Virginie Vandenbossche
email: virginie.vandenbossche@ensiacet.fr

Julien Brault

Gérard Vilarem

Luc Rigal

Bio-catalytic action of twin-screw extruder enzymatic hydrolysis on the deconstruction of annual plant material: Case of sweet corn co-products

Keywords:

Sweet corn co-product Twin-

Introduction

The lignocellulosic materials from agriculture and forest by-products represent an important source of renewable and carbon-neutral energy. These resources are clean, cheap, available in large quantities, and are independent of geographical location, plus they are carbon neutral and renewable. However, the lignocellulosic biomass is a complex assembly of cellulose, hemicelluloses and lignin whose extraction and conversion requires the use of a refining process [START_REF] Himmel | Biomass recalcitrance. Engineering plants and enzymes for biofuels production[END_REF].

These transformation processes are actually studied worldwide and many reviews exist on the subject (Van Dyk and Pletschke 2012). They are typically realized in two steps: one of pretreatment intended to improve the accessibility of the cellulose and a second allowing hydrolysis of the cellulose by enzymatic action. [START_REF] Sun | Hydrolysis of lignocellulosic materials for ethanol production: a review[END_REF]Cheng, 2002, Mosier et al. (2005), [START_REF] Zheng | Overview of biomass pretreatment for cellulosic ethanol production[END_REF], [START_REF] Harmsen | Literature Review of Physical and Chemical Pretreatment Processes for Lignocellulosic Biomass[END_REF] and [START_REF] Alvira | Pretreatment technologies for an efficient bioethanol production process based on enzymatic hydrolysis: a review[END_REF] have reviewed pre-treatment tech-nologies for a bioethanol production process based on enzymatic hydrolysis. Most cited pretreatments, among which dilute acid pretreatment [START_REF] Saha | Dilute acid pretreatment, enzymatic saccharification and fermentation of wheat straw to ethanol[END_REF], steam explosion [START_REF] Oliva | Effect of lignocellulosic degradation compounds from steam explosion pretreatment on ethanol fermentation by thermotolerant yeast Kluyveromyces marxianus[END_REF][START_REF] Cara | Enhanced enzymatic hydrolysis of olive tree wood by steam explosion and alkaline peroxide delignification[END_REF][START_REF] Varga | Optimization of steam pretreatment of corn stover to enhance enzymatic digestibility[END_REF][START_REF] Ballesteros | Ethanol production from steam-explosion pretreated wheat straw[END_REF] and ammonia fiber explosion [START_REF] Galbe | Pretreatment of lignocellulosic materials for efficient bioethanol production[END_REF] are the most well known, are penalized by implementation constraints, technology or reagent costs, or inhibitor production for enzymatic hydrolysis or ethanolic fermentation.

Soft alkaline pretreatment is one approach that has several potential advantages compared to other pretreatment processes, including lower operating cost, reduced degradation of holocellulose, and subsequent formation of inhibitors for downstream processing [START_REF] Carvalheiro | Hemicellulose biorefineries: a review on biomass pretreatments[END_REF][START_REF] Kumar | Physical and chemical characterizations of corn stover and poplar solids resulting from leading pretreatment technologies[END_REF][START_REF] Mcintosh | Optimisation of dilute alkaline pretreatment for enzymatic saccharification of wheat straw[END_REF]Taherzadeh and Karimi, 2008). The main mechanisms of soft alkaline pretreatment are hydroxyl group solvation, leading to the rupture of intra or inter molecular hydrogen bonds, and the breakdown of ester bonds with cleavage of linkages in the lignocellulosic cell wall matrix, all of which lead to the alteration of the lignin structure, the hydrolysis of the lignin-hemicellulose complex, cellulose swelling, and the partial decrystallization of the cellulose [START_REF] Bobleter | Hydrothermal degradation of polymers derived from plants[END_REF][START_REF] Sun | Hydrolysis of lignocellulosic materials for ethanol production: a review[END_REF].

A way to improve process costs is to increase the solid concentration of biomass in each unit operation. Such high solid concentrations dramatically increase economic viability, reducing operating costs due to decreased volumes. However, increasing solid concentration increases the apparent viscosity of biomass slurries, making mixing and conveying operations more challenging. Among the processes used to carry out pretreatment with a minimum number of steps, twin-screw extrusion technology has many advantages and allows working with high solid concentrations. It produces a high shear, rapid heat transfer, and effective and rapid mixing, in a continuous operation, with good adjustability of treatment steps.

Co-penetrating and co-rotating twin-screw extruders are most common [START_REF] Dziezak | Single and twin-screw extruders in food processing[END_REF], and a very wide choice of screw elements is available. The screw profile is defined by the arrangement of the different screw elements and their characteristics (pitch, length, number and shape of screw, and angle between two successive elements in case of paddle screws) in different positions and spaced differently.

The performance of twin-screw extrusion and the influence of the operating parameters have been studied on biomass fractionation from poplar (N' Diaye and Rigal, 2000), Miscanthus sp. [START_REF] De Vrije | Pre-treatment of Miscanthus for hydrogen production by Thermotoga elfii[END_REF], sugar beet pulp [START_REF] Rouilly | Thermo-mechanical processing of sugar beet pulp. I. Twin-screw extrusion process[END_REF], sunflower [START_REF] Evon | Direct extraction of oil from sunflower seeds by twin-screw extruder according to an aqueous extraction process: feasibility study and influence of operating conditions[END_REF][START_REF] Kartika | Extraction of sunflower oil by twin-screw extruder: screw configuration and operating condition effects[END_REF], soybean hulls [START_REF] Yoo | Thermo-mechanical extrusion pre-treatment for conversion of soybean hulls to fermentable sugars[END_REF], rice straw [START_REF] Chen | Pretreatment of rice straw using an extrusion/extraction process at bench-scale for producing cellulosic ethanol[END_REF], and wheat straw and bran [START_REF] Marechal | Twin-screw extrusion and filtration for xylan production from wheat straw and bran[END_REF][START_REF] Zeitoun | Twin-screw extrusion for hemicellulose recovery: influence on extract purity and purification performance[END_REF]Vandenbossche et al., 2014a).

More particularly, twin-screw extrusion can be used to pretreat different biomasses for the production of sugars, and several authors have reported this type of application over the last few Constraint elements and operating conditions for Alkaline pre-treatment (CF2C = reversed double-thread screw, CF1C = reversed simple-thread screw, DM is the dry matter of inlet sweet corn, Ss is the screw rotation speed (rpm), pHex is the pH of the extrudate, L/S is the liquid/solid ratio, FR is the extruder filling ratio, I is the current feeding the motor (A). years. [START_REF] Zheng | Extrusion pretreatment of lignocellulosic biomass: a review[END_REF] provide an overview of the extrusion pretreatment of lignocellulosic biomass. [START_REF] Lamsal | Extrusion as a thermo-mechanical pre-treatment for lignocellulosic ethanol[END_REF] first showed that extrusion led to higher reducing sugar yields compared to grinding, and they studied the process on wheat bran in two steps: impregnation with water followed by mechanical treatment (grinding or extrusion). A combination of both extrusion and alkaline pretreatment has been explored more recently [START_REF] Zhang | Alkali combined extrusion pretreatment of corn stover to enhance enzyme saccharification[END_REF][START_REF] Karunanithy | Optimization of alkali concentration, switchgrass particle size and extruder parameters for maximum sugar recovery using reponse surface methodology[END_REF][START_REF] Duque | Optimization of integrated alkaline-extrusion pretreatment ofbarley straw for sugar production by enzymatic hydrolysis[END_REF][START_REF] Um | Chemicals effect on the enzymatic digestibility of rape straw over the thermo-mechanical pretreatment using a continuous twin screw-driven reactor (CTSR)[END_REF][START_REF] Kang | Optimization of alkali-extrusion pretreatment with twin-screw for bioethanol production from Miscanthus[END_REF][START_REF] Han | High efficiency bioethanol production from barley straw using a continuous pretreatment reactor[END_REF]. [START_REF] Karunanithy | Thermo-mechanical pre-treatment of feedstocks[END_REF] provide an overview of this type of thermo-mechanical pretreatment, including the mechanism influencing extruder and feedstock parameters, plus evaluation of pretreatment efficiency. A continuous process combining alkaline thermo-mechano-chemical pretreatment, followed by injection of enzymes into the twinscrew extruder, called "bioextrusion" was developed and tested on different biomass such as sweet corn residue (SC), blue agave bagasse, oil palm empty fruit bunch as residue from palm oil manufacture, and barley straw (Vandenbossche et al., 2014b;[START_REF] Duque | Sugar production from barley straw biomass pretreated by combined alkali and enzymatic extrusion[END_REF]. This new process leads to excellent mixing of the enzymes with the pretreated biomass at high concentrations, and allows saccharification to begin during bioextrusion.

The implementation of the continuous process, and the influence of the bio-catalytic action of hydrolytic enzymes on deconstruction of annual plant material in the twin-screw extruder during "bioextrusion", is studied here in case of a weakly lignified material: the sweet corn co-products.

Experimental

Materials

Feedstocks

Dehydrated sweet corn co-products, provided by SARL Soupro+, came from an industrial corn grain cannery. It was milled using a hammer mill fitted with a 6 mm screen, and contained 4% ash, 39% cellulose, 36% hemicelluloses and 4% lignin (all expressed in %/dry matter and determined according to the French standard NF V 03-322 and ADF-NDF method cited in Section 2.3).

Enzymatic cocktails

Enzymatic saccharification was conducted using the Saccharised C6 cocktail from Advanced Enzyme Technologies (India) in a citrate buffer (300 mM, pH 4.8). A solution of 1% of saccharised C6 cocktail is characterized by: protein content of 9 mg/ml, cellulose activity of 5s 5FPU/ml, endoglucanase activity of 1940nkat/ml, xylanase acivity of 16,900nkat/ml, and ␤-glucosidase activity of 310nkat/ml. The protein content was measured after acetone precipitation with Bio-Rad DC protein assay kit, cellulose and endoglucanase activity according to [START_REF] Ghose | Measurement of cellulase activities[END_REF], xylanase activity according to [START_REF] Bailey | Inter-laboratory testing of methods for assay of xylanase activity[END_REF] and ␤-glucosidase activity according to [START_REF] Bailey | Induction, isolation and testing of stable Trichoderma reesei mutant with improved production of solubilizing cellulase[END_REF].

A preliminary study showed that the mixture of advance enzyme had an optimum temperature at 54 • C. Beyond 56 • C enzyme activity begins to decrease, to keep a safety margin we placed at 50 • C for the bioextrusion trials.

Twin-screw extruder

Thermo-mechano-chemical alkaline pre-treatment and the neutralization phase, were conducted using a co-penetrating and co-rotating Clextral BC 45 twin-screw extruder. For the bioextrustion phase a Clextral BC 21 is used. Both BC 45 and BC 21 had seven modular barrels, each of these measuring 200 mm in length for BC 45 and 100 mm for BC21. Both extruders were equipped with different segmental screw elements. The metallurgy is, respectively, CLX200 for BC45 and hastelloy for BC21. Both are corrosion resistant. Modules were thermo-regulated by thermal induction for BC45 and by heater band for BC21; module cooling was by water circulation. A filter section was used to enable the filtrate to be collected and consisted of six hemispherical dishes with conical holes (1 mm entry, 2 mm exit). The outputs of extruders were not equipped with die. Schematic representation of the sequence of steps for the process carried out in two successive twin-screw extruders is shown in

Alkaline thermo-mechano-chemical pre-treatment and neutralization phase

The alkali used for the pretreatment is sodium hydroxide (NaOH), and the neutralization step uses phosphoric acid (H 3 PO 4 ). Operating conditions are described in Tables 1 and3.

Feedstocks were fed into the extruder's first module and the alkaline solution injected using a piston pump. A first zone of mechanical pressure, consisting of a succession of bilobal paddles, ensures grinding and good mixing of the material with this alkaline solution. An acid solution was then injected using a piston pump, to neutralize the medium and reduce the viscosity of the matter to ensure good filtration. A second zone of mechanical pressure guarantees good mixing with the medium, and fast neutralization. The filtration zone situated in module 6 was carried out by a third zone of mechanical pressure using reversed pitch screws to ensure the formation of a dynamic plug, and thus the pressing of the mixture.

Bioextrusion

Bioextrusion consists of introducing an enzyme cocktail into the extruder, and then using a succession of compression and expansion steps to facilitate the penetration of these enzymes into the matter. Operating conditions are shown in Tables 2 and3.

Analytical methods

Dry matter and parietal compounds

Moisture contents were determined according to the French standard NF V 03-903 and mineral contents according to the French standard NF V 03-322. An estimation of the three parietal constituents (cellulose, hemicelluloses, and lignins) contained in the solids, was made using the ADF-NDF method of Van Soest andWine (1967, 1968). All determinations have been carried out in triplicate and standard deviation was less than 1.5% for all measurements.

An estimation of the hot water-soluble components contained, was made by measuring the mass loss of the test sample after 1 h in boiling water. This method has been adapted using standard TAPPI 204 cm-97 on the Fibertec Tecator M1017 apparatus. All determinations were carried out in duplicate.

Sugars

Reducing sugars were determined using the DNS method [START_REF] Miller | Use of dinitrosalicylic acid reagent for determination of reducing sugar[END_REF].

Total sugars were determined after acid hydrolysis using a method adapted from NERL [START_REF] Sluiter | Determination of structural carbohydrates and lignin in biomass[END_REF]. 10 ml of filtrate was mixed with 1.25 ml 72% H2SO4. Hydrolysis kinetics were carried out in sealed tubes between 30 and 90 min at 100 • C. After cooling, samples were neutralized with 3.6 ml NaOH 32%(m/v), diluted and filtered (0.45 m). Analysis was then made using highperformance liquid chromatography (HPLC).

Free and total sugars were measured using HPLIC (DIONEX ICS-3000 coupled with pulsed amperometric detection (PAD) and fitted with a Carbopac PA1 column).

Enzymatic hydrolysability was determined by enzymatic hydrolysis in 50 mM citrate phosphate buffer (pH 4.6) in the presence of Advanced enzymes (2.5%/DM substrate) at 50 • C for 48 h. It has been calculated as the percentage of reducing sugars released by enzymatic hydrolysis, relative to dry matter.

Sugars released after bioextrusion were measured on water solubles extracted from bioextrudates. The latter, obtained after 1, 3, 5 or 7 passages in the BC21 twin-screw extruder, were first diluted in water with a Liquid/Solid ratio of 20 and then filtered onto a glass filter (porosity grade: 2).

Degree of polymerization (DP) of oligosaccharides

The degree of polymerization (DP) of oligosaccharides was determined in high performance anion-exchange chromatography equipped with a pulsed amperometric detector and an eluent generator EluGen ® (HPAEC-PAD, ICS-3000, Dionex). The analyses were carried out with an anion exchange column (250 mm Carbopac PA200) and used a mixture of two eluents (A:100 mM sodium hydroxide and B:100 mM sodium hydroxide with 1 M sodium acetate) with a gradient from 100% of A at t = 0 to 75% of A and 25% of B at t = 35 min. The flow rate of the column was fixed at 0.35 mL/min.

Residence time distribution (RTD)

Sweet corn particles colored with Erythrosin were placed quickly (<2s) on the first screw element. This dye was chosen as a tracer for its neutral characteristics with respect to the process and because of its good colorization of most vegetable matter, itself often highly colored [START_REF] N'diaye | Fractionnement de la Matiere Veígeítale: Mise au Point d'un Proceíde Thermo-Meícano-Chimique et Modeílisation du Reíacteur bi[END_REF]. Samples of extrudate are taken every 5 s at the solid outlets of the extruder. All samples are dried at 103 to eliminate large size particles which affect measurements. The 'a' coordinate of colored outlet samples in the CIE (L*a*b) system, is read directly using a spectrophotocolorimeter (Model CM 500i, Minolta, Japan, illuminant: D65, observer angle: 2 • ). The RTD data are examined through typical distribution versus time plots. RTD is defined as:

Ei = Ci n i=1 Ci × Dt
where Ci is the tracer concentration in each sample and Dt is the sampling period. Typical curves are shown on (Fig. 3).

Dynamic viscosity

Viscosity values were determined in accordance with TAPPI Standard T 230 om-94 "viscosity of pulp" (capillary viscometer method). In our case, the sample was solvated with cupriethylenediamine and passed through a canon-fenske Viscometer, type 150 at 25 • C. The viscometer readings were performed three times for each sample.

Results and discussion

The process of deconstruction of lignocellulosic plant material is carried out using two consecutive extruders BC 45 and BC 21. It included three different parts: an alkaline pre-treatment (in BC 45), a neutralization phase (in BC 45), and an enzyme impregnation phase (bioextrusion, in BC 21) during which the hemicelluloses and cellulose saccharification began. (Figs. 1 and2).

Alkaline pre-treatment and neutralization phase

The first step of the process is the destructuration of cellulosic material under alkaline conditions.

The alkaline extraction conditions for hemicelluloses and lignin in twin-screw extruders have been widely studied, for instance on sorghum and poplar wood fibers [START_REF] N'diaye | Fractionnement de la Matiere Veígeítale: Mise au Point d'un Proceíde Thermo-Meícano-Chimique et Modeílisation du Reíacteur bi[END_REF] or on wheat straw [START_REF] Magro | Valorisation des Pailles de blé par Fractionnement Thermo-Mécano-Chimique dans un Réacteur bivis[END_REF][START_REF] Marechal | Analyse des Principaux Facteurs Impliqués dans le Fraction-Nement Combiné de Pailles et de Sons de blé en Extrudeur bi-vis: Obtentiond'agro-Matériaux[END_REF][START_REF] Zeitoun | Twin-screw extrusion for hemicellulose recovery: influence on extract purity and purification performance[END_REF], and this previous work has allowed us to define the configuration and screw profiles used in this study. The aim of this first step is to open up the complex structure of the biomass, and facilitate access of the hydrolytic enzymes to polysaccharides, by increasing the surface area [START_REF] Karunanithy | Optimization of alkali concentration, switchgrass particle size and extruder parameters for maximum sugar recovery using reponse surface methodology[END_REF] and porosity [START_REF] Zhang | Alkali combined extrusion pretreatment of corn stover to enhance enzyme saccharification[END_REF]Vandenbossche et al., 2014b).

The thermomechanical effect of the flow restricting elements of the screw profile, ensures physical disintegration of the material by separating the fiber bundles. The presence of sodium hydroxide ensures additional chemical destructuration by solubilization of organic matter, especially hemicelluloses and lignin, depending on the amount used.

The neutralization phase is necessary to obtain a pH compatible with effective enzyme activity during bioextrusion. Phosphoric acid is used in this step because it is triprotic and so limits the mass of acid needed. This neutralization step also decreases the viscosity of the matter via a change of pH and a dilution effect, which facilitates filtration and plays a part in developing friction, shear, and impacts residence time. The effectiveness of the filtration step depends on the formation and stability of the "dynamic plug" formed in module 7, and to the presence of constraint elements (Fig. 2). The selection of these constraint elements is important for the formation of a stable "dynamic plug". The use of a C2FC -25 reversed double-thread screw failed to achieve a stable filtration despite varying the operating conditions, including a change in the amount of sodium hydroxide, in the liquid-solid ratio, or in the degree of filling (Table 1). Indeed, in the presence of insufficient backpressure, the operation of the extruder as a liquid/solid separator may be cyclic, with an accumulation of fluid in the filtration zone resulting in a loss of dynamic plug structure. Liquid separation is less effective and the solid is insufficiently pressed. This is accentuated in the presence of high levels of sodium or high liquid/solid ratios and can explain the worst results, obtained with a NaOH/DM ratio of 32.4%. A change in extruder configuration, consisting of replacing the C2FC -25 reversed double-thread screw with a reversed simple-thread screw C1FC -25, has achieved a steady state for a L/S ratio close to 8, a filling ratio (Q/S) of 0.07-0.08 and for a range of NaOH/DM ratios between 10% and 19%. The reversed simple-thread screw C1FC -25 ensured better back pressure than the C2FC -25 reversed double-thread screw. For subsequent work a minimum NaOH/DM ratio of 10.5% has been retained. Under these conditions, 8% of the cellulose is driven into the filtrate, and 41% of the hemicelluloses plus 14% of the lignins are extracted by the alkaline pretreatment including neutralization step (Table 4). The pH of the filtrate and of the extrudate at the end of this step is, respectively, of 5.5 and 6.

Development of the impregnation profile on BC 21

After the alkaline pretreatment and the neutralization step, enzymes are introduced into the conveying zone of the bioextrusion step (Fig. 2), and impregnation of the material is ensured by the use of a series of mechanical pressure and relaxation zones. Four constraint zones are implemented in modules 10, 12-14. The first three consist of a series of bilobe paddle screws, the first mounted at an angle of 90 • (no conveying effect), and the last two at -45 • (retention effect on the material). The fourth zone is formed either by a reversed screw or by a monolobe paddle screw mounted at an angle of -45 • . These constraint elements provide good mix- ing of the material. Between these areas of mechanical pressure, conveying elements with decreasing pitch ensure the transport of the mixture. In order to increase the residence time and optimize the biomass -enzyme contact, different constraint elements have been tested in module 14 (Tables 1 and2). The reversed doublethread screws C2FC -33 generated insufficient backpressure to form a stable "dynamic plug", whatever the length tested. Reversed screws with shorter pitch or monolobe paddle-screws were used and allowed a steady state to be achieved. The determination of the residence time distribution of the matter in the extruder for these two profiles B 3 and B 4, showed that the use of monolobe paddle-screws resulted in a lower axial dispersion of residence time as well as a slight shortening thereof. However, in both cases the retention time is very similar, close to two minutes (Fig. 3). Comparison of the yields of sugars released by enzymatic hydrolysis at a low concentration (2.5%,) without adding fresh enzymes, revealed no significant difference after 48 h (Fig. 4). At most, the hydrolysis kinetic was initially slightly accelerated. This result also showed that the implementation of a shearing element such as reversed screws, does not cause significant deactivation of the enzymes.

Increase in the residence time of the enzyme impregnation step: bioextrusion

As can be seen from Fig. 3 the residence time of the material in the BC 21 for the bioextrusion step is very close to 2 min whatever the restricting element used. This is very low compared to the times required for a significant increase in enzymatic hydrolysis for release of the monosaccharides. Thus, to understand and validate the action of enzymes in the extruder it is necessary to extend their action time during bioextrusion. The increased extruder contact time is simulated by recirculating the bioextrudate in BC21. The operation is undertaken using the same configuration and the same screw profile as for the first passage (Figs. 1 and2), but without further addition of enzyme solution. It is as if the duration of the contact time was multiplied by the number of passages through the extruder.

The material is worked on more and more with successive passages through the extruder, and becomes sticky. This pasty consistence is less and less textured, the material is liquefied gradually, and its rheological properties change. And this change in rheology tends to prove that a fiber destructuring effect is occurring during bioextrusion, which was also observed by [START_REF] Samaniuk | The effect of high intensity mixing on the enzymatic hydrolysis of concentrated cellulose fiber suspensions[END_REF], who have shown a synergistic relationship between mixing and enzyme activity during enzymatic hydrolysis. And by using a torque rheometer, they demonstrated that the torque required for mixing biomass samples with 20% solid dry matter, decreases in the presence of enzymes as a function of time, and this is accompanied by an increase in glucose converted.

3.4. Nature of the transformations of the lignocellulosic material in the twin-screw extruder during enzyme impregnation

Solubilization and extraction of saccharides

To study the impact of bioextrusion on the evolution of the matter destructuration, an analysis of dissolved compounds during this bioextrusion is made. To this end, samples of bioextrudate are collected after each passage through the BC 21 extruder, and then diluted in water, with a liquid/solid ratio (L/S) of 20. They are then pressed and filtered. Free sugars, reducing sugars and total sugars are then measured in the recovered filtrates.

To compare the thermomechanical effects with the thermomechanical effects coupled to enzymatic action, a test is carried out without the addition of enzyme, maintaining a solid liquid ratio of 2.5 by addition of water. Analysis of the resultant filtrate from the tests without enzyme, shows that very few saccharides are extracted during extrusion whatever the number of bioextrusion passages (Fig. 5). The thermomechanical effect generated by extrusion under these operating conditions does not act on the release of sugars, either in free form or in polysaccharide form. During the bioextrusion, free sugars appear, which means that the enzymes are active in the extruder and that monosaccharides are released (1-2% of dry matter introduced). Analysis of the reducing sugars and of the total sugars in the wash filtrates reveals the presence of sugars released by enzymes in the twin-screw extruder. These sugars are in the form of free sugars, oligosaccharides and polysaccharides, and their proportion increases with the number of passages through the BC21 extruder. Looking at the difference between the proportion of reducing sugars and of total sugars derived from the different determination methods, the DNS method quantifies only the reducing ends of mono, oligo and polysaccharides, whereas the method by acid hydrolysis covers all the monosaccharides in these sugars.

A control is prepared in order to compare the enzymatic action during bioextrusion with the enzymatic action in a stirred reactor. Alkali pretreated biomass is placed in contact with the enzymes in a stirred reactor under the same operating conditions as for the bioextrusion (T: 50 • C, enzyme rate: 4.6%, L/S: 2.5) for 70 min. The latter corresponds to the time necessary to achieve the seven passages including the dead time between two successive ones. The sugar released by enzymes after this time in the stirred reactor, is the same in terms of reducing sugars and less in terms of total sugars or free sugars, compared to those obtained in the twin-screw extruder. This reflects the fact that in the case of bioextrusion, the sugars are released in greater amounts in the form of longer chains or in the form of monosaccharides. The difference in the total amount released and the length of sugar chain would seem to show a difference of hydrolytic enzyme action. However, this is probably related to the mixing intensity in the extruder, inducing better penetration of endoglucanases inside the material. Analysis by ion chromatography HPAE-PAD with a Dionex CarboPac PA200 column, confirms the presence of polysaccharides released during bioextrusion (Fig. 6). Compared to standard maltodextrins, these would be oligomers with a low degree of polymerization (DP <6-7).

During the bioextrusion, the solubilization of the saccharides is accompanied by a destructuration of the biomass, resulting in an increase in insoluble fine particles carried along in the filtrate when washing the bioextrudate (Fig. 7). This increases with increasing number of passages, and may reflect the effect of breakdown of the biomass by hydrolysis of polysaccharides. Its proportion nearly doubled when extrusion was performed in the presence of enzymes for 7 successive passages.

Similarly, the amount of hot water soluble components measured in the bioextrudates increases. It doubles after 7 passages in the presence of enzymes compared to extrusion without enzymes (Fig. 8), confirming the effect of these hydrolytic enzymes in the extruder.

Modification of ligno(hemi) cellulosic fibers

The structural modification of the fibers is observed under an electron microscope throughout the pretreatment and bioextrusion steps in the twin-screw extruder (Fig. 9). After pretreatment, the fiber structure appears much wrinkled (Fig. 9b), showing that the fiber surface has released its extractable compounds, and a part of the hemicelluloses and lignin. This extraction of hemicelluloses and lignin is clearing the cellulose microfibrils on the fiber surface.

Following bioextrusion, the action of enzymes in the twin-screw extruder is visible on the surface of the fibers (Fig. 9c), and they appear to be damaged. The enzymes attack the surface of the fiber giving a "peeling" effect caused by a partial depolymerization of the cellulose.

Modification of the degree of polymerization (DP) of cellulose

In the paper industry, the degree of polymerization of the cellulose is conventionally evaluated by measuring the viscosity after t = measurement duration (s); V ′ = intrinsic viscosity (dm3 kg -1 ); V = dynamic viscosity (mPa s). However, this method, designed for pure cellulose fibers, is not intended to be applied directly to complex materials as is the case for sweet corn coproducts. To minimize interference with the non-cellulosic compounds, materials obtained at each step were purified to isolate their holocellulosic fraction and solubilize the water-solubles and the lignin. The protocol for measuring the viscosity directly in the cupriethylene diamine, applied to holocellulosic fractions of raw material, pretreated extrudates and the bioextrudates, revealed that the viscosity of the solutions increase sharply with the pre-treatment and then gradually decrease with the number of bioextrusion passages (Fig. 10). During pretreatment in the twin-screw extruder, a proportion of hemicelluloses is dissolved, whereas cellulose is not affected, and this proportion of hemicelluloses to cellulose decreases. Nonetheless, hemicelluloses are characterised by a significantly lower degree of polymerisation than cellulose (3-20 times lower) [START_REF] Klemm | Cellulose: fascinating biopolymer and sustainable raw material[END_REF]Girio et al., 2010). This results in a higher value of DP for pretreated extrudates compared to raw matter, and explains the higher viscosity measured for the holocellulose from the pretreated biomass. Therefore the decrease in measured viscosity observed with bioextrusion, essentially reflects a shortening of the cellulose chains and confirms the enzyme action during bioextrusion.

Conclusion

The implementation of a new process of deconstruction of annual plant material in a twin-screw extruder is described using sweet corn co-products. The choice of the profile is explained. The process has been investigated to better understand enzymatic action in the bioextrusion step, and the results obtained in this study prove the hydrolytic action of the enzymes in the extruder.

The material becomes sticky and less and less textured with successive passages through the extruder. Its rheological properties change and it gradually liquefies. The proportion of sugar released increases with the number of passages.

Oligosaccharides with a low degree of polymerization (DP <6-7) were released during the bioextrusion. The content of hot water soluble components measured in the bioextrudates, increases.

Partial depolymerization of the cellulose induced a "peeling" on the fiber surface.

The enzyme action during bioextrusion was confirmed by a decrease in measured viscosity of the solubilized bioextrudate in cupriethylenediamine, reflecting in fact a shortening of the cellulose chains.

These results prove that enzymes are active in the extruder and that the process of deconstruction of annual plant material using this apparatus is highly advantageous and could be a very attractive pre-treatment for the production of bioethanol. It enables working with a high consistency, in a high shear environment, over a short time, and initiating enzymatic hydrolysis without excessive dilution. In a subsequent study, the process will be adapted and optimized in one continuous step.

Fig. 1 .

 1 Fig. 1. Schematic representation of the sequences of the process carried out in two successive twin screw extruders with recirculation of the bioextrudate in the second twin screw extruder with or without a filtration module.

Fig. 2 .

 2 Fig. 2. Screw configuration for the combined process of pretreatment and bioextrusion with or without liquid/solid separation zone. (T2F = trapezoidal double-thread screw; C2F = conveying double-thread screw; BB = bilobe paddle screw; CF2C = reversed double-thread screw. The numbers following the type of the screw indicate the pitch of T2F, C2F, and CF2C screws or the angle between two successive elements in case of BB screws).

Fig 1 .

 1 Screw configuration for the combined process of pretreatment and bioextrusion is shown in Fig 2.After alkaline pretreatment and neutralization in twin-screw extruder BC 45, the extrudate was stored frozen and defrosted just before being introduced into BC 21 for the bioextrusion.

Fig. 3 .

 3 Fig. 3. Residence time distribution (RTD) of the material in the BC21 twin-screw extruder equipped with profiles B3 and B4.

  Enzymatic hydrolysability kinetics of the extrudate produced by profile B3 and B4.

Fig. 5 .

 5 Fig. 5. Sugar released in bioextrudate after 1, 3, 5 or 7 passages through the twin screw extruder with only thermomechanical effects (TM) or with thermomechanical effects coupled to enzymatic action (TME), compared with biomass treated in a stirred reactor in the presence of advanced enzymes, 4.6%/ms, consistency of 30%, 70 min, T: 50 • C (control). TMO corresponds to the alkalin pretreated coproduct of sweet corn.

Fig. 6 .

 6 Fig. 6. HPAE-PAD analysis of oligosaccharides released into the filtrate in the seventh cycle of bioextrusion compared with the maltodextrine standard.

Fig. 7 .

 7 Fig. 7. Fine particles produced by extrusion and bioextrusion in single and multipass mode.

Fig. 8 .

 8 Fig. 8. Hot water solubles after extrusion and bioextrusion in single and multipass mode.

Fig. 9 .

 9 Fig. 9. Scanning electron microscopy (SEM) of fiber observed in case of raw sweet corn coproduct (a), of alkaline pretreated sweet corn coproduct (b), of bioextruded sweet corn coproduct (c).

Fig. 10 .

 10 Fig. 10. Evolution of the dynamic viscosity of the sweet corn co-products at different stages of treatment.

Table 1

 1 Operating conditions.

	Profile	Constraintelements	SS	T	NaOH/DM	H3PO4/DM	pHex	L/S	FR	I	Stability
			(rpm)	( • C)	(%)	(%)		(kg h -1 /kg h -1 )		(A)
	A 1	CF2C -25	110	100	32.4	36.6	7.0	10.3	0.06	9	Instability of dynamic plug
			100	100	10.7	13.1	6.5	3.6	0.20	23	Instability
											filtration maintained but
											irregular
			110	100	9.4	11.0	6.0	7.6	0.08	17	Instability
											filtration maintained but
											irregular
	A 2	CF1C -25	110	100	10.5	11.8	6.0	8.2	0.07	17	Stable
			110	100	12.1	29.4	5.0	8.4	0.08	21	Stable
			110	100	18.6	28.0	5.5	8.1	0.07	14	Stable

Table 2

 2 Constraint elements and operating conditions for Bioextrusion step.

	Profile	Constraint elements	SS	T	Enzyme/DM	L/S	FR	Stability
			(rpm)	( • C)	(%)	(kg h -1 /kg h -1 )
	B 1	CF2C -33 (25)	280	50	4.0	2.5	0.01	Instabilityof dynamic plug
	B 2	CF2C -33 (50)	280	50	4.0	2.5	0.01	Instabilityof dynamic plug
	B 3	CF2C -16 (25)	200	50	4.1	2.5	0.01	Stable
	B 4	DM -45 • (5 × 5)	200	50	4.6	2.5	0.01	Stable
	CF2C = reversed double-thread screw, DM = monolobe paddle-screw, the length of the screws is indicated in brackets, FR = extruder filling ratio.

Table 3

 3 Operating conditions for alkaline pre-treatment and bioextrusion.

	Alkaline pretreatment		Bioextrusion
	SS (rpm)	110	SS (rpm)	200
	T ( • C)	100	T ( • C)	50
	QS (Kg/h)	8.8	QS (Kg/h)	3.29
	DMs (%)	90	DMs (%)	39.6
	Q LAlk (Kg/h)	20.7	QT+Ez (kg/h)	1.95
	QNaOH (Kg/h)	0.8	QEz (g/h)	60
	Q LAc (Kg/h)	51.6
	QH3PO4 (Kg/h)	0.9
	NaOH/DM (%)	10.5	Enzyme/DM (%)	4.6
	H3PO4/DM (%)	11.7
	L/S (kg.h -1 /kg.h -1 )	7.6	L/S (kg h -1 /kg h -1 )	2.5
	Qext (kg/h)	16.5	Q bioext (kg/h)	5.29
	DMext	40.7	DM bioext	29.0
	Q fil (kg/h)	62.4
	DM fil	4.7
	pHext	6
	pH fil	5.5
	I (A)	17
	SME (Wh/kg)	141

Where SS is the screw rotation speed (rpm); T is the set temperature of the heating modules ( • C); Qs and DMS are the feed rate (kg/h) and dry matter (%) of sweet corn; Q LAlk and Q LAc are respectively the inlet flow rate of alkali solution (kg/h) and acid solution (kg/h); QNaOH and QH3PO4 are the inlet flow of NaOH and H3O4; NaOH/DM and H3PO4/DM are the reagent to sweet corn dry matter ratio (%); L/S is the liquid/solid ratio; Qext, Q bioext and Q fil are, respectively, the flow rate of extrudate, bioextrudate and filtrate (kg/h); DMext, DM bioext and DM fil are, respectively, the dry matter of extrudate, bioextrudate and filtrate. I is the current feeding the motor (A); SME is the specific mechanical energy consumed by the motor per unit weight of solid matter (Wh/kg).

Table 4

 4 Composition and enzymatic hydrolysability of the pretreated coproduct of sweet corn.

	Composition
	DM (%)	40,7
	MM (%/DM)	7,3
	OM (%/DM)	92,7
	Hot water soluble (%/DM)	31,4
	Cellulose (%/DM)	46,4
	Hemicelluloses (%/DM)	28,8
	Lignin (%/DM)	4,8
	Glucose (%/DM)	54,4
	Xylose (%/DM)	22,3
	Arabinose (%/DM)	3,0
	Galactose (%/DM)	0,7
	Mannose (%/DM)	0,1
	Enzymatic hydrolysability
	(%/DM)	35

DM = dry matter; MM = mineral matter; OM = organic matter.

Acknowledgements

This work has been partially funded by the European Community (Seventh Framework Programme under Grant agreement reference n • 227498 (BABETHANOL PROJECT)). The authors would like to thank the staff from VTT for the hydrolysability analyses.


Lucas Royer

Maud Marchal

Anthony Le Bras

Guillaume Dardenne

Alexandre Krupa

Real-time Tracking of Deformable Target in 3D Ultrasound Images

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Real-time Tracking of Deformable Target in 3D Ultrasound Images

Lucas Royer1,2,4 , Maud Marchal 1,2 , Anthony Le Bras 1,3 , Guillaume Dardenne 1 , Alexandre Krupa 1,4 Abstract-In this paper, we present a novel approach for tracking a deformable anatomical target within 3D ultrasound volumes. Our method is able to estimate deformations caused by the physiological motions of the patient. The displacements of moving structures are estimated from an intensity-based approach combined with a physically-based model and has therefore the advantage to be less sensitive to the image noise. Furthermore, our method does not use any fiducial marker and has real-time capabilities. The accuracy of our method is evaluated on real data acquired from an organic phantom. The validation is performed on different types of motions comprising rigid and non-rigid motions. Thus, our approach opens novel possibilities for computer-assisted interventions where deformable organs are involved.

I. INTRODUCTION

In intra-operative interventions such as biopsy, it is often desirable to follow the motions of deformable structures in order to adjust the needle position under image guidance. These applications are generally performed under ultrasound (US) guidance as it produces real-time visual feedback of soft tissue deformations. Furthermore, US imaging provides low-cost, portable and non-invasive imaging, since it is acquired from a non-cumbersome transducer and a nonionizing energy. However, such systems suffer from different shortcomings, including the field of view often limited by small acoustic windows. Additionally, the quality of US images is often affected by speckle noise, artifacts, and shadows that makes soft-tissue tracking challenging. These limitations generally prevent from using classical tracking approaches proposed for other image modalities. To overcome the previous limitations, we propose a novel approach that combines a physically-based model with dense dissimilarity minimization in successive 3D ultrasound images. Moreover, we derive an adaption of our method that takes into account the US volume geometry. This allows to track only partially visible target and to propagate the deformation to structures that are not within the field of view. Our approach has the advantage of being real-time and robust to the noise. Furthermore, it provides an accurate estimation of target deformation and does not use any fiducial marker. The rest of the paper is organized as follows. In section II, related work combining visual tracking and physical model are presented. In section III, we detail our intensitybased approach combined with a physically-based model. In section IV, we present results on real data that demonstrate the performance of our method. Finally, section V concludes the paper.

II. RELATED WORK

Several techniques have been presented in order to extract the motions of a deformable target within ultrasound images. They can be classified in three types. Some of them are based on block-matching methods where local displacements are computed by minimizing image dissimilarity between consecutive frames. In the literature, typical examples using Sum of Squared Differences (SSD) [START_REF] Yeung | Feature-adaptive motion tracking of ultrasound image sequences using a deformable mesh[END_REF] [2] or Cross-correlation [START_REF] Compas | Combining shape and speckle tracking for deformation analysis in echocardiography using radial basis functions[END_REF] have been proposed. However, these methods assume that the displacement is the same within a local region block. Other approaches are based on the extraction of contours which represent the interfaces of soft tissues [START_REF] Peng | 2D Ultrasound Image Processing in Identifying Responses of Urogenital Structures to Pelvic Floor Muscle Activity[END_REF]. Nevertheless, these methods are generally based on a Bayesian framework in order to decrease the noise sensitivity. Angelova and Mihaylova [START_REF] Angelova | Contour segmentation in 2D ultrasound medical images with particle filtering[END_REF] developed a method that detects contours over the time where prior dynamics are considered. These techniques assume that strong edges are always visible within US sequence. Other approaches rely on the estimation of the optical-flow where dense displacement field is obtained from spatio-temporal derivatives of the pixels intensities inspired from the work of Lucas and Kanade [START_REF] Lucas | An iterative image registration technique with an application to stereo vision[END_REF]. These methods are based on the hypothesis of the constancy of the US wave reflection by a given physical structure over the time. Lee [START_REF] Lee | Intensity-based visual servoing for non-rigid motion compensation of soft tissue structures due to physiological motion using 4D ultrasound[END_REF] also provided a method based on thin-plate splines (TPS) for modeling the deformation. However, inaccurate results can be observed in practice due to the inability to fit high local deformation with TPS-based model. Mikic et al. [START_REF] Mikic | Segmentation and tracking in echocardiographic sequences: active contours guided by optical flow estimates[END_REF] presented a non-rigid target tracking method based on the optical flow which allows tracking a deformable target undergoing rigid displacement. Nevertheless, these methods may produce inaccurate results due to the image noise sensitivity. To cope with the previous issues, several works combining visual tracking and physical model have recently emerged. Some of them have been developed for others image modalities such as RGB-D camera [START_REF] Leizea | Real-time Deformation, Registration and Tracking of Solids Based on Physical Simulation[END_REF], stereo-camera [START_REF] Haouchine | Image-guided simulation of heterogeneous tissue deformation for augmented reality during hepatic surgery[END_REF], or magnetic resonance (MR) imaging [START_REF] Shen | Deformable Image Registration -A Critical Evaluation: Demons, B-Spline FFD and Spring Mass System[END_REF]. Few methods are based on principal component analysis (PCA) in order to generate a database of plausible shapes of the organ of interest as for example the prostate [START_REF] Hu | MR to ultrasound registration for image-guided prostate interventions[END_REF]. However, the PCA techniques are generally computationally expensive and are not suitable for real-time tracking. In contrast, several registration techniques driving directly the physically-based model without atlas generation have been developed. For example, Shen et al. [START_REF] Shen | Deformable Image Registration -A Critical Evaluation: Demons, B-Spline FFD and Spring Mass System[END_REF] proposed a registration method on MR imaging based on the mass-spring system in order to constrain the deformation. Zhang and al. [START_REF] Zhang | Model-based nonrigid image registration using scale-invariant features[END_REF] introduced another MRI registration based on finite element model where the extraction of the scale invariant features (SIFT), proposed in [START_REF] Lowe | Distinctive image features from scale-invariant keypoints[END_REF], is required to drive the model. However, the extraction of such points is not suitable in US images due to the speckle noise. Marami et al. [START_REF] Marami | Dynamic Tracking of a Deformable Tissue Based on 3D-2D MR-US Image Registration[END_REF] proposed very recently an elastic registration method applicable to multi-modality image registration. Their algorithm uses an estimation and a filtering process based on a linear elastic model of tissue deformation to automatically register the pre-operative 3D MR data to the intra-operative 2D US images. However, this approach does not focus on the 3D tracking of a specific target with appropriate elastic parameters and does not comply with the real-time constraint needed for robotic applications. To the best of our knowledge, no method combining realtime tracking and physically-based model within 3D US volume has been developed.

III. METHOD A. Model Generation

The main objective of our method is to estimate the position of a deformable target within 3D ultrasound sequences. The target represents a structure of interest containing N v voxels and is delimited by a visible surface in the US volume. The first step of our algorithm consists in extracting the target geometry by manually segmenting each slice of the first US volume of the sequence. The resulting surface is then smoothed in order to remove sharp edges, and a corresponding tetrahedral mesh is generated from it. The mesh is defined by a set of connected tetrahedral cells, each containing four vertices and a specific number of voxels. The number of vertices is defined as N c . The position of a voxel p = p x p y p z that lies inside a specific cell can be related to the positions of its four vertices q j by using barycentric coordinates. This can be expressed in the following relation:

p = M 1 M 2 M 3 M 4     q 1 q 2 q 3 q 4     (1)
where q i denotes the position of the vertice lying in the cell. M i represents a diagonal matrix of 3×3 elements containing the barycentric coefficients that relates the voxel p to the positions of the vertices q i . From the equation (1), we can link all the voxel positions lying in the mesh model with all the vertices by defining a (3 • N v) × 1 vector p im containing all the voxel positions, and a (3•N c)×1 vector q containing all the vertex positions.

p im = M.q (2)
where M is a constant matrix containing (3

• N v ) × (3 • N c )
elements. Each 3-line of M defines the set of barycentric coefficients that relates a voxel position with the positions of the vertices. Thanks to equation ( 2), we can update the positions of the target when the vertices of the model are displaced. These positions will be obtained from the variation of the inter-volume intensity and the mechanical behavior of the mass-spring model as we will explain in the following.

B. Intensity-based Approach

In this work, we assume that the US wave reflected by a physical point is constant and time independent. This ensures that the intensity of the target is constant across the time. This assumption can be expressed as:

I t (p im (t)) = I t0 (p im (t 0 )) (3)
where t 0 and t represent respectively the acquisition times of the first volume and the current volume. p im (t i ) denotes the voxel positions of the target at the time of acquisition of the volume i. I t denotes the US intensity of the volume acquired at time t. Thus, I t (p im (t)) is a column vector defining the intensity of the target at time index t. The objective is to find iteratively the optimal displacement ∆q of the vertices in order to minimize the cost function C which computes the least square of the intensity difference between I t (p im (t)) and I t0 (p im (t 0 )) such that:

C(q k (t)) = (I t (p im (t)) -I t0 (p im (t 0 )) 2 (4)
C(q k (t)) = (I t (M(q k-1 (t) + ∆q) -I t0 (M(q(t 0 )))) 2 (5)
where q k-1 (t) represents the estimation of the parameters at time t at iteration k -1 of the optimization algorithm. In order to compute the optimal displacement of the vertices, the cost function is linearized by using the first order Taylor approximation such that:

C(q k (t)) ≈ (J∆q + I t (M(q k-1 (t))) -I t0 (M(q(t 0 )))) 2 (6
) where J denotes the Jacobian matrix which links the variation of the intensity I of the voxels to the variation of the parameters ∆q. The intensity variation of the voxels can be linked to the variation of the voxel coordinates such that:

İ = ∇I. ṗim (7)
where ∇I represents the spatial derivatives of the voxel intensities regarding the x, y, and z axis and ṗim the displacements of voxels. In our case, the image gradient of voxel p is computed from 6 × 1 spatial derivative filters.

Combining equation [START_REF] Lee | Intensity-based visual servoing for non-rigid motion compensation of soft tissue structures due to physiological motion using 4D ultrasound[END_REF], with the time derivative of equation ( 2), the Jacobian can be expressed as:

J = ∇I.M (8)
Let us recall that our objective is to minimize the difference between the intensity vector of the reference target I t0 (p im (t 0 )) and current target I t (p im (t)). Several minimization strategies can be used in order to obtain the optimal displacements of the vertices. We chose to use the steepest gradient strategy instead of Gauss-Newton method as the pseudo-inverse of the Jacobian is highly computational demanding due to its size. We therefore obtain:

∆q = -αJ t [I t (M(q k-1 (t))) -I t0 (M(q(t 0 )))] (9)
where α > 0 denotes the step size of the steepest gradient method. This coefficient has an influence on the convergence rate of the difference between the current intensities I t (M(q k-1 (t))) and the initial ones I t0 (M(q(t 0 ))). J T represents the transpose matrix of the Jacobian J. However, straightforward minimization of the intensity variation yields to poor results in general due to possible local minima, in particular in presence of noise or partial object occlusion that are inherent to US images. In this work, we chose to address this problem by integrating a physically-based model of the expected deformation.

C. Deformable Component

In order to constrain the deformations of the target, a mechanical model is combined to the previous approach. It has the advantage to limit the noise sensitivity of the intensity-based approach. In this work, we assume that the deformation of the target is viscoelastic with a linear behavior. Our deformable model consists of a mass-springdamper system where the vertices of the model are linked with springs. In order to tune the behavior of the model, a specific value of mass can be set to each vertex. Furthermore, specific elastic and damping parameters can be added to each link and can constrain the deformation in a variety of ways. Thus, the force

f ij = [f xij f y ij f z ij ]
T exerted on a vertex q i from a neighbor vertex q j is expressed as follows:

f ij = K ij (d ij -d init ij )(q i -q j ) + D ij ( qi -qj ) • (q i -q j ) (10)
where d ij is a scalar value representing the distance between the vertices q i and q j in their current positions. d init ij denotes the initial distance value at the rest state of the deformable mass-spring. The • operator expresses the Hadamard product, K ij is a scalar value denoting the stiffness of the spring that links the two vertices while D ij is the damping coefficient value. The values of these coefficients can be different, depending on the homogeneities of the tissues. From the previous equation, we can compute the internal forces f i applied on each vertex q i of the deformable system such that:

f i = Ni n=0 f in + G i qi (11)
where G i is a scalar value representing the velocity damping coefficient associated to the vertex q i . N i denotes the number of springs that are connected to the vertex q i . Once the internal forces are computed from the previous equation, we integrate them with a semi-implicit Euler integration scheme for simulating our system in order to obtain the associated displacements. Then, we compute the optimal positions of the vertices combining the displacement obtained from the intensity-based approach with the internal displacements. This can be expressed as follows:

q(t) = q(t -1) + ∆q + ∆v (12)
where ∆v represents the internal displacements obtained from the simulation of the model described above. The external displacements ∆q are obtained from the intensitybased approach presented in equation ( 9), where the α coefficient (step size of the steepest gradient method) also acts as the balance between the internal forces and the external displacements. The original method described previously is well-suited if the target is completely visible within US volumes. However, it is likely that the target becomes partially invisible due to the limited field of view and large motions that can be applied to the probe. To cope with these issues, we adapted the intensity-based approach by taking into account the US volume geometry. To do so, we use the prescan volume instead of the 3D complete postscan volume. A prescan volume corresponds to the raw data acquired from the ultrasound probe, and is obtained by putting together each US scan-line. As it is illustrated in Figure 1, the prescan volumes are represented by a set of rectangular frames, and have therefore the advantage to present a simple geometry. However, since our transducer has a curved shape, prescan volumes are not geometrically correct as it does not show the exact position of the tissue being imaged. Thus, to adapt the intensity-based approach, a conversion scan step ψ, described in [START_REF] Lee | Intensity-based visual servoing for non-rigid motion compensation of soft tissue structures due to physiological motion using 4D ultrasound[END_REF], is required in order to rearrange the prescan data. ψ is the transformation which maps a point p of the prescan volume into the point p in the rearranged prescan volume. Equation ( 4) can now be expressed as:

D. Prescan data adaptation

C(q k t ) = (I t (ψ(p im (t))) -I t0 (ψ(p im (t 0 ))) 2 (13)
where p im represents the voxel positions of the target in prescan data. I t and I t0 denote the rearranged prescan US intensity volume at time t and t 0 . However, as we can see from Figure 1, several voxel positions do not have an associated intensity value in the prescan volume since the rearranged prescan data is not a continuous set of voxels.

To overcome this limitation, we implemented a method that interpolates the unknown intensity of a point p by using the inverse distance weighting (IDW) interpolation of neighboring points. It can be expressed as follows:

Ĩ(p) = Nn v=0 γ v (p)I(p i ) Nn v=0 γ v (p) (14)
where Ĩ(p) represents the unknown intensity value associated to the point p. p i denotes the set of neighbors of p that have an associated intensity value, N n expresses the number of neighbors points. γ v is the distance inverse between p and its neighbors p i . This distance is evaluated from rearranged prescan data which presents a correct geometry. To adapt the method described in the previous section, we need also to design a new spatial derivative filter adapted to prescan data. As previously stated, the image gradient of a voxel p is computed from 6 × 1 filters and takes into account the intensity value of nearest neighbors.Thus, by using the IDW interpolation technique, the image gradient of a voxel p in prescan frame can be expressed as :

∆I(ψ(p )) = ∆I(p) = 3 j=-3 w j Ĩ(p j ) (15)
where p represents a point in the prescan volume. w j depicts the filter coefficients, and p j represents the nearest neighbors of p within rearranged prescan volume. When the target goes out of the field of view, our approach does not take into account the points p that are outside of the prescan volume. This can be performed by setting their derivative value to zero. The rest of the method remains unchanged. This adaptation allows to take into account the US volume geometry. However, for this purpose, it should be mentioned that we can also use another method that consists in generating a mask on postscan data. .

IV. RESULTS

The ultrasound volumes are obtained from an Ultrasonix station thanks to a motorized probe (4DC7-3/40) which allows to acquire volumes containing 44 frames per volume with an angular step of 1.4 • . Thus, the US field of view is equal to 64.4 • and its associated volume rate is 0.8 vol/s. Since our US platform is designed for the purpose of research work, we have access to the prescan data as well as reconstructed postscan data. From this system, we can retrieve postscan and prescan volumes that consists respectively of 129 × 189 × 135 voxels and 128 × 120 × 44 voxels, and their associated depth is 15 cm. The size of a voxel represents a cube with 1.33 mm length sides. Our tracking system achieves real-time capabilities, since the computation time of 350 ms per 3D US frame is much lower than the acquisition time. It has been built with C++/GPU code by using Cuda [START_REF] Nickolls | Scalable parallel programming with CUDA[END_REF] and VTK [START_REF] Schroeder | The visualization toolkit: an object-oriented approach to 3D graphics[END_REF] libraries. The preprocessing steps described previously and including the segmentation and the model generation are performed respectively with the ITK-SNAP software [START_REF] Yushkevich | User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability[END_REF], and the tetGen software [START_REF] Si | TetGen, a Delaunay-Based Quality Tetrahedral Mesh Generator[END_REF]. In order to demonstrate the performance of our method, several experiments including both translation and deformation motions have been performed by using a home-made phantom which contains animal soft tissue (duck gizzard) and gelatin. As it can be seen in Fig. 2, the US probe is mounted on the end effector of a 6 Degrees of Freedom (DOF) robot (Viper 650, Adept). The probe is displaced on the surface of the phantom by applying different trajectories to the robot. We evaluated the performance of our tracking system by measuring the robot motion regarding the x, y and z axis with odometry. The tracking task is then performed on a target representing a convex surface visible within the US volume that can be delimited in the first US volume of the sequence with a manual segmentation as shown in Fig. 2. In the following experiments, we set empirically the elastic and damping parameters to K ij = 2.5, D ij = 0.1 for all the springs, and G i = 2.0 for all vertices. An interesting approach could consists in evaluating these parameters from elastography. The step size of the steepest gradient method has been set to α = 2 × 10 -6 .

A. Experiments With Rigid Motion

At first, we evaluated the tracking task with a lateral translation motion where the robot makes the probe follow a square trajectory on the phantom surface. In this experiment, the probe is displaced along the y and z-axis and no deformation has been involved. Thus, the target volume is constant over the time and is not compressed. In order to validate our method, we compared our results to the approach without the mass-spring (MS) system by removing the ∆v term of equation [START_REF] Hu | MR to ultrasound registration for image-guided prostate interventions[END_REF]. Fig. 4 shows the comparison between the displacement estimation of the target obtained by computing the barycenter position of our model at each 3D US frame. Our results are compared to the displacement estimated from odometry. In Fig. 4, we observed that our method achieves a low tracking error of (1.23 mm±0.03 mm) compared to the method without mass-spring system (5.25 mm±0.42 mm). Thus, our approach provides subvoxel accuracy since this error is lower than the size of a voxel. Fig. 3 presents the evolution of the model volume in the US sequence. This is computed by summing the volume of each tetrahedral cell. We noticed that the estimated volume is constant with our method over each 3D US frame contrary to the method without MS system where the volume varies significantly due to the image noise. We can also see that our method produces a smaller final intensity error given by equation ( 4). An example of the tracking task during a probe rotation motion along the x-axis is presented in Fig. 5(a-e).

B. Experiment With Deformation

In the second experiment, we evaluated the response of the tracking task when the target is deformed. To do so, we repeatedly compressed and released the deformable phantom with the probe mounted on the robot. In this case, the deformation has been applied by moving the probe along the x-axis thanks to the robot. This produces significant and periodic variation of the target volume. The estimated displacement of the target is shown in the Fig. 6. Contrary to the previous experiment, we can observe that the barycenter displacement does not exactly match the displacement obtained from the robot odometry. This difference is due to the compression of both the target and neighborhood gelatin. This attenuates the barycenter displacement compared to the motion measured by odometry. The Fig. 6 shows that the estimation of the volume varies periodically over the time with our method. Due to the noise sensitivity, we can observe that without the combination of a physically-based model, the volume varies unsteadily and even overcomes the initial volume of the target. An example of the tracking task with a deformation is presented in Fig. 5(f-j).

C. Experiment With Partially Visible Target

We also evaluated the performance of our method when the target becomes only partially visible during the sequence. To do so, the robot followed a large square trajectory as well as an elevation motion which deformed the target. In this experiment, we compared the method applied on prescan data and the original method applied on postscan data. Fig. 7 shows the displacements of the model barycenter and the comparison with odometry measurement. We observed that the prescan adaption improves the tracking accuracy (1.94 mm±0.18 mm instead of 3.25 mm± 0.20 mm with postscan data) when the target goes out of the field of view. Fig. 5(ko) shows an example of the tracking task on frames showing a slice of target which is displaced to the right and goes out of the field of view. We noticed that, with the postscan method, our model does not have a plausible shape when the target is not within the field of view (see Fig. 5(m). This can be also visualized in Fig. 8 from which the volume of the model is abnormally reduced when the target goes out of the field of view at frame 20. The postscan method remains however robust since the model returns to its initial shape when the target becomes visible again during its return to the initial position shown in Fig. 5(n). We can also observe that the prescan method shows a stable evolution of the volume (see Figures 5678). V. CONCLUSION In this paper, we have developed a real-time approach for tracking a deformable target combined with a physical-based model. This method has therefore the advantage to be robust to speckle noise. Additionally, our approach allows tracking of partially visible target within 3D ultrasound volumes. The performance of our algorithm has been validated on realdata acquired from organic phantom, and has been evaluated with visual assessment combined with robotic odometry ground truth. Thus, our method opens novel perspectives in computer-assisted interventions based on US imaging where deformable organs are involved, such as image-guided needle biopsy or ablation therapies. In future work, we will propose to enhance the physical model in order to improve the tracking accuracy. Furthermore, our approach will be validated on real data obtained from patients.

Fig. 1 .

 1 Fig. 1. (Left) Prescan Data. (Right) Rearranged Prescan Data

Fig. 2 .

 2 Fig. 2. (Left) Experiment setup. (Right) 3D slices of the ulltrasound volume representing the target and the tetrahedral mesh model (148 cells, 60 vertices).

Fig. 3 .

 3 Fig. 3. Results obtained during lateral translation motion. (Left) Volume estimation of the target. (Right) Least square intensity error.

Fig. 4 .Fig. 5 .

 45 Fig. 4. Results obtained during lateral translation motion. (Left) Displacement of the target regarding the Y-axis over the time. (Middle) Displacement of the target regarding the Z-axis over the time. (Right) 3D Displacement of the target

Fig. 6 .

 6 Fig. 6. Results obtained during deformation motion. (Left) Displacement of the target regarding the X-axis over each frame. (Right) Volume estimation of the model over each frame

Fig. 7 .

 7 Fig. 7. Results obtained with partially visible target. (Left) Displacement of the target regarding the Y-axis over the time. (Middle) Displacement of the target regarding the Z-axis over the time. (Right) 3D Displacement of the target

Fig. 8 .

 8 Fig. 8. Results obtained with partially visible target. Comparison of the volume estimation of the target with the prescan method and with the postscan method

Lucas Royer, Maud Marchal, Anthony Le Bras, Guillaume Dardenne and Alexandre Krupa are with Institut de Recherche Technologique b-com, Rennes, France.

Maud Marchal and Lucas Royer are with INSA de Rennes, France.

Anthony Le Bras is with CHU de Rennes, France.

Alexandre Krupa and Lucas Royer are with Inria Rennes -Bretagne Atlantique.