The integrated system is robust to
deformations required for delivery, provides a uniform biasing
layer that minimally affects stent mechanics, and represents a
much improved form factor over the first-generation system.
Appropriate scaling of this sensing methodology could allow
use in stents of all kinds, including coronary and ureteral stents.
The authors would like to thank Prof. G. Elta and
Prof. R. Kwon of the Division of Gastroenterology, Department
of Internal Medicine, University of Michigan, Ann Arbor, for
the discussions regarding stent usage. M. Richardson assisted
with interrogation system design and implementation. F. Shariff
assisted with testing. Dr. H. Kim assisted with parylene coating
steps. Metglas, Inc., Hoosier Magnetics, Inc., and Dow Corning
provided samples for this project. In addition, Y. Gianchandani
would like to extend thanks for the support through the IR/D
program while working at the National Science Foundation
(NSF). The findings do not necessarily reflect the views of
the NSF.
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Scott R. Green received the B.S. degree in mechanical engineering from RoseHulman Institute of Technology, Terre Haute, IN, in 2003.
He held a position as Senior Design Engineer at Stryker Corporation in
the Instruments Division before attending the University of Michigan for
graduate studies. He has been named as an inventor on two issued or pending
U.S. patents. His research interests include design of medical devices and
micromachined sensors and actuators.
Yogesh B. Gianchandani received the B.S., M.S.,
and Ph.D. degrees in electrical engineering, with a
focus on microelectronics and MEMS.
He is currently a Professor with the University of
Michigan, Ann Arbor, with a primary appointment
in the Department of Electrical Engineering and
Computer Science and a courtesy appointment in
the Department of Mechanical Engineering. He is
temporarily with the National Science Foundation,
Arlington, VA, as the Program Director within the
Electrical, Communication, and Cyber Systems Division. He was previously with the University of Wisconsin, Madison. He also
held industry positions, working in the area of integrated circuit design. His
research interests include all aspects of design, fabrication, and packaging of
micromachined sensors and actuators and their interface circuits. He is the
author of approximately 200 papers in journals and conference proceedings
and is the holder of about 30 issued or pending U.S. patents. He was a
Chief Coeditor of Comprehensive Microsystems: Fundamentals, Technology,
and Applications (Elsevier, 2007). He serves on the editorial boards of several
Dr. Gianchandani was a General Cochair of the IEEE/ASME International
Conference on Micro Electro Mechanical Systems in 2002.
Authorized licensed use limited to: IEEE Xplore. Downloaded on March 4, 2009 at 16:32 from IEEE Xplore. Restrictions apply.
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