Antoine Barbot

Dominique Decanini

Gilgueng Hwang
email: gilgueng.hwang@c2n.upsaclay.fr

Local Flow Sensing on Helical Microrobot for Semi-Automatic Motion Adaptation

Helical microrobots with dimensions below 100 µm could serve many applications for manipulation and sensing in small, closed environments such as blood vessels or inside microfluidic chips. However, environmental conditions such as surface stiction from the channel wall or local flow can quickly result in the loss of control of the microrobot, especially for untrained users. Therefore, to automatically adapt to changing conditions, we propose an algorithm that switches between a surface-based motion of the microrobot and a 3D swimming motion depending on the local flow value. Indeed swimming is better to avoid obstacles and difficult surface stiction area but it is more sensitive to the flow than surface motion as rolling or spintop motion. Firstly, we prove the flow sensing ability of helical microrobots based on the difference between the tracked and theoretical speed. For this, a 50 µm long and 5 µm diameter helical microrobot measures the flow profile shape in two different microchannels. These measurements are then compared with simulation results. Then, we demonstrate both swimming and surface-based motion using closed-loop control. Finally, we test our algorithm by following a 2D path using closed-loop control, and adapting the type of motion depending on the flow speed measured by the microrobot. Such results could enable simple high-level control which could expand the development of microrobots toward applications in complex microfluidic environment.

Introduction

Many potential applications for untethered microrobot moving in liquid have been proposed. They mainly concern biological and medical science to carry out procedure, such as drug delivery [START_REF] Mhanna | Artificial bacterial flagella for remotecontrolled targeted single-cell drug delivery[END_REF][START_REF] Medina-Sánchez | Cellular Cargo Delivery: Toward Assisted Fertilization by Sperm-Carrying Micromotors[END_REF], cargo transport, [START_REF] Tottori | Magnetic helical micromachines: Fabrication, controlled swimming, and cargo transport[END_REF]Zhang et al. 2010a), biopsy [START_REF] Leong | Tetherless thermobiochemically actuated microgrippers[END_REF] or gene transmission [START_REF] Qiu | Magnetic Helical Microswimmers Functionalized with Lipoplexes for Targeted Gene Delivery[END_REF]. However, there are only a few examples in which untethered microrobots were successfully used beyond the proof of concept and were directly applied as a tool. For now, the applications are restrained to delicate handling of small crystals [START_REF] Tung | Protein crystal harvesting using the RodBot: A wireless mobile microrobot[END_REF]) and stress force studies on microorganisms [START_REF] Kawahara | On-chip microrobot for investigating the response of aquatic microorganisms to mechanical stimulation[END_REF]. This is partly due to the difficulty in producing microrobots that can be moved and controlled in an in-vivo environment without being lost in the flow or becoming permanently stuck to a solid surface. As in-vitro condition simplifies the environment, the integration of microrobots into a microfluidic chip to serve as untethered tools has been investigated. Moreover this platform can propose a common environment for microrobot and biological specimens such as cell [START_REF] Hagiwara | Precise Control of Magnetically Driven Microtools for Enucleation of Oocytes in a Microfluidic Chip[END_REF], tissue, or microorganisms [START_REF] Kawahara | On-chip microrobot for investigating the response of aquatic microorganisms to mechanical stimulation[END_REF].

However, to allow a large range of possible microfluidic chip tasks or even potential in-vivo task, the optimization cannot only come from the environment but also from the microrobot design and control method. In our previous work [START_REF] Barbot | On-chip Microfluidic Multimodal Swimmer toward 3D Navigation[END_REF], we show that helical microrobots with different kind of motions have increased robustness and can perform more complicated task. Indeed, the proposed microrobots could choose between propelling itself in 3dimensions (3D) in the liquid (swimming motion) or to propel due to the surface friction with the channel wall in 2D. The different advantages of these two types of motions was essential to succeed in the selective integration (i.e. integration of a single specific microrobot) inside microfluidic chips. In this paper, we propose a semiautomatic control scheme, in which an algorithm chooses instead of the user, the most pertinent motion needed to move along a defined path. The goal is to guarantee that the microrobot cannot be lost due to excessive flow.

For the two motions of our ferromagnetic helical microrobot, the rotation of the magnetic field creates the propelling force and the normal axis of the rotation plane sets the microrobots direction. In our design, a rotating frequency above 50 Hz guarantees that the microrobot aligns perpendicularly along its primary axis with the magnetic field [START_REF] Barbot | Helical microrobot for force sensing inside microfluidic chip[END_REF]. Therefore, when the magnetic field rotates, the microrobot rotates around its axis plane and its direction is perpendicular to the rotating magnetic field plane. A corkscrew design transforms the rotation into a propelling force and allows the microrobot to swim in 3D. It has been demonstrated with different fabrication methods such as self-scrolling (Zhang et al. 2010b), template electrosynthesis [START_REF] Li | Template electrosynthesis of tailored-made helical nanoswimmers[END_REF] or manual assembly for millimetric scale version [START_REF] Honda | Micro swimming mechanisms propelled by external magnetic fields[END_REF][START_REF] Mahoney | Velocity Control with Gravity Compensation for Magnetic Helical Microswimmers[END_REF] 2011). Swimming is the first motion that we choose for our control algorithm.

The second motion relies on surface friction to be less impacted by the fluid flow. Rolling, whereby the microrobot touches the surface with its full length, is too hard to control at lower speeds due to the wobbling phenomena. Therefore, for a simpler control we use the "spintop motion". In this motion, the propelling force pushes the microrobot to the surface of the channel so the contact is made only with one extremity of the microrobot. The angle difference with a perpendicular contact between the microrobot and the surface is then used to control the speed at a constant frequency thus avoiding any wobbling issues. More detail on this motion can be found in our previous work [START_REF] Barbot | On-chip Microfluidic Multimodal Swimmer toward 3D Navigation[END_REF]. It was also reported as a method to move microspheres [START_REF] Ye | Micro-manipulation using rotational fluid flows induced by remote magnetic micromanipulators[END_REF]. The small contact area between the surface and the microrobot that occurs as a result of this motion is also interesting for biomedical applications and has been used for drilling tissue [START_REF] Xi | Rolled-up magnetic microdrillers: towards remotely controlled minimally invasive surgery[END_REF]) and drug delivery [START_REF] Srivastava | Medibots: dual-action biogenic microdaggers for single-cell surgery and drug release[END_REF] with magnetic actuated microrobots.

Figure 1 b) shows schematics of these two motions and illustrates their advantages and drawbacks. Swimming allows movements in 3D whereas spintop motion can be blocked by an impassable obstacle or can make the microrobot permanently stuck to the surface. However, the spintop motion is less sensitive to the external flow speed and prevents loss of the microrobot in the stream if the flow becomes too strong. Therefore, we propose to use the microrobot to measure the local flow speed. Then, depending on this speed measurement, the choice between swimming or spintop motion can be automatically made.

Because it can switch from entirely surface friction propel motion (rolling) to entirely fluid motion (swimming) and still offer intermediate motion (spintop motion) , this microrobot was called Roll-to-Swim (RTS) [START_REF] Barbot | On-chip Microfluidic Multimodal Swimmer toward 3D Navigation[END_REF]. We also refer to the refereed article for the magnetic actuation setup and software control. A Scanning Electronic Microscope (SEM) image of our microrobot is presented in Figure 1 a). We can note that the helix is left-handed. Therefore, in swimming the RTS advances forward when the rotation is anticlockwise. For spintop motion, the direction is mainly set by the friction between one head and the surface. So the direction axis makes approximately a -90 degrees angle with the projection of the RTS axis on the surface. This paper first gives a brief presentation of the fabrication methods of the RTS and the microfluidic chip as well as the steps to integrate one RTS inside the chip. Then, spintop motion and swimming motions are presented as well as their associated closed-loop control system. The swimming closed-loop controller is described in more in detail as it offers a new approach by compensating the gravity in openloop using a prior characterization of the helical microrobot. Then, this closed-loop control is used to demonstrate the ability of the RTS to measure local flows by retrieving different flow profiles in 1 mm high channels.

Finally, this new ability of local flow sensing in swimming motion is used to propose and test a new control algorithm that avoids losing the RTS when the flow becomes too large. The key part of this algorithm is to detect excessive flow conditions and to trigger a transition to continue the desired path along the microchannel surface using spintop motion. The automatization to come back from spintop motion to a swimming closed loop path following is also demonstrated. However, the choice to trigger this transition is for now manual. Indeed the automatization of this task which beneficial to the microrobot robustness requires deeper study of the surface mechanism. This is out of the scope of this paper. Therefore, The objective of this article is to demonstrate that the robustness of the swimming motion under flux is increased by the flow sensing capability. The obstacle detection and avoidance are not the goal of this paper since the principle targeted motion is 3D swimming which can avoid unknown surface stiction. To limit the probability of surface stiction, the spintop motion is preferred to the rolling motion as the contact with the surface is much smaller. However, for large flux, the rolling motion could be an interesting solution as it is even less impacted by the flow than the spintop motion.

System and Control

Microrobot and Chip Fabrication

Our helical microrobot (RTS) is fabricated with twophoton lithography using a commercially available system (nanoscribe: photonic professional). Figure 2 A presents the different steps of this fabrication. First a photo sensitive resist (IPG 780) is deposited on a glass substrate. Then the laser is focused on a precise position to polymerize an elementary volume of resist; this volume is referred as the voxel. By moving the laser, the shape of the helical microrobot can be achieved. Finally, a development step removes the uncured resist. In order to be able to move the microrobot with a magnetic field a Physical Vapor Deposition (PVD) process deposits a ferromagnetic layer on the microrobot. Firstly, 20 nm of chrome is deposited to ensure a good adhesion both at 0 • and 75 • inclinations, then 100nm of nickel is deposited at 0 • and 75 • to form the ferromagnetic layer. This design method of helical magnetic microrobot was firstly reported by Totorri et al. [START_REF] Tottori | Magnetic helical micromachines: Fabrication, controlled swimming, and cargo transport[END_REF] The microfluidic chip is designed with a soft polymer material called polydimethylsiloxane (PDMS). We choose this material for the simplicity of it curing which allows for simple mass production using a master mold. Moreover the transparency of this material is compatible with optic feedback and its elasticity and low Young"s modulus allow for simple valve systems by simply compressing microchannels.

Figure 2 B shows the different fabrication steps of the microfluidic chip. First, the master mold is made with a micromilling machine. Then uncured PDMS mixed with a curing agent is poured on this mold and put in a 75 • C oven to cure for one hour. After this, the PDMS is removed from the mold and openings are performed with a cutting tool to allow fluid injections and an open chamber for the integration of the microrobot. Finally, the chip is closed by bounding the PDMS to a glass substrate with an oxygen plasma treatment.

Figure 2 C sketches the integration of one RTS inside the microfluidic chip. For this integration, the chip is placed on a magnetic setup made with an assembly of three orthogonal pairs of Helmholtz coils. Therefore, this setup can produce a homogenous magnetic field in any direction. To move and control the RTS, we command this setup to produce a rotating magnetic field. Indeed, as the RTS is ferromagnetic it remains aligned with the field and also rotates at the same frequency. This rotation results in the displacement of the RTS, either due to the friction with the surface which results in rolling or spintop motion, or by the interaction of the helical shape with the fluid that creates a propelling force and make the RTS swim in 3D in the fluid. By combining these different motions the mobility of the RTS is increased and it can reach any place inside the microfluidic chip. For this, a tip first detaches one RTS from the fabrication surface placed on an open chamber of the chip. Then, this RTS swims until the entrance of a microchannel connecting the open chamber to the isolated part of the chip. At this point the RTS can move using the friction surface to easily overcome the residual parasite flow on the microchannel. When the RTS is inside the microfluidic chip the connection with the open chamber is closed by compressing the connecting microchannel with a screw. For more details on the fabrication protocol, the control, and integration of the microrobot inside the microfluidic chip we refer to our previous work [START_REF] Barbot | On-chip Microfluidic Multimodal Swimmer toward 3D Navigation[END_REF]. This work also explains in detail the different possible motions of the RTS with their different advantages and drawbacks.

Motion Introduction and Closed-loop control

The goal of this paper is to propose spintop motion as an automatic path following alternative to swimming in strong flow. Therefore, to propose an experimental demonstration we must first develop a closed-loop control for both motions. In this subsection we also want to briefly stress the differences between swimming and spintop motion.

Spintop motion is a friction based motion where only the tip of the microrobot touches the surface. The rotation of the RTS creates a friction force at this contact point which propels it along the surface. As a spintop toy, the RTS moves only if the angle between the surface and its axis is not perpendicular. In other words, if the surface is horizontal and the RTS stands vertically while rotating, it will not move.

A fine model of the spintop motion capable of estimating a quantitative behavior of the RTS is for now impossible. Indeed this motion is a non-linear combination of the microrobot surface friction and the viscous propelling force. The non-linearity is due to the impact of the surface on the flow distribution as well as to the propelling force which increase the friction by pushing the RTS toward the surface. In Appendix B.1, we also report a series of experiments demonstrating that two friction regimes can exist: One which is purely based on viscous friction and another one with a contact friction well modeled by a Coulomb law.

Therefore, we based our control on an empirical model of the spintop motion. In the vicinity of the horizontal (in practice around 20 degrees), we noted that the speed evolves linearly with the change in the pitch angle of θ [START_REF] Barbot | On-chip Microfluidic Multimodal Swimmer toward 3D Navigation[END_REF]) Therefore, we use this region to control the speed of the RTS. Firstly, the position error between the real position and the command position ε x and ε y are sent to two different Proportional Integrative Derivative (PID) correctors with the same tuning. From these PID, we obtain respectively C x and C y which correspond to the two components of a vector, the angle of which set the control direction and the length set the control speed. Therefore, the following control formula for the pitch (θ) controlling the speed arises:

θ = π 2 -C 2 x + C 2 y , (1)
A cap is set on θ value to force the value 0 in case Equation 1give a negative result for large C value. Secondly, the yaw angle ( illustrated by ψ in Figures 3 a) ) is used to set the moving direction of the RTS on the surface. Between this angle which is set by the normal of the rotating magnetic field plane and the RTS direction, there is an angle slightly inferior to -90 degrees ( It would be superior to 90 degrees for a right hand helical microrobot). This is the result of the summation of the rotating component of the RTS head on the surface that provides a rolling like motion and of the propelling force of the helix. To compensate for this effect, an offset is added on the control ψ. Therefore this offset is slightly superior to 90 degrees for a left-hand helix. This offset is also finely tuned manually for now as it can change between different microrobot as well as surface state.

The control formula for ψ is:

ψ = argument(C) + offset, (2)
The spintop motion presents the advantage of being more robust to flow than swimming. Indeed it is less impacted by the mainstream flow as it evolves close to the surface where the flow speed must match a null condition. However, this motion is in 2D and the RTS can be blocked by obstacles and can face the risk to be stuck permanently to the surface. The swimming motion can overcome this issues by using the helical shape of the microrobot to propel the RTS in 3D thanks to the liquid viscous drag.

As our tracking is based on the microscope image, the closed-loop control of swimming motion is limited to the horizontal plane. For now, we propose a closed-loop control on the image plane while an open-loop maintains the RTS at the same altitude by compensating its weight. Therefore, we cannot prevent a small drift of the RTS that slightly changes its altitude with time.

For non-neutral buoyant microrobots, the speed due to gravity adds to the propulsive speed. So the fluid drag which opposes the total speed, has components along both the microrobot direction and perpendicular to this direction. due to the linearity of the flow equation at low Reynold number, the drag (D) can be explicit by the following equation

D = -a V -a ⊥ V ⊥ , (3)
with a and a ⊥ the drag coefficients respectively parallel and perpendicular to the microrobot direction. V and V ⊥ are the components of the microrobot"s speed parallel and perpendicular to the microrobot direction.

A closed loop control of the propulsion and direction using a component of a helical microrobot thrust for gravity compensation has already been demonstrated [START_REF] Mahoney | Velocity Control with Gravity Compensation for Magnetic Helical Microswimmers[END_REF]. For this study we are concerned by controlling the velocity in the horizontal plane. Therefore we have only developed an open loop control to compensate the gravity where the component of the thrust on the vertical axis is set to compensate the vertical falling speed measured during the characterisation of the microrobot. This open loop leads to slight drift in the vertical direction over time.

For the closed loop control, we can consider the projection of the speed on the horizontal plane (which corresponds to our microscope image plane) and by assuming the flow parallel to the RTS direction:

V h = pω cos θ, (4)
with p = -b/a , a and b correspond here to the first two coefficients of the propulsion matrix [START_REF] Purcell | Life at low Reynolds number[END_REF]. In our case b is positive but would be negative for a right hand microrobot.

In practise p is measured just before the experiment to guarantee that intrinsic properties of the RTS are the same (magnetization, shape, quantity of magnetic material). Indeed we experimentally observed that the value of p could evolve with time [START_REF] Barbot | Helical microrobot for force sensing inside microfluidic chip[END_REF] The measurement process of p is partially automatic as the user only has to drive the RTS with the same direction for few seconds while holding a certain key on the keyboard used for control. In this case the microrobot direction needs to be horizontal so the viscous drag on the horizontal plane is only affected by a Thanks to the linearity of the propulsion force with the rotation speed and our assumption on the viscous drag , we can use a simple PID controller by decoupling each direction of space (x, y, z) as shown in Figure 3 b). On each of these directions, we express the propulsion force in the corresponding rotation frequency (ω x , ω y , ω z ). ω x and ω y are calculated with a PID controller fed with the error on x and y between the tracked position and the command position. ω z is the needed value to overcome gravity and it is given by:

ω z = v 0 p , (5)
with v 0 the falling speed of the RTS when the rotation frequency is forced to 0Hz. This value is experimentally determined once for each new RTS in an isolated chamber of the microfluidic chip to prevent parasite flows. Five different measurements are made to check the repeatability. The typical 0Hz speed for our RTS model is around 7.5 µm • s -1 . The standard deviation for a single microrobot is below 0.3 µm • s -1 . [START_REF] Barbot | Helical microrobot for force sensing inside microfluidic chip[END_REF] As the frequency is proportional to the force and therefore the microrobot speed, the control information on (ω x , ω y , ω z ) can be linearly assembled to give the control in terms of (ω, ψ, θ):

ω = ω 2 x + ω 2 y + ω 2 z , (6)
tan ψ = ω y ω x , (7)
tan θ = ω z ω 2 x + ω 2 y , (8)
with ω the rotation frequency of the magnetic field, which corresponds to the rotation frequency of the RTS as the control is made under the step-out frequency [START_REF] Barbot | On-chip Microfluidic Multimodal Swimmer toward 3D Navigation[END_REF] , ψ the yaw angle corresponding to the direction of the microrobot in the horizontal plane and θ the pitch angle corresponding to the angle between the microrobot direction and the horizontal plane.

Local Flow Measurement

Principle

Figure 4 shows how we can deduce the speed of the flow in the horizontal plane from the RTS theoretical speed. For this, we use a measurement of the absolute speed obtained with the image tracking from the camera. Then we subtract from this speed the theoretical speed to obtain the drift of the RTS. This corresponds to the flow speed. The theoretical speed is calculated using the parameter p which linearly links the rotation frequency to the helical microrobot theoretical speed (see equation 4). As explained in the previous section, this parameter is obtained by measuring the horizontal speed of the RTS when there is no flow.

Figure 4 also explains how this calculation is made in our control software. It is important to note that to get more precision, we directly use the value of the yaw angle from the tracking. Indeed as the amplification and electromagnetic coil stages do not have a perfectly linear behavior, small errors in the command direction can happen and induce an error in the theoretical RTS speed direction.

This flow measurement principle could be easily extended to measure vertical flow. The only limitation for this is to develop an altitude tracking. With such tracking, the theoretical vertical speed could be compared to the actual vertical speed. Note that the theoretical value should be compensated by the vertical speed due to gravity and buoyancy effects.

Experimental Demonstration

To demonstrate the flow sensing ability of the RTS, we propose to analyze two different flow profiles due to different channel geometries inside the microfluidic chip. Figure 5 Therefore, this generates a pressure difference between the microfluidic input and output which results in a flow inside the channel. This geometry allow a flow speed component only in the horizontal plane. In Figure 5 a) the path of the RTS is displayed in red. This path is in the middle of the channel and goes from the bottom to the top. The closed-loop control maintains the RTS centered on this path, the vertical component of the propulsion remains the same. Therefore, the vertical speed of the RTS can be considered constant. In a post-process step this speed is measured precisely by determining the time for the RTS to go from the bottom to the top of the channel. During the experiment the altitude of the microfluidic chip is moved by a motorized microstage commanded manually to keep the RTS in the optical focus plane.

Figure 5 b) shows the two different locations of the measurement inside the microfluidic chip to provide two square channels with different width. On this figure we also display the position of the isolated chamber used to make the experiment to determine the falling speed of the RTS (v 0 ). This speed is an essential parameter of the altitude openloop swimming control. We also mention the position of the area where the tests to adapt the motion depending on the flow condition are performed. Supplemental Video 2 shows a experimental view record of one flow profile measurement.

Figures 5 c) andd) show the flow profiles measurement for the two different channel widths e. As expected, a parabola shape is obtained for e = 3mm. Indeed in this case the lateral walls are too far to have any influence on the flow and the solution is close to the 2D one with infinite e value. For e = 0.3mm, the walls have a large influence and the solution is closer to a square shape flow. To provide a comparison, we simulate with a finite element software (Comsol multiphysics) the expected flow profile for the two channels. The Stokes equation was used to compute the flow by taking the IPA (Isopropyl alcohol) viscosity at 20 • C. The obtained profiles are scaled to match the maximum value of the experimentally measured profiles. Therefore, as the flow is linear, the flow profile is the same for any pressure shift between P 0 and P 1 . We take it equal to 1 bar. We see that the measured shapes fit well the simulated ones.

The position errors of the swimming closed-loop control was below 15 µm for x positions. This error is mainly caused by the difficulties of the PID controller to keep the RTS at the same position as the flow intensity changes with the altitude. This error does not impact the measurements as the flow profile is invariant with x. For y, the error is below 2 µm and we can therefore consider its impact to be negligible. The error evolution with time is displayed in Appendix B.. The difference between the experiments and the simulations as well as the relatively strong noise can be mainly attributed to the tracking algorithm. As presented in the previous section on the swimming closed-loop control, the change in RTS direction impacts the tracking position. Therefore, an error is made on the RTS speed when its direction changes. Moreover if the focal plane is not perfectly set in the middle of the RTS, either the top or the bottom will be more in focus thus resulting in the displacement of the tracking center. This error can also be important as we manually track the optical plane which creates another artificial speed of the RTS added directly to the measurement.

The error is also due to the drift approximation which consider the flow parallel to the microrobot direction. Indeed the microrobot have a small speed component due to the gravity. Therefore, the error on the measurement is below 10 %. The detail of the error development in this case is presented in the Appendix B.3. For now this error is less important than the instability of the tracking, but in case this method should be used for precise flow measurement a precise characterisation of the microrobot to determine its perpendicular and parallel drag coefficients should be realised in order to correct this error.

Finally some error could also arise when the RTS is near a wall as surfaces impact the flow distribution and thus the propelling force. However, this error decays quickly far from the surface and is generally already below 10 % after 2 body length µm (Spagnolie and Lauga 2012) which is sufficient to detect on overflow scenario. In the RTS case, we proved in a previous work that the impact of the wall of the speed along the wall direction is below 3 % for a 20 µm distance [START_REF] Barbot | Helical microrobot for force sensing inside microfluidic chip[END_REF]) (supplementary material G). So the error from the surface impact is for now negligible compared to the tracking noise, which should be the first step to improve for better measurement. Then more complex measurement paths could be developed to measure the flow at a specific location with the RTS going into different directions. By doing so the loss or gain of propulsion due to the surface could be extracted as well as a corrected flow value. The presented results of this paper seem satisfactory enough for a proof of concept. We believe they are enough to guarantee that we can use this method to measure the local flow at the RTS position in order to automatically detect dangerous overflow situation.

Local Flow Measurement for Semi-Automatic Motion Adaptation

Motion Choice Algorithm

To limit the risk of the RTS to be carried away by the flow and to improve its robustness, we want to develop a semiautomatic control. The user could benefit from their high level understanding of the physics and the situation in order to define the best path in the chip. Then the RTS control system could compute the safest motion along this path. Therefore, we propose here to develop such algorithm to ensure the safest RTS displacement along a predefined path.

To design this algorithm, we first need to see how dangerous situations presented in Figure 1 can be avoided. Unfortunately the surface stiction is for now difficult to detect in advance. We hope that further research on the friction between the RTS and the surface based on the Appendix B.1 results will propose a solution to anticipate the probability of the RTS to be stuck to the surface. However, an excessive flow can easily be detected. Therefore, the best motion decision algorithm can be summarized for now in the following sentence: "If swimming is possible then use swimming."

Figure 6 explains how this algorithm is implemented in our current system. In swimming motion the RTS follows the path by closed-loop control. If the flow measurement exceeds a given threshold the state changes and the RTS switches to "swimming to spintop motion" state. In this state the RTS aims toward the ground at the fastest speed. This flow threshold depends only on the microrobot speed and can be chosen by the maximal acceptable drift angle between the propulsion and the direction. Therefore, the threshold is given by the following formula : It is safer to pick an angle below 45 degrees (i.e a threshold speed below the microrobot speed) as some destination becomes unreachable when the maximal drift exceed 45 degrees. Then the collision with the ground is detected by evaluating the difference between the axis of the RTS and its direction projected on the horizontal plane. In swimming this difference is close to zero whereas it is close to π/2 in spintop motion and correspond to the offset presented in Figure 3 a). The last measured flow speed is used to determine a direction that will partially compensate the flow drift.

Threshold = speed tan (drift) (9)
When spintop motion is detected the closed-loop control on the path is restored and the RTS continues to follow the path in spintop motion. The decision of trying to return to swimming is for now made by the user. Indeed they are in the best position to determine if the flow speed has reduced. However, to reduce even more the time spent in spintop motion and therefore the probability of getting stuck to the surface, triggering periodically the command to try to return in swimming is probably the optimal strategy. In general cases, the frequency of the take off attempt should be at least the double of the maximum flow change frequency in order to not miss any swimming opportunity.

In ideal cases, the swimming altitude should be the lowest as possible to be less impacted by the flow as well as reducing the necessary time to switch to a friction based motion. A minimum safe distance to avoid surface contact is one body length of the microrobot. However, there is a need to add to this distance a margin corresponding to the maximum error of the altitude control to take into account of the altitude control error as well as the maximum roughness of the surface. As we do not have yet an altitude tracking, we evaluate this altitude with the time assuming a constant vertical speed. Therefore a minimum value for the take off time can be given by the following formula:

take off time > L sin θ + m + v , (10)
with L the body length of the microrobot, θ the maximum pitch angle in the trajectory as defined in Figure 4, m the maximum roughness of the surface, the tracking error and v the set vertical speed.

In our experiment the consecutive swimming time of the RTS was restrained to 40 seconds and the maximum error of the gravity compensation was 1 µm • s -1 This leads to = 40µm. Moreover L = 50µm and m = 0, to be sure to cover every configuration of the microrobot we also consider θ = π/2. Therefore according to equation 10, a minimum take off time of 4.5 seconds was required by setting a vertical speed of 20 µm • s -1 .

In practice we choose to wait for 5 seconds for the RTS to reach a 100 µm altitude for swimming. At the end of this time the RTS comes back to the swimming state and continues to follow the path by closed-loop control with the swimming motion.

A constant horizontal position during the "spintop motion to swimming" state is maintained by the swimming closed-loop algorithm. The RTS compensates the flow and maintains the same x and y position during the "spintop motion to swimming" state. As the RTS is in swimming mode during the ascension, the flow can be measured. If it becomes larger than the flow threshold, the RTS returns to the ground by switching to the "swimming to spintop motion" state. Therefore, trying to switch to swimming is guaranteed without risk of losing the RTS on the flow and can be tried as often as one needs.

The flow measurement is perturbed by the surface proximity when the RTS takes off. Therefore, there is some delay in the algorithm before starting the flow measurement after take off. To improve robustness, this delay needs to be as small as possible, especially if strong flow is present. Assuming that we have a spheroidal microrobot with the propulsion distributed on most of the surface, the perturbation become lower than 25 % at half a body length of the surface [START_REF] Spagnolie | Hydrodynamics of selfpropulsion near a boundary: predictions and accuracy of farfield approximations[END_REF]. Therefore a good indication for starting the sensing time before the measurement can be given by the following expression :

sensing time ≈ L 2v (11)
In our case this give a waiting time of 1.25 seconds, that we decide to round to 1 second. Indeed, even if some error can still be made on this measurement, it is better to have a rough flow estimation quickly in case the flow value is clearly larger than the threshold.

Experimental Demonstration

To test this algorithm we propose to command the RTS on a repeated rectangular path. This repetition of path is required as we have not implemented a moving platform to follow the RTS on the chip on a long closed-loop control path. This gives us time to tune the flow in order to test the motion adaptation algorithm. To precisely tune this flow, the input reservoir is put on a platform where the altitude can be set manually with millimeter precision to vary the pressure. As both input and output reservoir surfaces are connected to the ambient pressure, the elevation of the input reservoir creates a pressure difference that generates the flow. To reduce the noise of the flow measurement by the RTS, we perform an averaging of its value on one second.

Figure 7 shows the results for this path. In this figure, experimental views are presented at different times. As we do not have an altitude tracking system, the focus is set manually. At first the RTS follows the path thanks to the swimming closed-loop control as it can be seen in Figure 7 picture a). The altitude is around 100 µm. At the end of the first rectangular path we start to increase the flow gradually. At the beginning the RTS can continue to perform the swimming motion and the PID controller automatically compensates the drift due to the flow. The picture b) is taken at this time. We see that the RTS is not parallel to the path to compensate the flow drift. Then the flow value reaches the threshold which is set to 15 µm • s -1 . This triggers the transition to spintop motion and the RTS control switches to the state: "swimming to spintop motion". The closed-loop control is stopped and the RTS aims for the ground. Even if the direction of the RTS is set to compensate the flow, some errors in the position appear in the y position.

When the RTS touches the ground, the closed-loop control is restored in spintop motion. As the propulsion is controlled by the pitch angle in this motion, the frequency is set to 100Hz. The flow measurement becomes impossible and is therefore stopped. The picture c) is taken during this spintop motion closed-loop control. To test the ability of the RTS to return to swimming, we reduce the flow. Then we manually trigger the transition to the "spintop motion to swimming" state which restores the swimming closedloop control. The picture d) is taken during this state. The horizontal position is kept constant as the vertical speed is set to 20 µm • s -1 to reach an altitude around 100 µm. After one second in this state, the flow threshold is reactivated to detect potential overflow. As it can be seen on the flow measurement curve, the take-off from spintop motion to swimming creates perturbations that increase the flow estimation artificially. This justifies waiting one second before restoring the flow threshold. After five seconds the desired altitude of approximately 100 µm is reached, because we set the ascending speed to 20 µm • s -1 in the altitude open-loop. Then the closed-loop controller on x and y is set to follow the path and the altitude open-loop on a 0 µm • s -1 speed. As it can be seen in Figure 7 e) the swimming state is restored. Supplementary Video 3 shows the record corresponding to these results.

In some cases where we would like to restore the swimming motion, the flow speed is still over the threshold and too dangerous for swimming motion. Therefore, it is important to prevent the RTS from reaching the swimming state and to be carried away by the flow. Figure 8 a) shows a record corresponding to this situation. Here the RTS first follows the rectangular path with the closed-loop control of the spintop motion. Then the user triggers the transition to swimming and the RTS control switches to the "spintop motion to swimming" state. After one second the flow threshold is set and then the measurement flow reaches this threshold. This triggers the transition to the "swimming to spintop motion" state until the RTS returns in contact with the ground and continues to follow the path by closed-loop controlled spintop motion . The video of this experiment is available in Supplementary Video 4.

Finally, Figure 8 b) shows what happens if the RTS control does not perform motion adaptation. In this experiment the RTS follows the path by swimming motion and the flow is continuously increased. At first, the RTS control can compensate the flow drift but as the necessary frequency gets higher than 200Hz, the RTS reaches its step out rotation frequency and the speed no longer increases with the frequency. This happen for a flow speed around 30µm • s -1 . The RTS speed decreases brutally after the step-out. This results in an almost immediate loss of the microrobot from the frame and a definite potential loss. The rapidity of this phenomenon demonstrates the advantage of automatic motion control as a human user cannot monitor at all times the motion of the RTS to conclude in a too large flow situation. The video of this experiment is available in Supplementary Video 5.

It can be seen on all the experimental records that the flow measurement presents strong fluctuations and noise. These errors are due to perturbations in tracking during the brutal change of direction of the RTS while following the rectangular path. Therefore, to reduce it, the solution would be to improve the tracking precision.

In laminar regime, the flow intensity reduced near the surface, therefore one good strategy to avoid strong flow is to move close to the surface. This can be especially interesting for helical microrobots with an almost neutral buoyancy and therefore a small maximum pitch to overcome gravity. For such case, surface impact on the microrobot drag and therefore the flow measurement could be impacted. So in this case an experimental tuning of the microrobot would be necessary to compensate this measurement error.

Conclusion

In this paper we first proposed automatic control of the swimming motion improving flow robustness. This automatic control relies on two closed-loop controls for the x and y coordinates and one open-loop control based on the RTS characterization for the z coordinates. This open-loop control was necessary as we currently have no way to track the RTS altitude online. Then this automatic closed-loop control was used to measure the flow profile in rectangular channels with different widths. These flow profiles obtained by the RTS were shown to match the shapes obtained by numerical simulations. Finally, we showed that a semiautomatic algorithm with minimum user intervention could switch from swimming to spintop motion (which is a surface motion less impacted by the flow in order to keep a path control in various flow conditions). As no information on the flow can be obtained in spintop motion, the signal to trigger the return to swimming motion remains, for now, a manual input from the user.

Indeed the ability to detect unsafe surfaces where the microrobot could easily become stuck to the surface would also improve the general robustness in a full automatic control. Improvement on the image analysis could provide obstacle detection in order to swim over them. For high obstacles, changing the focus of the lens could be used to determine their altitude whereas obstacles smaller than the depth of focus of the field could be detected by contrast change in the image.

Finally, to continue improving the robustness of the RTS, the motion algorithm could also include the rolling motion. In this motion the RTS is in contact with the surface along all of its length and therefore it is even less dependent on the flow. However, this motion is more likely to result in the microrobot becoming permanently stuck on the surface of the microfluidic channel.

We think this work is the first step to allow a simple user control interface for helical microrobots where no prior knowledge of the microrobot control and particularities is required. The user input should be limited only by the desired position of the RTS. Ultimately, the system should choose the best motion to safely go to this location. If this location is too dangerous to be reached safely by any means, the motion should be stopped and a warning displayed to the user.

and the surface is providing all the propelling force that counterbalances the fluid drag exerted by the fluid. This fluid drag is considered as proportional to the speed thanks to the low Reynolds number approximation.

From the curves in Figure 9, we see that two different linear tendencies seem to emerge. They are referred as (1) and (2) and their linear approximations are drawn. For the gold surface, two similar experiments were performed but different results were obtained. One follows the tendency (2) and the other follows the tendency (1) until 70 Hz where it brutally changes for the tendency (2). The reasons of the RTS to be in one of the two regimes is still an open question.

Even if it is not possible for now to understand the reasons of the RTS to switch between these two tendencies and how much this depends on the surface composition, we can still make some interesting assumptions on the underlying physics. The main difference between these two linear tendencies is that when the frequency is null, (1) seems to extrapolate to a null value and (2) to a value around 5 µm • s -1 . This difference can be explained by different friction models. The tendency (1) seems to be well explained by a lubricated model where liquid remains between the RTS and the surface. In this case, the friction on one object is obtained only by the fluid action on this object. The friction force is then given for a low Reynolds flow by the following equation:

dF = ν • ∂u ∂n , ( 12
)
where dF is the force element at a given point, ν the viscosity, u the speed of the fluid and n the normal axis to the surface on the point. As we are in a low Reynolds flow, the flow speed at the boundary must match the speed of the objects. In this case, if the rotating speed of the RTS is null, the flow speed will also be null at every point and so will be any of its space derivatives. The low Reynolds number approximation also guarantees linearity which means that the rotation speed of the RTS is linearly linked to the flow speed and thus to the RTS speed. Hence the linearity and the null value at 0 Hz seem well explained by this lubricated model. The presence of a liquid layer between the RTS and the surface seems therefore a convincing hypothesis for the tendency (1).

The tendency (2) seems to fit a Coulomb kinematic friction scheme. This well-established empirical model proposes an interpretation for the increase in surface contact due to the normal force between two objects. It is given by the following equation:

F = K • F N , ( 13
)
where F is the friction force, K the kinematic friction coefficient between the two materials and F N the force normal to the contact between the two objects. This model explains also the affine trend of the curves (2). Indeed the RTS rotation frequency is linearly linked to the normal force thanks to the helical shape that maintains a pushing force on the substrate. However, the main justification of this model is that it explains the positive value of the affine approximation of the curve at zero frequency. Indeed the force between the RTS and the surface is the sum of the corkscrew force and the weight of the RTS. Therefore, by extrapolating this model to 0 Hz, some speed should remain, corresponding to the friction force only due to the weight of the RTS. Thus, we can conclude that the friction is in part governed by Coulomb model. In fact in the case (2) we cannot suppress the influence of the propulsion coming from the viscosity which explains the tendency (1). Indeed there is no reason for this viscous force to completely disappear. Therefore, the lateral force resulting in the speed of the tendency (2) is probably a mix of the viscous drag and solid friction modeled by the Coulomb law. The switch between these two regimes during one of the gold surface experiments as well as the gradual change from one regime to another in the gold experiment in Figure 9 gives credit to these contact model hypotheses. Indeed in both cases the transition happens from the lubricated model to the physical contact model with the increase of frequency, i.e. with the increase in the force that pushes the RTS on the substrate. It seems logical that increasing this force tends to suppress the liquid layer between the two objects. We see that the RTS converges to the command position when it stops moving. However a relatively large error and delay are present for a moving command. Unfortunately we saw experimentally that a more reactive system presents a risk of being unstable and therefore the PID controller could not be tuned to reduce more this error.

B.2 Swimming Closed-loop Control Time Response

B.3 Flow Channel Measurement: Position Errors

The Figure 11 displays the distance between the RTS and the control point during the flow measurement experiment.

Figure 1

 1 Figure 1. a) SEM picture of the RTS. b) illustration on how the right combination of spintop motion and swim motion can guide the RTS through a complex environment.

Figure 2 .

 2 Figure 2. Fabrication steps to obtain a helical microrobot embedded in a microfluidic chip. I) presents the steps link to the helical microrobot fabrication, II) presents the microfluidic chip fabrication steps, III) present the integration of the microrobot inside the chip.

Figure 3 .

 3 Figure 3. Closed-loop control diagram and experimental top view photomontage for spintop motion and swimming motion

  Figure 3 b) also presents a step-by-step photomontage of the experimental microscope view during a swimming closed-loop on a rectangular path. During the experiment a flow around 5 µm • s -1 was pushing the RTS along the y direction. This explains the difference between the real path and the command path on the y axis. More details on the command and real position evolution with time are available in Appendix B.2.

Figure 4 .Figure 5 .

 45 Figure 4. Online flow measurement principle and implementation. Note that the yaw angle is directly measured on the microrobot instead of using the input value. This allows us not to suffer from the error introduced by the electromagnetic setup.

Figure 6 .

 6 Figure 6. Algorithm to guarantee the safest motion along a path.

Figure 7 .

 7 Figure 7. Semi-automatic motion along a cyclic rectangular path. A record of this experiment is presented in the trial 2 of Supplemental video 3. The flow speed increases until the flow threshold is reached and the RTS switches to spintop motion motion. Then the flow is reduced and the RTS is manually triggered to change to swimming motion. For several time markers, an experimental view is displayed: a) swimming with no flow, b) swimming by compensating the flow, c) spintop motion, d) transition from swimming to spintop motion, e) swimming by compensating the flow.

Figure 8 .

 8 Figure8. a) shows the RTS in spintop motion with a flow superior to the threshold. When the transition to swimming is triggered the algorithm quickly detects the overflow and comes back to spintop motion. The video of this experiment is available in supplemental video 4. b) shows the RTS swimming without motion adaptation. As the flow increases the RTS control saturates and the RTS is carried away by the flow. The video of this experiment is available in Supplementary Video 5.

Figure 10

 10 Figure10displays the evolution of the command and real position with time during swimming closed-loop control . We see that the RTS converges to the command position when it stops moving. However a relatively large error and delay are present for a moving command. Unfortunately we saw experimentally that a more reactive system presents a risk of being unstable and therefore the PID controller could not be tuned to reduce more this error.

Figure 10 .Figure 11 .

 1011 Figure 10. Command and real position of the RTS control in closed-loop while swimming.

  al.

Sunrise Setting Ltd, UK

SAGE Publications Ltd, UK

Prepared using sagej.cls [Version: 2016/06/24 v1.10]

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Journal Title XX(X)

Appendix A: Index to multimedia extension

Figure 9 shows the evolution of the RTS speed with the frequency where the force provided by the helical part is pushing the RTS into the substrate. To get these results, we integrate several substrates coated with different materials into the main chamber of the microfluidic chip. The metallic substrates are in glass with a 100 µm thickness and the coating is made with a PVD process with a thickness of 10nm. To integrate these substrates into the chip we first bond on the glass cover, a thin sheet of PDMS (≈ 100µm) with a total surface around 25 mm 2 . Then we cut the different substrates in 2 mm 2 square parts and bond them to the PDMS sheet. Finally, the microfluidic chip is bonded to the glass and the main chamber is placed to contain the PDMS sheet and the different substrates. As this main chamber thickness is 1mm different substrates are still accessible to the RTS by swimming.

Then the inlet and outlet of the chip are closed to limit parasite flows and the RTS uses swimming motion to reach the different substrates and perform spintop motion. Unfortunately during the experiment a small parasite flow around 5 µm was still present. To analyze these data we consider that the friction point between the RTS Prepared using sagej.cls


Hamza Raki
email: hamza.raki@creatis.insa-lyon.fr

Kevin Tse

Ve Koon

Isabelle Saniour
email: isabellesaniour@live.com

Simon A Lambert
email: simon.lambert@creatis.insa-lyon.fr

Olivier Beuf
email: olivier.beuf@creatis.insa-lyon.fr

Henri Souchay
email: henri.souchay@med.ge.com

Fraser Robb
email: fraser.robb@ge.com

Keywords: Active decoupling, endoluminal coils, MEMS, MR switch. I

MEMS (Micro Electro Mechanical System) switches were assessed and compared to PIN diode in fulfilling the task of active decoupling of Receiver Endoluminal Coils (RECs). Three prototype RECs with the PIN diode in parallel (pPIN), MEMS in parallel (pMEMS) and MEMS in series (sMEMS) with the REC loop were built. Quality factors (Q-values), decoupling efficiency and switching delays were characterized on bench and Signal-to-Noise Ratios (SNRs) established on images at 1.5 T. Q-values were equal to 62.5, 41.2 and 65.1 for pPIN, sMEMS and pMEMS, respectively. In the decoupled state, reflection coefficients S11 and S21 at resonance frequency both indicated proper decoupling. Switching delays were less than 0.7 µs and 10 µs for pPIN and MEMS RECs, respectively. Decoupling/coupling delays of MEMS remained compatible with most Magnetic Resonance (MR) clinical applications. For all prototypes, MR images displayed no signal saturation and similar elliptical image sensitivity patterns. No artifacts due to active decoupling failure were observed. Mean SNR values obtained with pMEMS REC were higher than those obtained with sMEMS REC but lower than with pPIN REC because of the use of additional instrumentation to render the scanner compatible with the MEMS utilization. MEMS in parallel are an interesting alternative to PIN diode for decoupling and could lead to better SNR with a compatible MR system (dedicated control signal). The MEMS in series can be used for both decoupling and reconfiguration of the REC loop geometry for colon wall examination.

penetration depth, which can result in tumor growing within the bowel wall being detected only at a late stage. This limitation could be overcome by a high spatial resolution (inplane pixel size <100 µm and 2-mm slice thickness) MRI examination.

MR arrays of external receiver radiofrequency (RF) coils are commonly used for abdominal imaging with improved Signal-to-Noise Ratio (SNR) owing to the combination of small surface coils that provide higher sensitivity [START_REF] Roemer | The NMR phased array[END_REF]- [START_REF] Perrier | Design of a Two-Channel NMR Coil Using an Impedance Transformation Approach[END_REF]. Unfortunately, the gain in SNR is not sufficient to depict the thin bowel and colon wall in deep regions located relatively far from the body surface, thus making analysis of the colon wall and subsequent colorectal cancer staging difficult.

For this purpose, early works [START_REF] Armenean | Optimization of endoluminal loop radiofrequency coils for gastrointestinal wall MR imaging[END_REF]- [START_REF] Hurst | Intravascular (catheter) NMR receiver probe: Preliminary design analysis and application to canine iliofemoral imaging[END_REF] have demonstrated the feasibility and value of Receiver Endoluminal Coils (RECs) close to the region of interest (ROI). A simple REC design consists of a very small surface coil with an adapted size and geometry so as to fit the lumen being investigated. In the case of colon examinations, the favored geometry is a rectangular loop with a width somewhat smaller than the diameter of the colon. In this configuration, the REC displays a cylindrical sensitivity pattern (with respect to its long axis) (b) Photograph of the 1 cm × 1 cm chip package of only the MEMS switch with 1 Algerian dinar coin. Second line shows two small diagrams of the MEMS and its driver circuit (MDC). The MDC consists of an electronic Decision Unit (DU) biased with 10 V and 0 V input voltages and allowed to compare a predetermined threshold voltage to the control signal. (c) If the DU receives a control signal of -5 V, an external 82 V (typical value) is delivered between the gate (G) and beam (B) electrodes of the MEMS thereby closing the switch. (d) If the DU receives a control signal of 7 V (or 100 mA delivered by the MRI system), the external 82 V is blocked and cannot be delivered to the MEMS gate. In this case, 0 V is delivered between G and B thereby opening the switch.

with a very high SNR close to the loop, enabling a decreased voxel size. The sensitivity decreases rapidly with the distance to the long axis [START_REF] Hayes | Noise performance of surface coils for magnetic resonance imaging at 1.5 T[END_REF] which is, however, sufficient to enable high-resolution imaging of the first centimeters of the colon wall and thus visualization of its different layers. RECs have been used in vivo on rabbit [START_REF] Beuf | In vivo colon wall imaging using endoluminal coils: Feasibility study on rabbits[END_REF] and mouse [START_REF] Dorez | Endoluminal high-resolution MR imaging protocol for colon walls analysis in a mouse model of colitis[END_REF], [START_REF] Dorez | In vivo MRS for the assessment of mouse colon using a dedicated endorectal coil: initial findings[END_REF] colon walls. The REC is used in combination with the whole-body RF transmitter coil in order to provide a well-transmitted RF pulse uniformity over the imaging volume [START_REF] Giovannetti | Low-Field MR Coils: Comparison between Strip and Wire Conductors[END_REF]. Both coils must hence be decoupled during the transmission phase in order to avoid any mutual induction and the subsequent nonuniform B1 magnetic field in the vicinity of the receiver loop coil. Therefore, a decoupling circuit is mandatory to avoid uncontrolled and spatially dependent image contrast. Another negative consequence is the local increase in the Specific Absorption Rate (SAR) that could lead to patient safety problems [START_REF] Edelstein | Electronic decoupling of surface-coil receivers for NMR imaging and spectroscopy[END_REF].

To this end, several electronic switching components enable this active decoupling. PIN diodes are the most popular components used as RF switches in the majority of clinical systems. The main advantages are their compatibility with a magnetic environment (non-magnetic packaging), the small size (a few millimeters) and a switching delay below 1 µs.

Other components such as MR-compatible memory resistive elements (Memristors) have been used [START_REF] Reykowski | Using memristor devices as switches for MRI RF coils[END_REF]. The principle resides in applying different DC control voltages to switch between two resistive states during transmit and receive modes. The particularity of this technology is that the Memristor, unlike the PIN diode, can memorize and retain the resistive state information after the control signal is removed. It is particularly relevant in multinuclear MR coils. Other methods based on optical components have been proposed in the literature mainly founded on photoelectronic devices such as the photoresistor [START_REF] Wong | An optical system for wireless detuning of parallel resonant circuits[END_REF], photodiode [START_REF] Weiss | In vivo safe catheter visualization and slice tracking using an optically detunable resonant marker[END_REF], [START_REF] Eggers | Image-based tracking of optically detunable parallel resonant circuits[END_REF], combined photodiodes and a PIN diode [START_REF] Saniour | Active optical-based detuning circuit for receiver endoluminal coil[END_REF], PhotoMOS [START_REF] Rescia | RECEIVE-ONLY SURFACE COIL 'WITH IMPROVED DETUNTNG FOR PRE-CLINICAL MR1 STUDIES[END_REF], or MOSFET with optically isolated [START_REF] Nacher | A fast MOSFET rf switch for low-field NMR and MRI[END_REF] devices. The optical decoupling solutions have the advantage of using nongalvanic transmission means thereby avoiding induced current in the shield and increasing patient safety [START_REF] Ayde | Potentialities of an Electro-Optic Crystal Fed by Nuclear Magnetic Resonant Coil for Remote and Low-Invasive Magnetic Field Characterization[END_REF]. Finally, low noise preamplifiers have been used for the decoupling between loops of the coil in the case of multiple-element array coils [START_REF] Fujita | RF Surface Receive Array Coils: The Art of an LC Circuit[END_REF]- [START_REF] Guclu | A method for preamplifier-decoupling improvement in quadrature phased-array coils[END_REF].

A few years ago, Micro ElectroMechanical System (MEMS) switches were introduced by GE Healthcare, Inc. [START_REF] Spence | Custom MEMS switch for MR surface coil decoupling[END_REF] to act as MR-compatible switches (Fig. 1-a andb). As can be seen in Figure 1 (c andd), the MEMS as a switch has two working states: open and closed. The open state electrically corresponds to a small capacitance (1.5-2 pF). The closed state is electrically equivalent to a very small parasitic resistance (0.5-1 Ω). Owing to their high electrical isolation (approximately 10 MΩ) and their MR compatibility, MEMS ("MEMS" will be used to denote "MEMS switch" hereafter) were successfully used as switches. MEMSs were used to open or close portions of metallic conductors for rapid reconfiguration of external two-channel array coil geometry as was done for spine and torso MRI [START_REF] Bulumulla | MEMS switch integrated radio frequency coils and arrays for magnetic resonance imaging[END_REF]. MEMSs have also been used as an alternative solution to the traditional passive decoupling strategy in the case of double-tuned RF coil designs used, for example, for sodium imaging. A proton coil is still required for co-registration purposes and the decoupling of the coils is performed with MEMSs by shifting the resonance frequencies.

The MEMS-based solution demonstrated comparable results to the PIN diode and better performance than the trap circuit [START_REF] Maggiorelli | Double-Tuned Surface 1H-23Na Radio Frequency Coils at 7 T: Comparison of Three Decoupling Methods[END_REF]. In the same context, a study based on four-element fixed phased transmit-receive coils used MEMS to switch between 1H (64 MHz) and 19F (60 MHz) resonance frequencies for lung imaging [START_REF] Maunder | Comparison of MEMS switches and PIN diodes for switched dual tuned RF coils[END_REF]. In a design of integrated parallel reception, excitation and shimming coil arrays, MEMSs were used to reduce the cost and complexity of the design by employing a single DC supply with an adaptive distribution of the DC current (instead of using "N" DC supplies for "N" shim loops) and thus achieving high RF shimming performances (high localized B0 homogeneity) with no SNR loss [START_REF] Darnell | Adaptive integrated parallel reception, excitation, and shimming (iPRES-A) with microelectromechanical systems switches[END_REF]. Finally, MEMSs were also used for both the control of an array of primary coils and the automatization of the impedance matching network of an MRI-compatible wireless power transfer system used on a wearable wireless receive coil array [START_REF] Byron | An MRI Compatible RF MEMs Controlled Wireless Power Transfer System[END_REF].

Endoluminal MRI using RECs could also benefit from the use of MEMSs for the active decoupling while additionally enabling reconfigurable REC geometries [START_REF] Raki | Design of a reconfigurable endoluminal coil using MEMS switches[END_REF]. In this work, we focus on studying the feasibility of using a MEMS as an active decoupling element. This is an interesting question considering the very small size of the REC loop and therefore its resulting small electrical resistance with respect to that of the MEMS. Since the impact of MEMSs used in parallel to the loop (the MEMS is similar to either an additional capacitor or a short circuit shifting the REC resonance frequency) is not the same as the impact of the MEMS used in series with the loop (the MEMS is similar to either an additional parasitic resistance or an open circuit in the REC loop), we first tested the two configurations not only in terms of active decoupling performance but also in terms of an active image quality to detect the performance advantages and drawbacks of each configuration. This manuscript presents the first step of our project. For this purpose, two RECs with active decoupling circuits using MEMS placed in series or parallel to the loop were assessed and compared with a reference REC using a conventional PIN diode. All RECs were characterized on experimental benches, in both coupled and decoupled states. Tuning, matching, quality factor and isolation (decoupling efficacy) were measured. Switching delays to couple or decouple the REC were also estimated. Finally, the three RECs were tested in imaging conditions on a 1.5-T MR system. SNR mean and distribution were measured to assess differences on the acquired image.

II. MATERIALS AND METHOD

A. REC prototypes

In this study, three copper RECs with identical rectangular loop geometries were mechanically etched on an FR4 epoxy substrate of 1.6-mm thickness (S63, LPKF Laser and Electronics, Germany). Each REC consisted of a single rectangular loop (5.1-mm width, 47-mm length and 35-µm copper track thickness). In order to operate with a 1.5-T MRI system, fixed capacitors (ATC, New York, USA and Temex Ceramics Exxelia, Pessac, France) were soldered in the proximal side to match 50 Ω and in the distal position to tune the loop to the working proton resonance frequency (63.87 MHz).

MEMS switches (MM7100, Menlo Microsystems, Inc., Irvine, CA, USA) were used in this study to couple or decouple the REC during endoluminal MRI. The MEMSs used are fast mechanical switches based on a mobile micro metallic actuator (Fig. 1-a) inside a chip of 1 cm × 1 cm size (Fig. 1-b). The MEMS is open by default and to close it, sufficient voltage (in our case, the typical value was 82 V) has to be applied between the gate (G) and the beam (B) of the MEMS. For our MRI application, since the MR system was not able to deliver these voltages, a driver circuit supplied the required voltage to open or close the MEMS so as to achieve the coupling/decoupling of the REC. As can be seen in the two schematics of the MEMS Driver Circuit (MDC) in Fig. 1-c,d, the MDC consists of an electronic decision unit (DU) and is equipped with four inputs. Three of them are used to receive continuously the required bias voltages: 0 V as ground, 10 V as bias voltage of the DU and 82 V to close the MEMS switch. The last input receives the control signal and directs it to the DU, which compares it to a predetermined threshold voltage leading to the delivery or blocking of the 82 V to the gate of the MEMS. Applying a -5 V control signal to the DU delivers the 82 V to the MEMS gate (G) and thus closes the MEMS switch (Fig. 1-c), while a 7-V voltage (which is equivalent to a current of 100 mA provided by the MRI system) leads to 0 V being applied between G and B and thus opens the switch (Fig. 1-d). More details about the MEMS and MDC information were provided in previous work [START_REF] Keimel | Microelectromechanical-Systems-Based Switches for Power Applications[END_REF]. To evaluate the MEMS performances for active decoupling of RECs, two different active decoupling REC configurations with MEMS in series (sMEMS REC) and MEMS in parallel (pMEMS REC) to the loop were built and assessed. In the sMEMS REC configuration (Fig. 2a-d), the MEMS switch was integrated into the loop (in series with the tuning capacitor Ct). During transmission, the MEMS must be open capacitor Ct). During transmission, the MEMS must be open (REC is not resonating). In the pMEMS REC (Fig. 2b-e), the switch was integrated in parallel to the tuning capacitor Ct. During transmission, the MEMS must be closed to shift the resonance frequency and thus detune the REC. In addition, a third REC was built with a conventional decoupling circuit using a PIN diode component (DH 80055, Temex Ceramics, Pessac, France) placed at a distal position in parallel (pPIN REC) to the tuning capacitor (Fig. 2c-f). The PIN diode was controlled using a forward biased voltage of 3.8 V (which is equivalent to a current of approximately 100 mA provided by the MRI system). A summary of tuning and matching capacitor values used to build the different RECs is given in Table I.

REC prototypes were characterized on two experimental measurement benches as well as by imaging using a phantom made of a cylindrical vessel (90 mm outer diameter, 100 mm long) filled with a solution of 1.25 g NiSO4 × 6H2O + 5 g NaCl per liter of distilled water mimicking tissue losses and with a through-hole (11 mm inner diameter) allowing for the introduction of the REC loops (Fig. 3). For ease of use, three identical phantoms were built, one for each REC prototype.

B. Experimental bench characterization set-up

Two complementary experimental electronic benches were used to assess the frequency and temporal responses of the different REC prototypes. Each REC was first connected to a Vector Network Analyzer (VNA) (Agilent Technologies Inc., E5071C, Santa Clara, CA, USA) to measure the reflection coefficient S11 in both coupled and decoupled states (Fig. 4ab). In the coupled state, the reflection coefficient response (S11) enabled tuning and matching of each REC at the Larmor resonance frequency (F0) by choosing the adequate tuning and matching fixed capacitors. Thus, the quality factor (Q-value) of each REC was derived from S11 response at -3 dB bandwidth [START_REF] Kajfez | Q factor measurements, analog and digital[END_REF]- [START_REF] Kajfez | Q-Factor Measurement with Network Analyzer[END_REF].

To assess the decoupling efficacy with a capacitive method, the S11 response in the decoupled state was first used. In parallel decoupling strategies (pMEMS and pPIN RECs), decoupling is achieved by detuning the RECs (shifting of the resonance frequency) resulting in no resonance at F0, which is characterized by the associated S11 value at F0 close to 0 dB. In the case of sMEMS REC, the switch is opened for a completely open REC loop thereby achieving active decoupling with a similar result in S11 at F0.

To confirm the obtained results, RECs were tested again with a mutual-induction method [START_REF] Griswold | Characterization of Multichannel Coil Arrays on the Benchtop[END_REF] based on the S21 measurements. Two homemade flux probes (Fig. 4-c) were connected to the two ports of a VNA and overlapped to be mutually decoupled in free space (parallel, adjacent and overlapping loops). The REC was then placed close to the two probes such that if it resonated at F0, a mutual coupling between the previously decoupled probes was created via the REC and specifically at its resonance frequency F0. On the contrary, if the REC was decoupled then at F0 there was no longer a coupling between the probes and the S21 measured would drop significantly (by at least 30 dB). This is illustrated in Fig. 4-d. For both S11 and S21 measurements, coupled/decoupled states were achieved by driving the opening or closing of the MEMS switch as explained previously.

To assess the decoupling speed, switching delays from the coupled state to the decoupled state and vice versa were measured. A circular broadband copper loop (41 mm inner diameters) connected to a frequency generator (HAMEG Instruments HM 8134-3, Mainhausen, Germany) was used to generate an RF signal at F0. By inductive coupling with the REC, the induced RF signal was measured using a digital oscilloscope (LeCroy waveJet 314, Lake Mary, FL, USA). To estimate the switching delays, a periodic square electric signal generated by a Waveform Generator (WG) (Agilent Technologies Inc. 33220A, Santa Clara, CA, USA) was used as coupling/decoupling control signal (-5 V / 7 V for MEMS on the MDC control input and 3.8 V / -10 V for the PIN diode) (Fig. 5-a). The coupling and decoupling delays were measured as the time between the DC square control signal and the RF response signal (Fig. 5-b).

C. MRI set-up

MRI experiments were performed on a clinical Optima MR450w 1.5-T MR scanner (General Electric, Inc. Trademark) (Fig. 6). The body coil was used as RF transmitter. Both the REC and phantom were placed within the scanner with their axes aligned with the B0 field in order to acquire 2D axial images. To interface the homemade REC to the MR system, a single-channel connector (A-plug) including a preamplifier (managed directly by the MRI system) with the appropriate configuration file was used. An RF circuit (RF/DC splitter) was placed between the RF channel of the A-plug connector and the REC in order to divert the DC control logic signal provided by the MR scanner. This was necessary because before starting a sequence, the MR system checks whether a coil is correctly plugged by verifying that a current can flow in the decoupling circuit. A decoy circuit was used in the case of MEMS RECs.

It should be noted that the DC control logic signal supplied by the MR scanner during the sequence does not always match the input control voltage required by the MEMS control circuit. Indeed, in the case of the pMEMS REC design, the MR control logic (out of the MRI system) has to be inverted. Since the MR scanner we used was not configurable to perform this task, our solution was to use a waveform generator (WG). Thus, pMEMS REC was controlled in this work only by using the WG (pMEMSWG REC), while the sMEMS REC was controlled either directly by the MR system (sMEMS REC), as in the case of the pPIN REC and also with WG (sMEMSWG REC) so as to be able to characterize the effect of using the WG. For both MEMS configurations using a WG, a control signal of -5 V/7 V was generated with the WG while a control signal of -5 V/100 mA was used with the A-plug of the MRI system. An external power supply (Electro-Automatik EA-PS 2384-03B, Viersen, Germany) was used to supply 10 V and 82 V (bias signals) to the MDC. Both the power supply and the WG were placed outside the Faraday cage, in the technical room. Thus, three DC lines using coaxial cables (4.7 m for each line) were used to transport the three DC signals (10 V, 82 V and control signals). Traps were added every eighth of the wavelength (λ/8) of each coaxial cable (11 traps for each DC line) to avoid RF-induced signal [START_REF] Schaller | Common modes and cable traps[END_REF], [START_REF] Peterson | Common mode signal rejection methods for MRI: Reduction of cable shield currents for high static magnetic field systems[END_REF], which would otherwise lead to not only strong image quality degradation but also damage to the device (MEMS or/and MDC). The same was done on the RF signal reception cable.

Multiple axial slices were acquired using gradient echo (GRE) and fast spin echo (FSE) sequences. Sequence acquisition parameters are summarized in Table II.

The sMEMS, pPIN and pMEMSWG REC prototypes were compared in terms of mean SNR and SNR isocontours. Although the MR control logic can be used directly to control the sMEMS REC, the WG and the associated input control voltage line (including traps) were also used to assess the effects of employing the WG on image quality (sMEMSWG REC).

Images were post-processed using Matlab software (Mathworks, Milwaukee, USA), and mean SNR and signal uniformity distributions were calculated. The SNR was determined as the ratio of the mean signal intensity calculated on the entire phantom image and the standard deviation of a rectangular ROI placed outside the phantom. The SNR distribution map (SNR isocontours) was calculated and drawn for each REC. Then, mean SNR values were calculated for FSE acquisitions on concentric circles centered on the REC loop for radiuses between 11 mm and 22 mm.

III. RESULTS

The measured frequency and temporal response parameters for the three REC prototypes in coupled and decoupled states are summarized in Table III. In coupled state, reflection coefficient values S11 at F0 were approximately -30 dB for all RECs. The loaded quality factor of the sMEMS was significantly lower (37%) than the pMEMS REC, which in turn had a slightly higher Q-value that the pPIN REC. Unloaded (data not reported) and loaded Q-values were almost similar for each individual REC.

In the decoupled state, measured reflection coefficient values S11 at F0 were measured to be almost 0 dB (no resonance) for all RECs. Resonance frequencies of pMEMS and pPIN RECs were shifted from 63.95 MHz to 20.47 MHz and 29.3 MHz, respectively. In the case of the sMEMS REC, the loop is completely open and thus the REC was not resonating at all. The mutual decoupling of the two loops at F0 was also shown with a drop in S21 measurements of more than 30 dB for all RECs.

Switching delays to both coupled and decoupled states were less than 0.7 µs for the pPIN REC. Regarding both MEMS RECs, coupling delays were equal to 7.5 µs and 2.1 µs for sMEMS and pMEMS, respectively, while decoupling delays were equal to 1.8 µs and 9.8 µs for sMEMS and pMEMS, respectively.

For imaging experiments, the presence of traps on the DC control line and DC power supply lines of the MDC was mandatory in order to have an unaltered DC bias and stable power supply during the scan. Indeed, in the absence of traps, RF signals induced by the transmitter body coil were superimposed onto the DC control signal (up to 20 V peak to peak, depending on the sequence used) and could inappropriately couple or decouple the REC. In that case, the acquired image is extremely noisy and moreover, could lead to damaging the MDC board and/or the MEMS switch. This occurs when the number of traps is inadequate.

From a qualitative point of view, Fig. 7 shows that the obtained images display similar elliptical image sensitivity patterns and no signal saturation even close to the REC. No artifacts due to active decoupling failure were observed on the images when using GRE or FSE. Small holes were observed on each image (anterior part of the phantom) obtained by both MEMS RECs.

Mean SNR values calculated for FSE acquisitions on concentric circles centered on the REC loop for radiuses between 11 mm and 22 mm showed that for all radiuses, SNR decreased in the following order: pPIN > sMEMS > pMEMSWG > sMEMSWG. The SNR isocontours calculated for all experimental configurations and prototypes using FSE sequences are displayed in Fig. 8. Figure 8-a enables an easy comparison of the image quality of sMEMS, pMEMSWG and pPIN RECs together; Fig. 8-b compares sMEMSWG, with pMEMSWG (performance of sMEMS and pMEMS in the same control conditions), while a comparison between sMEMS and sMEMSWG (WG control technique effect) is presented in Fig. 8-c.

IV. DISCUSSION

In this work, we evaluated the serial and parallel active decoupling of MEMS REC performances in terms of switching delays, quality factor, decoupling isolation (efficiency), delays (speed) and image SNR.

Opening and closing delays of the MEMS itself are 1 µs and 4 µs, respectively, which largely explains the decoupling and coupling delay differences of the same MEMS REC.

Opening of the MEMS leads to coupling of the pMEMS REC and decoupling of the sMEMS REC, which thus display almost similar delays of 2.1 µs and 1.8 µs, respectiv Closing of the MEMS switch instead leads to decoupling of the pMEMS REC and coupling of the sMEMS REC, which in this case display delays of 9.8 µs and 7.5 µs, respectively. The slight difference in delay (2.3 µs) in the last case is probably due to the specific operation technique of each configuration (by closing the MEMS, the REC loop is closed in coupled state in the case of the sMEMS REC while in the case of the pMEMS REC, the resonance frequency is shifted by shortcircuiting the tuning capacitor). With switching delays between 0.25 µs and 0.7 µs, the reference pPIN REC switches faster than the MEMS-based REC for coupled and decoupled states. These relatively longer delays are due to the time necessary to move the switch mechanically and to the switching delays of its driver circuit [START_REF] Maunder | Comparison of MEMS switches and PIN diodes for switched dual tuned RF coils[END_REF]. The increased delays by adding such driver circuit is also demonstrated in a recent work [START_REF] Der | Design of a high power PIN-diode controlled switchable RF transmit array for TRASE RF imaging[END_REF] using a PIN diode driver for high-power pulses where the rise-time is close to 1 µs and the fall-time increases to 7.4 µs, which are comparable to the results obtained with controlled MEMSs. Delays are nevertheless of the same order as optical-based decoupling circuits with 13.6 µs and 1.7 µs for tuning and detuning, respectively [START_REF] Saniour | Active optical-based detuning circuit for receiver endoluminal coil[END_REF], which proved to be effective. In any case, MEMS switching delays are still small and compliant with most MR clinical applications where the RF pulse duration of imaging sequences as well as signal readout time [START_REF] Maunder | Comparison of MEMS switches and PIN diodes for switched dual tuned RF coils[END_REF] are of the order of milliseconds. The only case that would be problematic in our application is that of ultra-short echo times (UTE) in the range of a few microseconds.

An equivalent REC devoid of any decoupling circuit was built and characterized and it has a Q-value of 66. Among the REC loop, the pMEMS REC had the highest Q-value (~65). It is comparable to the pPIN REC (~62), with the lowest value being for the sMEMS REC (~41). This is due to the MEMS characteristics and its location on the REC loop. In the case of the pMEMS REC in the coupled (reception) state, the MEMS is open. In this state, it can be considered as an additional capacitor of only about 1.5 pF placed in distal position in parallel to the tuning capacitor of the REC. Because it is a mechanically open switch, it has a high isolation resistance of approximately 10 MΩ. Thus, the MEMS does not impact the real part of the REC impedance, which affects the quality factor minimally. Rescia et. al. [START_REF] Rescia | RECEIVE-ONLY SURFACE COIL 'WITH IMPROVED DETUNTNG FOR PRE-CLINICAL MR1 STUDIES[END_REF] also demonstrated on a 4cm surface coil at 400 MHz (9.4 T) that the MEMS, placed in parallel to the loop, had almost no effect on the quality factor compared with other electrical components such as PIN diodes. In the case of the REC used, the PIN diode had a very moderate effect on the Q-factor. For the sMEMS REC, the switch is closed in the reception phase. It can then be considered as a significant 0.5 Ω to 1 Ω parasitic resistance [START_REF] Bulumulla | MEMS switch integrated radio frequency coils and arrays for magnetic resonance imaging[END_REF], [START_REF] Keimel | Microelectromechanical-Systems-Based Switches for Power Applications[END_REF] in the loop that should be compared with the loop size and its equivalent electrical resistance. This explains the experimental 0.62 ratio of Q-values between the sMEMS REC and a REC without any decoupling circuit. This ratio is comparable to the 0.59 theoretical Q-ratio calculations based on resistive losses [START_REF] Giovannetti | Sample-Induced Resistance Estimation in Magnetic Resonance Experiments: Simulation and Comparison of Two Methods[END_REF].

Regarding decoupling efficacy, S11 responses of around 0 dB demonstrate an efficient active decoupling of all RECs especially for sMEMS REC with an open loop. For all RECs, the S21 results confirm the decoupling efficacy with a drop of more than 30 dB in S21 measurements at F0. This was confirmed on MR images where no signal artifacts were detected.

Signal intensity distribution, SNR isocontour and mean SNR values of MR images obtained with sMEMS, pPIN and pMEMSWG RECs confirm that MEMSs are relevant systems for achieving active decoupling. In our experiments, the pPIN REC always provided the highest SNR. This was expected compared with the sMEMS REC, which had the lowest Qvalue owing to the presence of an additional parasitic resistance in the receive phase. In addition, despite the sMEMS decoupling signal being generated by the MR scanner through the A-plug, the external power supply was still used (to provide 10 V and 82 V bias voltages) and thus associated coaxial cables and traps were also used, which led to an additional noise on the images. The pMEMS REC provides an SNR higher than both the WBC (gain of 10) and the array coil. The latter displays high SNR only on the anterior surface of the phantom, (which is not our region of interest) with a rapid decrease when moving away from it. Since our region of interest is the internal surface, the array coil is not a convenient solution for our imaging target.

In the case of the pMEMSWG REC, SNR values are lower than those of the pPIN REC, which is not in agreement with the RF bench characterization results in terms of Q-value that was the highest for the pMEMS REC and thus we expected a better or at least similar SNR between pMEMS and pPIN RECs. As mentioned above, the use of an external power supply for the MDC of all MEMS-based RECs is the first factor affecting the measured SNR values negatively. In the case of the pMEMSWG REC, there is an additional source of noise originating from the external WG used to obtain the required coupling and decoupling signals (the MR system used could not handle it directly).

sMEMSWG and pMEMSWG REC experiments based on the same control condition (waveform generator) shown in Fig. 8b demonstrate that the pMEMSWG REC presents a higher SNR (about 45%) than the sMEMSWG REC, which means that the pMEMS REC (without WG control) can be estimated to yield an SNR higher (45%) than the sMEMS REC, in agreement with the obtained Q-values.

In order to characterize this added noise due to the external WG, images were also acquired using the sMEMS REC with the decoupling signal coming from the external wave generator (sMEMSWG REC). The resulting images (Fig. 7 columns 2 and 3) and SNR isocontours (figures 8-c) demonstrate that the use of the external WG control leads to an SNR reduction of approximately 35%, which can also be used to extrapolate SNR values that could be obtained with a pMEMS REC (without the use of the external WG). The pMEMSWG performance compared with the pPIN performance is hence significantly penalized by this step, which was mandatory because of the use of an MR450w system. Performing the experiments on a new GE MR scanner would ease the use of MEMS as a decoupling component thanks to the native integration of 82-V and 10-V delivery voltages as well as DC bias delivery for active decoupling in logic suited for all MEMS configurations. Such "on-board" voltage sources would then remove the additional noise from the mandatory setup described.

It should be noted that three separate but identical (size and solution) phantoms were used for the PIN, sMEMS and pMEMS experiments. The small holes that can be seen on the MEMS images correspond to air bubbles (see also Fig. 3-b) in each MEMS phantom due to the cylindrical vessel not being completely air-tight and not due to the MEMSs themselves. Coincidentally, the PIN phantom cylindrical vessel was almost completely air-tight and did not have air bubbles; thus, no hole was observed on the MR images obtained with the pPIN REC.

In addition, SNR values in the ROI of the pMEMS REC are much higher (factor of approximately 10) than those obtained with the Whole Body Coil (WBC) or with a commercial external array coil (GEM Flex Coil 16-S Array, 1.5T Receive Only, NeoCoil, USA) as shown in Fig. 9. It should be noted that the array coil displays its maximum SNR on the external surface of the phantom, which is not our ROI, and the SNR decreases rapidly when moving away from the surface so that in the ROI, its SNR is much lower than that achieved with our REC.

V. CONCLUSION

In conclusion, the pMEMSWG configuration (based on an external control) presented here does not yet provide significant advantages over the pPIN configuration (Fig. 8-a) mainly because of the external equipment required (WG, DC bias voltages, traps and coaxial cables). As perspectives, it is expected that the pMEMS REC decoupling solution should present higher SNRs than the conventional pPIN configuration, since the measured Q-values suggest that removing the external control and using an MR-compatible control method for the pMEMS REC would yield higher SNRs than those achieved with the pPIN REC. An interesting perspective for the use of MEMS switch resides in serial configuration (in series with the REC loop). Although the SNR is reduced, the decoupling remains efficient and MEMS could be used not only for active decoupling but also to allow for REC loop geometry reconfiguration. This could be particularly relevant in the case of colon wall examinations where REC-loop orientation with respect to the main magnetic field changes with location, leading to MR signal distribution changes. We believe that a combination of both sMEMS and pMEMS configurations in the design of new reconfigurable RECs may offer interesting designs and open the way for novel applications.

Fig. 1 .

 1 Fig. 1. Description of MEMS switch and its driver. (a) Scheme of the controllable RF MEMS switch developed by GE Healthcare Inc.(b) Photograph of the 1 cm × 1 cm chip package of only the MEMS switch with 1 Algerian dinar coin. Second line shows two small diagrams of the MEMS and its driver circuit (MDC). The MDC consists of an electronic Decision Unit (DU) biased with 10 V and 0 V input voltages and allowed to compare a predetermined threshold voltage to the control signal. (c) If the DU receives a control signal of -5 V, an external 82 V (typical value) is delivered between the gate (G) and beam (B) electrodes of the MEMS thereby closing the switch. (d) If the DU receives a control signal of 7 V (or 100 mA delivered by the MRI system), the external 82 V is blocked and cannot be delivered to the MEMS gate. In this case, 0 V is delivered between G and B thereby opening the switch.

Fig. 2 .

 2 Fig.2. Electrical schematics (first row) and the associated built prototypes (second row) of RECs using a controllable MEMS switch for active decoupling integrated (a,d) in series (sMEMS REC) or (b,e) in parallel (pMEMS REC) to the loop or using (c,f) a PIN diode integrated in parallel to the loop (pPIN REC). Ct and Cm are tuning and matching capacitors, respectively. R_loop and L_loop are the electrical resistance and inductance of the rectangular loop with 5.1 mm × 47 mm × 0.8 mm of width, length and thickness dimensions, respectively. The coaxial cable is the RF output used to transmit the NMR signal.

Fig. 3 .

 3 Fig. 3. (a) Drawing and (b) photograph of the cylindrical phantom used to load the REC for characterization as well as for imaging. The dimensions of the cylindrical phantom are 11 mm inner and 90 mm outer diameters and 100 mm length. It is filled with a solution of 1.25 g NiSO4 × 6H2O + 5 g NaCl per liter of distilled water mimicking tissue losses.

Fig. 4 .

 4 Fig. 4. First RF characterization set-up image (a) and the associated reflection coefficient (S11) measurement method (b) by using a VNA. At coupling state (resonance frequency), amplitude at Larmor resonance frequency (F0) and quality factor were derived from the S11 response. At decoupling state, S11 amplitude at F0 was measured. (c) Decoupled dual loops position. (d) Second RF characterization set-up image and (e) the associated S21 measurement method using the VNA.

Fig. 5 .

 5 Fig. 5. Switching delays set-up image (a) and the temporal response measurements (b) of the MEMS Receiver Endoluminal Coil (REC). A frequency generator excites a circular copper loop to generate an RF magnetic field at the resonance frequency of the REC. The latter detects the RF signal while in coupled state but is unable to do so in the decoupled state. Coupling and decoupling are ensured by applying a square electrical signal from the WG. The received signal is observed on a digital oscilloscope. (b) Then, delays to couple or decouple the REC are estimated by measuring the delay between the applied control signal and the receive RF signal response.

Fig. 6 .

 6 Fig.6. Implementation on a 1.5-T MR system of the Receiver Endoluminal Coil (REC) using controlled MEMS switch. The body coil is used as an RF transmitter coil and the endoluminal coil as receiver. The RF circuit makes it possible to deviate the DC signal. The trigger by the WG makes it possible to ensure the DC control without the need to add an inverter circuit in the case of pMEMS REC. The REC is connected to the MR scanner via a specific interfacing connector (A-plug) with an adequate file configuration to identify the REC. Since both REC and phantom have their axes (lengths) aligned with the B0 field, the 2D axial images are acquired using gradient echo (GRE) and spin echo (FSE) sequences.

Fig. 8 .

 8 Fig. 8. Signal-to-Noise Ratio (SNR) distributions based on iso-contour lines calculated and drawn on axial FSE images acquired by using the different REC prototypes. Numbers on each iso-contour line represent the calculated SNR value obtained with the associated REC. (a) Comparison between sMEMS, pMEMSWG and pPIN RECs to assess the quality image performance of MEMS RECs. (b) Comparison between sMEMSWG and pMEMSWG RECs acquired with the same experimental conditions to assess the performance of MEMS on the image quality in each configuration. (c) Comparison between sMEMS and sMEMSWG RECs to evaluate the effect of the use of the waveform generator on the acquired images.

Fig. 9 .

 9 Fig. 9. SNR maps obtained with (a) the whole-body coil, (b) an external commercial array coil and (c) the pMEMS REC.The pMEMS REC provides an SNR higher than both the WBC (gain of 10) and the array coil. The latter displays high SNR only on the anterior surface of the phantom, (which is not our region of interest) with a rapid decrease when moving away from it. Since our region of interest is the internal surface, the array coil is not a convenient solution for our imaging target.

TABLE I TUNING

 I AND MATCHING CAPACITOR VALUES USED

	RECs	MR switches	Ct values (pF)	Cm values (pF)
	pMEMS	MEMS	142.0	582.0
	sMEMS	MEMS	120.2	382.0
	pPIN	PIN diode	158.2	549.0

TABLE II ACQUISITION

 II PARAMETERS USED FOR BOTH GRADIENT ECHO (GRE) AND SPIN ECHO (FSE) SEQUENCES.

	Sequence Orientation	TR/TE (ms)	FA (°)	FOV (mm x mm)	Slice thickness (mm)	Spacing slices (mm)	Matrix size	Receive BW (±kHz)	Number of slices
	GRE	Axial	400/9	80	100x100	2	0.5	256x256	11.9	13
	FSE	Axial	3100/14.1	90/180	100x100	2	0.2	256x256	15.6	13
						pPIN		sMEMS	sMEMS WG	pMEMS WG

GRE FSE Fig. 7. Representative 2D axial MR images acquired with GRE and FSE sequences for the different RECs prototypes (PIN and MEMS) and displayed using a narrow signal intensity window.

TABLE III MEASURED

 III FREQUENCY AND TEMPORAL RESPONSE PARAMETERS FOR THE THREE COIL PROTOTYPES WITH COUPLING AND DECOUPLING STATES.

	States	Measured	pPIN	sMEMS	pMEMS
		F0 (MHz)	63.95	63.92	63.95
	Coupled	S11 at F0 (dB) S21 at F0 (dB)	-28.70 -18.45	-36.40 -26.20	-29.20 -20.30
		QLoaded	62.50	41.20	65.10
	Decoupled	S11 at F0 (dB) S21 at F0 (dB)	-0.28 -48.10	-0.09 -56.10	-0.10 -55.00
		Delays to couple (µs)	0.70	7.50	2.08
		Delays to decouple (µs)	0.25	1.84	9.80

ACKNOWLEDGMENT

The authors would like to thank Guido Kudielka, Julie Poujol, and Miguel Navarro from GE Healthcare for their help on MRI interfacing and configuration.

Authors would like to thank GE Healthcare, Inc. and ANRT for funding this project. This work was conducted in the scope of the LABEX PRIMES of University of Lyon (ANR-11-LABX-0063), within the program "Investissements d'Avenir" (ANR-11-IDEX-0007) operated by the French National Research Agency (ANR).