domain: spi phys chim

text:
Pierre Serrano
email: pierre.serrano@onera.fr

Louise Toualbi

Pascale Kanoute

Alain Couret

Cyclic deformation of TiAl generic microstructures at room and high temperature: Bauschinger effect & strain rate sensitivity

Keywords: A. Intermetallics (aluminides), B. Cyclic plasticity, B. Plastic deformation mechanisms, D. Microstructure, E. Yield behavior, F. Mechanical testing

Introduction

Technological advances in aircraft engine design require the use of lightweight materials at increasingly high temperatures. Therefore, intermetallics titanium aluminide alloys based on γ-TiAl have been introduced in the most recent civil turbo-engines as low pressure turbine blades [START_REF] Bp Bewlay | TiAl alloys in commercial aircraft engines[END_REF]. To extend the use of this material to other application technologies, new alloys are being developed with enhanced mechanical properties [START_REF] Kim | Advances in gammalloy materialsprocesses-application technology: successes, dilemmas, and future[END_REF].

Recently, it has been shown that a significant Bauschinger effect occurs at room temperature on TiAl alloy upon strain reversal loading [START_REF] Paul | On the Bauschinger effect in TiAl alloys[END_REF]. Even if this effect has been mentioned in some works [START_REF] Nakano | Anomalous behaviour of cyclic deformation and fatigue properties of TiAl PST crystals under constant applied stress[END_REF][START_REF] Gloanec | Low-cycle fatigue and deformation substructures in an engineering TiAl alloy[END_REF], few hysteresis loops analysis are available. It can even be stated that the link between the microstructure and the mechanical behavior is mostly understood upon uniaxial and monotonic loadings [START_REF] Kim | Progress in the understanding of gamma titanium aluminides[END_REF][START_REF] Appel | Gamma titanium aluminide alloys: science and technology[END_REF][START_REF] Brotzu | Mechanical properties of a TiAl-based alloy at room and high temperatures[END_REF][START_REF] Hee | Effect of microstructure on the hightemperature deformation behavior of Ti-48Al-2W intermetallic compounds[END_REF]. Indeed, the thermomechanical behavior of TiAl alloys has been studied only on already optimized microstructures with balanced properties [START_REF] El-Chaikh | Thermomechanical fatigue of titanium aluminides[END_REF][START_REF] Brookes | Axialtorsional thermomechanical fatigue of a near-γ TiAl-alloy[END_REF][START_REF] Appel | Electron microscope characterization of low cycle fatigue in a high-strength multiphase titanium aluminide alloy[END_REF].

In [START_REF] Satoh | Cyclic deformation and fatigue in TiAl intermetallic compound under plastic strain control[END_REF], a comparison is made between a near-γ microstructure and a lamellar microstructure but tests are only performed at room temperature. Other works discuss the cyclic behavior but only by means of the stress or strain amplitude evolution [START_REF] Berteaux | Microstructure-low cycle fatigue behaviour relationships in a pm γ-TiAl alloy[END_REF][START_REF] Gloanec | Low-cycle fatigue and deformation substructures in an engineering TiAl alloy[END_REF][START_REF] Malakondaiah | High-temperature low-cycle fatigue of a gamma titanium aluminide alloy Ti-46Al-2Nb-2Cr[END_REF][START_REF] Hénaff | Fatigue properties of TiAl alloys[END_REF][START_REF] Kruml | Microstructure degradation in high temperature fatigue of TiAl alloy[END_REF]. On cited works, the Bauschinger effect is not studied.

Hysteresis loop analysis give additional information on the link between the macroscopic behavior and microstructural mechanisms [START_REF] Fournier | Analysis of the hysteresis loops of a martensitic steel: Part i: Study of the influence of strain amplitude and temperature under pure fatigue loadings using an enhanced stress partitioning method[END_REF]. Moreover, such results are extremely valuable to develop refined plasticity theory [START_REF] David | Microstructure-sensitive computational structureproperty relations in materials design[END_REF][START_REF] Smith | Cyclic plasticity experiments and polycrystal plasticity modeling of three distinct ti alloy microstructures[END_REF].

In order to develop models that are able to describe the material behavior under the conditions seen by low pressure turbine blade, the cyclic behavior must be known over a range of various strain ranges and strain rates [START_REF] Coudon | A stochastic approach applied to directionally solidified turbine blades[END_REF]. To that attend, it has been shown that only a single multilevel and multirate cyclic test can be used [START_REF] Chaboche | Viscoplastic constitutive equations of combustion chamber materials including cyclic hardening and dynamic strain aging[END_REF].

The aim of this work is to study the link between TiAl microstructures and their corresponding mechanical behavior. The four generic microstructures of TiAl alloys are studied to correlate the macroscopic behavior to the corresponding microstructural mechanisms. Emphasis is made on the Bauschinger effect, but strain rate sensitivity is also considered so that the results can directly be used to develop advanced microstructure-sensitive models.

Material and methods

Microstructures

The material used is the G4 alloy (Ti-47Al-1Re-1W-0.2Si (at.%)) developed at the Onera and obtained via the powder metallurgy route [START_REF] Grange | Influence of microstructure on tensile and creep properties of a new castable TiAl-based alloy[END_REF]. The ingots have been casted at Howmet Casting facilities and the powder produced by argon gas atomization at Crucible Industries. Hot Isostatic Pressing (HIP) was performed by Bodycote at 140 MPa for 4h at 1250 • C. The chemical analysis of the powder compacts is given in Table 1. Cylindrical blank were machined from the powder compacts. Heat treatments were directly applied on those specimens to control the microstructure.

Temperature induced porosity (TIP) is observed after the treatments because of gas entrapped inside the powder after the atomization. The same observations as in [START_REF] Wegmann | Temperature induced porosity in hot isostatically pressed gamma titanium aluminide alloy powders[END_REF] were made : the occurence of TIP after temperature annealing depends on the grain size and the annealing time. TIP reduces drastically fatigue lifetime. However, in our case they have no impact on the macroscopic mechanical behavior and the hysteresis loop shape because of their low volume fraction (< 1 % for all microstructures) [START_REF] Chawla | Microstructure and mechanical behavior of porous sintered steels[END_REF].

For microscopic observations, samples were ground with a series of emery papers, then polished with colloidal silica paste abrasives down to 0.25 µm.

Microstructures were examined with a scanning electron microscope (SEM) by back scattered electron images (BSE). Microstructural parameters were quantified by image segmentation using ImageJ, a Java-based image processing program [START_REF] Schneider | Nih image to ImageJ: 25 years of image analysis[END_REF]. The apparent surface of grains was measured. The grain size is taken as the equivalent diameter of the measured shape. Series of micrographs were examined to ensure that the observations were statistically representatives. The lamellae spacing was measured following the methodology proposed in [START_REF] Berteaux | Etude des mécanismes d'écrouissage et d'endommagement cycliques des alliages TiAl élaborés par métallurgie des poudres[END_REF] for two types of cooling heat treatments: furnace and air cooling. The sample was cut in half and the apparent lamellar spacing and angle were measured on both side after polishing. A stereological correction is then applied to access the real lamellar spacing. Again, series of measurements were made to ensure that the observations were statistically representatives.

SEM images of the studied microstructures are shown in Fig. 1. The post-HIP microstructure is a near-γ microstructure (Fig. 1a) that contains mainly small equiaxed γ grains with a mean size of 5 µm. Smaller β 0 (∼ of 2 µm.

The duplex microstructure (Fig. 1b) is obtained following a heat treatment at 1340 • C during 4 hours followed by an air cooling. This microstructure contains equiaxed γ grains (∼ 5 µm) and lamellar colonies (∼ 15 µm). The grains are surrounded by borders consisting of γ phase and β 0 precipitates as observed in the IRIS alloy [START_REF] Voisin | Obtaining of a fine near-lamellar microstructure in TiAl alloys by spark plasma sintering[END_REF].

The near-lamellar microstructure (Fig. 1c) is obtained following a heat treatment at 1360 • C during 1 hour followed by an air cooling. This microstructure contains mainly relatively small lamellar colonies (∼ 90 %, ∼ 50 µm). Some equiaxed γ grains (∼ 10 %, ∼ 8 µm) are still present. All phases are surrounded by borders again.

Finally, the lamellar microstructure (Fig. 1d) is obtained following a heat treatment at 1370 given in Table 2.

It has to be noticed that getting accurate measurements of the TiAl microstructural attributes is difficult even by Electron BackScatter Diffraction (EBSD) because of the lamellae size and the pseudo-symmetry of the γ-phase.

For our case, statistical measurement performed by hand was find to be the most relevant method.

Mechanical testing

For mechanical testing, cylindrical specimens were machined with the geometry specified in Fig. 2. As described in [START_REF] Berteaux | Microstructure-low cycle fatigue behaviour relationships in a pm γ-TiAl alloy[END_REF], the size of the gauge section was optimized to ensure that it contains at least 250 grains for every investigated microstructure. Room temperature and high temperature tests at 750 • C were conducted in air atmosphere using a MTS servohydraulic machine. High temperature tests were performed using a furnace monitored by three thermocouples attached to the specimen. Extensometers attached directly to the gauge part of the specimens were used. Tests were performed in total mechanical strain control with a triangular waveform. Hence, the plastic strain rate is not constant [START_REF] Christ | The use of plastic strain control in thermomechanical fatigue testing[END_REF]. Nevertheless, this is the case in various works in which both the Bauschinger effect and strain rate sensitivity were investigated [START_REF] Paul | On the Bauschinger effect in TiAl alloys[END_REF][START_REF] Fournier | Analysis of the hysteresis loops of a martensitic steel: Part i: Study of the influence of strain amplitude and temperature under pure fatigue loadings using an enhanced stress partitioning method[END_REF][START_REF] Fournier | Analysis of the hysteresis loops of a martensitic steel: Part ii: study of the influence of creep and stress relaxation holding times on cyclic behaviour[END_REF]. As all the specimens undergo the same type of loading, we assume that it has a limited impact on the observed mechanical behaviors and the discussion [START_REF] Schallow | High-temperature fatigue behaviour of a second generation near-γ titanium aluminide sheet material under isothermal and thermomechanical conditions[END_REF].

At both room temperature and 750 • C, a tensile test was first performed.

Results were used to determine appropriate strain range level for each microstructure and to compare monotonic and cyclic behaviors. The strain rate at room temperature is ε = 10 -3 s -1 . At 750 • C, the viscous behavior was investigated by changing the strain rate to ε = 10 -5 s -1 during the test, then by stopping the test at ε = 0.8 % and performing a 8 hours relaxation to study the mechanical behavior upon very low strain rate.

Multilevel cyclic tests were performed in symmetric tension-compression cycle (R ε = -1) at ε = 10 -3 s -1 for room temperature tests and multiple strain rates for high temperature tests. Strain range levels were defined based on tensile test results. This type of cyclic tests highlights the major characteristics of the mechanical behavior with a limited number of specimens and is suited to the development of refined plasticity theories.

Bauschinger effect analysis

The mechanical behavior of material upon strain reversal loading is usually characterized by the Bauschinger effect, defined as a reduction of the yield stress upon load reversal [START_REF] Bauschinger | On the change of position of the elastic limit of iron and steel under cyclic variations of stress[END_REF]. A scheme showing the typical stress-strain behavior under strain controlled symmetric tension-compression tests is shown in Fig. 3. Quantities detailed below are measured and used to discuss the microstructure influence on this effect.

For tensile test and the first cycle of cyclic tests, the yield stress σ 0 and 0.2 % yield stress σ 0.2 are respectively defined as the lowest stress point at which permanent deformation can be measured and the stress at 0.2 % plastic strain. A Bauschinger effect is observed if the compressive yield stress σ c is lower than the yield stress such that |σ c | < |σ 0 |. This effect can be caused by different phenomena.

It has been stated that in the case of TiAl alloys, the Bauschinger effect is mainly caused by a significant transient softening [START_REF] Paul | On the Bauschinger effect in TiAl alloys[END_REF]. This transient softening can be seen as a decrease of the initial elastic domain ED 0 defined as ED 0 = 2σ 0 , that becomes ED C upon load reversal [START_REF] Pb Pragnell | Considerations in the use of yield asymmetries for the analysis of internal stresses in metal matrix composites[END_REF][START_REF] Pb Prangnell | An examination of the mean stress contribution to the Bauschinger effect by neutron diffraction[END_REF]. The transient softening can therefore be quantified as

T S OC = ED 0 -ED C .
Even in the case where ED 0 = ED C , a Bauschinger effect is still observed if there is kinematic hardening. This is defined as the displacement of the initial elastic domain center, the point A on the scheme, that becomes point B upon load reversal. This leads once again to a lower compressive yield

stress σ c as |σ c | < |σ 0 |.
Cyclic plasticity experiments with R ε = -1 can also reveal a tensioncompression asymmetry [START_REF] Smith | Cyclic plasticity experiments and polycrystal plasticity modeling of three distinct ti alloy microstructures[END_REF]. Such asymmetry is usually discussed by studying the difference between the stress at the maximal peak strain σ 1 and the stress at the minimal peak strain, σ 2 . Another interesting way to reveal a tensioncompression asymmetry is to compare the size of the elastic domain upon compression, ED C , to the size of the elastic domain upon tension, ED T .

It should be noted that the Bauschinger effect can also be caused by a permanent softening, which is defined by a loss of strength between the forward and reverse parts of the test [START_REF] Brown | The work-hardening of copper-silica v. equilibrium plastic relaxation by secondary dislocations[END_REF]. As it has already been shown that it does not happen in the case of TiAl alloys in [START_REF] Paul | On the Bauschinger effect in TiAl alloys[END_REF], it will not be mentioned thereafter.

Finally, the evolution of the elastic domains, the peak stresses and the hysteresis loop shapes over the course of the test are studied to get insight over a potential cyclic hardening or softening.

The size of the elastic domain is measured on the stabilized hysteresis loops by using Cottrell's method [START_REF] Howard | Dislocations and plastic flow in crystals[END_REF]. This method has been widely used and discussed elsewhere [START_REF] Fournier | Analysis of the hysteresis loops of a martensitic steel: Part i: Study of the influence of strain amplitude and temperature under pure fatigue loadings using an enhanced stress partitioning method[END_REF][START_REF] Kuhlmann | Dislocation behavior in fatigue ii. friction stress and back stress as inferred from an analysis of hysteresis loops[END_REF][START_REF] Feaugas | Grain-size effects on tensile behavior of nickel and AISI 316L stainless steel[END_REF][START_REF] Ji Dickson | A comparison of two simple methods for measuring cyclic internal and effective stresses[END_REF][START_REF] Xu | A study of the back stress and the friction stress behaviors of Ti-6Al-4V alloy during low cycle fatigue at room temperature[END_REF]. It consists in extracting the elastic part of the hysteresis loop by considering that it corresponds to all the data points located between two parallel lines. Those lines have a slope equal to Young's modulus and are shifted by a strain offset. This offset parameter is defined based on the sensitivity of the extensometer. In our case, we use

ε offset = (3 ± 2) × 10 -5
as in [START_REF] Haddou | The effects of grain size on the cyclic deformation behaviour of polycrystalline nickel[END_REF]. A scheme illustrating the technique is shown on the right part of Fig. 3. In some cases, the elasticity modulus can change during the test and has to be adjusted for the elastic part detection [START_REF] Dong | Characteristic cyclic plastic deformation in ZK60 magnesium alloy[END_REF]. This was not necessary in our study.

Results

Room temperature tests

Room temperature tensile properties are illustrated in Fig. For all microstructures, the mechanical behavior during the first strain range level is elastic as no permanent strain is observed and σ 0 is not reached.

The initial elastic domain is therefore clearly defined by ED 0 = 2σ 0 . Then, a typical Bauschinger effect is observed for all microstructure after the onset of plasticity. This effect is mainly caused by a significant transient softening (i.e. ED 0 is reduced to ED C and ED T (see section 2.3)) but a kinematic hardening is also observed. The stabilized hysteresis loop is immediately reached as only the level transition cycles (dashed lines on the figures) are different from the others. Almost no cyclic hardening or softening are observed for all microstructures and there is no modification of the loop shapes until failure. When comparing the near-lamellar and lamellar microstructures, it can observed that plasticity starts in both cases at ∆ε/2 = 0.2 % as the yield stress are the same, but that the hysteresis loop is more open for the lamellar microstructure.

To discuss the Bauschinger effect causes, the stress is plotted as a function of the plastic strain for the stabilized hysteresis loops corresponding to ∆ε/2 = 0.4 % in Fig. 6a. The kinematic hardening observed for the nearlamellar microstructure is more pronounced than the one of the lamellar microstructure. Indeed, for both microstructures, the initial elastic domains ED 0 and their associated centers are identical. But it can be observed in Regarding both lamellar microstructures, their elastic domain sizes and transient softenings are equals.

High temperature tests

Tensile tests at two strain rates followed by a 8 hours relaxation were room temperature to avoid early failure (see Fig. 7a).

The behavior is now ductile. A decrease of the mechanical properties (Young modulus, yield stresses) is observed compared to room temperature tests. Nevertheless, the microstructure strength hierarchy is the same, and the observations made regarding the plasticity and hardening behavior are still true. It is not possible to say if the bump on the near-γ microstructure has disappeared, as it might have occurred simultaneously with the first strain rate change.

The strain rate modification reveals the microstructure impact on the viscosity. The strain rate sensitivity is discussed through the stress gap that occurs during the strain rate modification. The near-γ microstructure presents the most viscosity effects, followed by the duplex. The near-lamellar and lamellar microstructures show almost no strain rate sensitivity. During the 8 hours relaxation, the mechanical behavior upon very low strain rate is revealed (Fig. 7b). The lamellar microstructure shows low relaxation whereas the near-lamellar has almost the same behavior as the duplex and near-γ microstructure. Therefore, a small amount of globular γ phase on a TiAl microstructure significantly modifies the low strain rate mechanical behavior.

Multilevel and multistrain cyclic tests are conducted in tension-compression

under strain controlled at 750 • C. At each strain amplitude, 64 cycles are performed at a strain rate ε = 10 -3 s -1 , 32 at ε = 10 -4 s -1 and 8 at ε = 10 -5 s -1 .

Successive strain amplitudes were defined to get enough data from each microstructure based on their room temperature and high temperature tensile test results to avoid early failure. The amplitudes are 0.4 %, 0.6 %, 0.8 % and 1 % for the near-γ and duplex microstructures. The amplitudes are 0.2 %, 0.4 %, 0.6 % and 0.8 % for the near-lamellar and lamellar microstructures.

Results are shown in

Discussion

The microstructure strength hierarchy observed here is similar to the one commonly observed at room temperature in the literature [START_REF] Kim | Progress in the understanding of gamma titanium aluminides[END_REF][START_REF] Couret | Microstructures and mechanical properties of TiAl alloys consolidated by spark plasma sintering[END_REF]. A classical Hall-Petch relationship between yield stress and the grain/colony size is followed as observed elsewhere [START_REF] Mr Kabir | Correlations between microstructure and room temperature tensile behavior of a duplex TNB alloy for systematically heat treated samples[END_REF]. Indeed, even if in the case of the fully lamellar microstructure the lamellae thickness is sub-micrometric, once the soft colonies are deformed the mean free path of the dislocation within the lamellae habit plane is very important because of the lamellar colony size. We also point out that the lamellae width affects the yield strength for microstructures with similar colony size [START_REF] Liu | Microstructural control and mechanical properties of dual-phase TiAl alloys[END_REF].

At room temperature and 750 • C and for strain rate in the range from 10 -3 s -1 to 10 -5 s -1 , plastic deformation in TiAl occurs by glide of ordinary dislocations and twinning [START_REF] Appel | Gamma titanium aluminide alloys: science and technology[END_REF][START_REF] Couret | Glide mechanism of ordinary dislocations in the γ phase of TiAl[END_REF]. At 750 • C, the deformation is eased by the activation of cross slip. Superdislocations are generally reported as a deformation mechanism for the γ phase. However, as they are almost never observed experimentally in conventional alloys and in the investigated alloy their contribution to the deformation is supposed very small [START_REF] Jabbar | Microstructures and deformation mechanisms of a G4 TiAl alloy produced by spark plasma sintering[END_REF][START_REF] Monchoux | Deformation modes and size effect in near-γ TiAl alloys[END_REF]. Therefore, they are not considered in the following discussion. Based on the experimental results, the mechanical behavior of TiAl microstructures can be classified in two categories.

The first type of behavior corresponds to microstrutures with a high γ phase volume fraction, in our case the near-γ and duplex. Their behavior is characterized by a steep entry into plasticity, with an initial bump for the near γ microstructure, followed by a pronounced transient softening upon reverse flow stress. The bump phenomena was linked to the activation of the first twinning systems in [START_REF] Kauffmann | On the origin of acoustic emission during room temperature compressive deformation of a γ-TiAl based alloy[END_REF][START_REF] Wt Marketz | Deformation mechanisms in TiAl intermetallics: experiments and modeling[END_REF]. It might be related to the fact that twin nucleation is more difficult than twin propagation [START_REF] Farenc | An in situ study of twin propagation in TiAl[END_REF]. Detailed TEM and neutron diffraction investigations during cyclic tests suggest that major twins are nucleated when the strain range increase, i.e. during the first cycle of the considered strain range level [START_REF] Beran | Complex investigation of deformation twinning in γ-TiAl by TEM and neutron diffraction[END_REF]. This twin nucleation during the first loading part creates interfaces. TEM investigations of TiAl deformation and dislocation dynamic simulations confirmed that those boundaries are obstacles to dislocation movement, and therefore back stress generators [START_REF] Zghal | Transmission of the deformation through γ-γ interfaces in a polysynthetically twinned TiAl alloy ii. twin interfaces (180 rotational)[END_REF][START_REF] Fan | The role of twinning deformation on the hardening response of polycrystalline magnesium from discrete dislocation dynamics simulations[END_REF]. Furthermore, it was observed on steel that this back stress does create a significant Bauschinger effect caused by transient softening [START_REF] Karaman | The effect of twinning and slip on the bauschinger effect of hadfield steel single crystals[END_REF].

Based on the extensive microstructural analysis available in the literature, the following scenario can be defined. Twins are created during the first loading part of cyclic tests, therefore creating new boundaries within grains and an important back stress when interacting with dislocations. Because the shortness of their Burgers vector and since only one twinning dislocation can propagate in each plane, twinning is able to bring only a moderate amount of strain. Hence, the subsequent strain is largely due to glide of ordinary dislocations, the movement of which being impacted by the twins forming obstacles to their propagation. During the compression strain application, partial back movement of twin dislocations, nucleation and propagation of new twinning systems compatible with the compression strain as well as glide of ordinary dislocations should be activated. These three kinds of movement are facilitated by the back stress created during the tensile strain, leading to the tension-compression asymmetry. As a result, a pronounced Bauschinger effect caused mainly by transient softening is observed during the compression part. In fact, if another test was performed by starting in compression first, we suspect that the asymmetry might change direction. When cycling at the same strain range, ordinary dislocations which are easily able to multiply and to accommodate both compression and tension strains propagate the applied deformation. The stress does not increase and therefore the twin network does not change drastically as twins thicken. Hence, the stabilized cycle is immediately reached. The same scenario occurs at high temperature. The bump is only less pronounced as deformation by ordinary disclocations is eased by the activation of cross slip.

The small cyclic hardening observed at room temperature is similar to the cyclic stress strain behavior that has been observed on an equiaxed near-γ microstructure in [START_REF] Al Gloanec | Cyclic deformation mechanisms in a gamma titanium aluminide alloy at room temperature[END_REF], but different from the one observed on cast material for which a significant hardening was observed [START_REF] Satoh | Cyclic deformation and fatigue in TiAl intermetallic compound under plastic strain control[END_REF]. That hardening is usually associated to the formation of a vein-like dislocation structure and twin-dislocation interactions [START_REF] Gloanec | Low-cycle fatigue and deformation substructures in an engineering TiAl alloy[END_REF]. The mechanisms responsible for the absence of cyclic hardening at high temperature are unclear but are most certainly related to the capability of dislocations to cross-slip, promoting the dislocation annihilation processes, thus limiting the hardening.

The second type of behavior corresponds to microstructures with a high volume fraction of lamellar colonies. This behavior is characterized by a smooth entry to plasticity. The two investigated microstructures exhibit the same yield stresses but different 0.2 % yield stresses. This can be explain by the important plastic anisotropy of lamellar colonies that has been revealed by studying lamellar single colonies [START_REF] Inui | Room-temperature tensile deformation of polysynthetically twinned (PST) crystals of TiAl[END_REF]. This anisotropy leads favorably oriented lamellar colonies to yield well before the macroscopic yield stress [START_REF] Edward | Deformation of lamellar γ-TiAl below the general yield stress[END_REF][START_REF] Botten | The significance of acoustic emission during stressing of TiAl-based alloys. part i: Detection of cracking during loading up in tension[END_REF]. Therefore when performing tests at the macroscopic scale, the grain size and lamellae size effects are only observed on the 0.2 % yield stresses following a Hall-Petch law. This is why in this work, from a macroscopic point of view, the microstructural attributes affect mostly the kinematic hardening and not the apparent yield stress.

During cyclic tests, a Bauschinger effect caused by transient softening is observed. The transient softening is far less pronounced than the one of microstructures with a high volume fraction of γ grains. Nevertheless, TEM observations suggest that the phenomena causing this softening are most certainly similar but less striking because of the multiscale aspect of lamellar microstructures [START_REF] Gloanec | Low-cycle fatigue and deformation substructures in an engineering TiAl alloy[END_REF]. As the deformation is constrained within lamellae, those phenomena are not highlighted during macroscopic tests.

Regarding the strain rate sensitivity, multirate tensile test results show that the microstructure sensitivity to viscosity effects increases with the γ grain volume fraction. The relaxation parts of the tests reveal that the mechanical behavior upon very low strain rate is drastically modified by adding a small volume fraction of γ phase. Overall, it thus appears that the strain rate sensitivity is closely related to the microstructure γ phase volume fraction. This is an important observation as near-lamellar microstructures that consist of lamellar colonies with a fraction of globular γ grains are often considered as the optimal microstructure in TiAl alloys because of their balanced mechanical properties [START_REF] Voisin | Obtaining of a fine near-lamellar microstructure in TiAl alloys by spark plasma sintering[END_REF][START_REF] Clemens | Design, processing, microstructure, properties, and applications of advanced intermetallic TiAl alloys[END_REF].

Conclusions

The mechanical behavior of four generic TiAl microstructures has been

Fig. 1 .

 1 Fig. 1. SEM micrographs of the generic microstructures studied in this work.

Fig. 2 .

 2 Fig. 2. Test specimen geometry.

Fig. 3 .

 3 Fig. 3. Cyclic stress-strain behavior scheme. σ 0 is the yield stress, σ 02 the 0.2% yield stress, σ 1 and σ 2 are the stresses respectively at the maximum and minimum peak strain, σ C and σ T are respectively the compressive and tensile yield strength during a fully reversed cycle. ED 0 , ED C and ED T are respectively, the initial elastic domain, the compressive elastic domain and the tensile elastic domain. Points A and B are the centers of their corresponding elastic domains. The right part of the scheme illustrates the detection technique of the linear part based on the use of an offset parameter ε offset .

  4. The ductility is limited regardless of the microstructure. The microstructures exhibit various behavior. The near-γ shows the highest stress state with an initial bump. The duplex microstructure shows a steep entry in plasticity and linear hardening. For the near-lamellar and the lamellar microstructure the elastic-plastic transition is smoother. Their yield stresses are equals, but differences in hardening led to different σ 0.2% . Multilevel cycle tests were designed to study the elastic-plastic transition by performing 5 cycles at ∆ε = 0.1 %, 5 cycles at ∆ε = 0.2 % and cycles up to specimen failure at ∆ε = 0.4 %. Results are shown in Fig. 5. Poor powder quality induced failure after a small number of cycles, but enough were performed to analyze the cyclic stress strain behavior.

Fig. 6a that

  Fig. 6a that at ∆ε/2 = 0.4 %, the elastic domain center of the near-lamellar microstructure is now at a higher position than the one of the lamellar microstructure. The elastic domain sizes can be easily measured from those hysteresis loops, as they correspond to the stress range for which no plastic strain occurs. The elastic domain sizes upon compressive and tensile stress (ED C and ED T ) are measured, and the transient softening is quantified as T S OC = ED 0 -ED C and T S OT = ED 0 -ED T . The evolution of the elastic domains size and transient softening as a function of the γ phase volume fraction are shown in Fig. 6b. The transient softening increases dramatically with the γ phase volume fraction. The comparison between the elastic domain sizes upon compressive and tensile stress reveals a pronounced tension-compression asymmetry for the two microstructures that contains a significant γ phase volume fraction.

  performed at 750 • C. The results are shown in Fig.7. The strain rate modification was made at ε = 0.4 % and ε = 0.6 % based on the results at

Fig. 4 .

 4 Fig. 4. Tensile curves at room temperature, ε = 10 -3 s -1 .

Fig. 5 .

 5 Fig. 5. Room temperature multilevel cyclic tests results, ε = 10 -3 s -1 .

Fig. 6 .

 6 Fig. 6. Bauschinger effect analysis at room temperature. NG, DP, NL and LM respectively stand for Near-γ, Duplex, Near-lamellar and Lamellar microstructure. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 8 .Fig. 7 .

 87 Fig. 7. High temperature tensile tests at two strain rates (a) follow by an 8 hours relaxation (b).

Fig. 8 .

 8 Fig. 8. Multilevel and multirate cyclic tests at 750 • C results. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

Fig. 9 .

 9 Fig. 9. Bauschinger effect analysis at 750 • C. NG, DP, NL and LM respectively stand for Near-γ, Duplex, Near-lamellar and Lamellar microstructure. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)

  studied at room and high temperature by means of mechanical testing, including cyclic plasticity experiments. The phenomena observed at the macroscopic scale are linked with their corresponding microscopic mechanisms. The mechanical behaviors can be classified in two categories. Microstructures containing an important volume fraction of γ grains exhibit a pronounced Bauschinger effect caused mainly by a spectacular transient softening. This sudden softening is attributed to the onset of twinning and the dislocation-twin interactions. At room temperature, those microstructures exhibit a tension-compression asymmetry during cyclic tests that manifests as different elastic domain sizes depending on the flow stress direction.Microstructure with a high volume fraction of lamellar colonies show little distinction between the elastic and plastic domains because of the multiscale aspect of the microstructure and the pronounced plastic anisotropy of lamellar colonies. A Bauschinger effect is also observed, partly caused by a transient softening that is far less significant than the one described above, but mostly due to a pronounced kinematic hardening. At the macroscopic scale, the impact of the lamellar microstructural attributes appears only on the kinematic hardening because of the multiscale aspect of the microstructure.At 750 • C, the microstructure strength hierarchy and the observed phenomena are the same than those observed at room temperature. Particularly, the transient softening levels are almost exactly similar to the ones observed at room temperature. The strain rate sensitivity is shown to increase with the globular γ phase volume fraction. The relaxation parts of the tests revealed that even a low amount of globular γ grains deeply modifies the mechanical behavior upon very low strain rate.To conclude, components made in TiAl alloys must be designed carefully as significant unwanted plasticity might occur upon strain reversal, particularly if the mechanical behavior is close to the one observed for the near-γ and duplex microstructures. The given experimental results open a fruitful opportunity for the microstructure-sensitive modeling of TiAl alloys mechanical behavior.Ongoing work focus on modeling the observed cyclic deformation by means of rate-dependent crystal plasticity and computational homogenization.

Table 1 .

 1 Chemical composition of the studied G4 alloy.

	Ti (at. %) Al (at. %) Re (at. %) W (at. %) Si (at. %) C (ppm) O (ppm)
	50.85	47.01	0.95	0.95	0.24	175	1240

Table 2 .

 2 Results of the microstructural characterization. VF stands for Volume Fraction, GS stands for Grain Size, LS stands for Lamellae Size.

Aknowledgement

The authors would like to thank our colleagues Camille Liard for performing the experiments and Marc Thomas for providing the material and helpful discussions.


E Mylona

Oscar Acosta

T Lizee

J Castelli

C Lafond

G Crehange

N Magne

S Chiavassa

S Supiot

R De Crevoisier

Bladder and urethra subregions predicting urinary toxicity after prostate cancer radiotherapy

published or not. The documents may come

PO-0853 Bladder and urethra subregions predicting urinary toxicity after prostate cancer radiotherapy E. Mylona 1 , O. Acosta 1 , T. Lizee 1 , J. Castelli 2 , C. Lafond 2 , G. Crehange 3 , N. Magne 4 , S. Chiavassa 5 , S. Supiot 5 , R. De Crevoisier

Purpose or Objective

To apply a voxel-based analysis on the planning 3D dose distribution in order to identify symptom-related subregions (SRSs) of the bladder/urethra associated with acute and late urinary toxicity in prostate cancer radiation therapy (RT).

Material and Methods

Overall, 272 prostate cancer patients treated with IMRT/IGRT from two multicentric prospective phase III trials were analyzed. Each patient's organ contours (bladder, urethra and prostate) were spatially normalized to a common coordinate system (CCS) via non-rigid registration. The obtained 3D deformation fields were used to propagate the planning dose distributions from the native space of each patient to the CCS. A voxel-based statistical analysis was applied to generate 3D dosevolume maps for different urinary symptoms and identify corresponding SRSs with statistically significant dose differences between patients with/without toxicity. All the identified SRSs were propagated from the CCS back to the native space of each individual and DVHs for the SRSs and the whole bladder were computed. Dose bins of significant dose difference between patients with/without urinary toxicity were identified. Logistic regression was used to estimate the DVH prediction capability (1Gy binwise) of the SRSs compared to the whole bladder.

Results

A local dose-effect relationship was found in the bladder and the urethra. SRSs of significant dose differences (p-value<0.01) were identified for four endpoints: acute and late incontinence in the urethra and the trigone, late retention and dysuria in the posterior part of the bladder, with average dose differences ranging from 1.26 to 9.28 Gy. Figure 2 shows these SRSs onthe template. The DVHs of the SRSs were significantly predictive of toxicity with maximum areas under the ROC curve (AUC): 71% for acute incontinence, 80% for late incontinence, 68% for late retention and 79% for late dysuria. The DVH of the bladder was predictive only for late incontinence and late dysuria (AUC=70%). Table 1 shows the prediction capability of the DVH for the SRSs and the whole bladder in the native space.

Conclusion

The dose delivered to the urethra, the trigone and the posterior region of the bladder was predictive of acute and late incontinence, late retention and late dysuria. These SRSs appear more predictive than the whole bladder, suggesting that identification of radiosensitive subregions can improve the prediction capabilities for urinary toxicity and may be spared in order to decrease urinary symptoms. Additionally, this study provides the first evidence that explicitly correlate the true 3D dose to the urethra with urinary toxicity following EBRT.

  StPriest en Jarez, France ; 5 Institut de Cancérologie de l'Ouest, Medical Physics Department, Nantes, France

1,2 1 INSERM U1099 -University of Rennes, Laboratoire Traitement du Signal et de l'Image, Rennes, France ; 2 Centre Eugène Marquis, Radiotherapy department, Rennes, France ; 3 Centre Georges François Leclerc, Radiotherapy department, Dijon, France ; 4 Lucien Neuwirth Cancer Institute, Radiotherapy department,


E Mylona

A Cicchetti

T Rancati

F Palorini

S Supiot

N Magné

G Crehange

O Acosta

R De Crevoisier

OC-0615 Predicting urinary toxicity via 2D and 3D dose map analyses in prostate cancer radiotherapy

-To evaluate their prediction capabilities with respect to the DVH of the whole bladder.

Material and Methods

In total 272 prostate cancer patients treated with IMRT/IGRT from two multicentric phase III trials (STIC-IGRT and PROFIT), were prospectively analyzed. Local relationships between dose and specific urinary endpoints were investigated via 2D DSMs and 3D DVMs, by analyzing the planning dose distribution at pixel and voxel scales, respectively. DSMs were generated by anteriorly cutting and virtually unfolding the bladder surface. Maps were laterally normalized and aligned at the most inferiorposterior point. Normalized DSMs (nDSM) both laterally and vertically were also computed. DVMs were produced by first non-rigidly registering the population to a common coordinate system and then propagating the 3D dose distribution according to the transformation beforehand obtained. Pixel and voxel-wise non parametric analyses were performed, for DSMs and DVMs respectively, to identify regions of statistically significant dose differences between patients with/without toxicities. The spatial correlation between the regions found with the nDSMs and DVMs for each symptom was evaluated with the Jaccard score (intersection surface divided by union surface). Prediction capability was estimated by the area under the ROC curve (AUC) from logistic regression, performed at each dose bin of the DVH of the sub-volumes, the dosesurface histogram (DSH) of the sub-surfaces, and the DVH of the whole bladder.

Results

A local dose-effect relationship was found for three late toxicity endpoints: incontinence (grade≥2), retention (grade≥1) and dysuria (grade≥1). The 5-year toxicity rates were 4%, 23% and 13%, respectively. The subregions found with the two methods are mostly located in the inferior and posterior bladder. The Jaccard scores were 0.3 (dysuria), 0.42 (retention) and 0.7 (incontinence). Figure 1 shows the identified sub-surface (top) and sub-volume (bottom) for urinary incontinence. Table 1 shows the prediction capabilities, for each endpoint, of the subvolume's DVH, sub-surface's DSH and whole bladder's DVH. The dose bin with the highest significant AUC is reported.

Conclusion

Specific bladder subregions were identified by the two methods as more predictive of urinary toxicity than the whole bladder. Particularly, the DVM method highlights the importance of volumes near the bladder surface, but inside bladder volume, underlining the influence of variable bladder filling and, thus, entailing the need of optimizing dose even in "non-obvious" regions (i.e. what appears to be urine at planning CT).

  University of Rennes, Laboratoire Traitement du Signal et de l'Image, Rennes, France ; 2 IRCCS Istituto Nazionale dei Tumori, Prostate Cancer Program, Milan, Italy ; 3 Institut de Cancérologie de l'Ouest -René Gauducheau, Radiotherapy Department, Nantes, France; 4 Lucien Neuwirth Cancer Institute, Radiotherapy Department, St Priest en Jarez, France ; 5 Centre Georges François Leclerc, Radiotherapy Department, Dijon, France; 6 Centre Eugène Marquis, Radiotherapy Department, Rennes, FrancePurpose or ObjectiveRisk estimation of urinary toxicity after prostate radiotherapy is generally based on bladder DVH, disregarding any spatial dose-distribution information. bladder subregions associated with urinary toxicity via pixel-wise and voxel-wise statistical analysis on 2D dose-surface maps (DSM) and 3D dose-volume maps (DVM), respectively.


Kyungtae Lim

Jay Yoon Lee

Jaime Carbonell

Thierry Poibeau
email: thierry.poibeau@ens.fr2jaylee

Semi-Supervised Learning on Meta Structure: Multi-Task Tagging and Parsing in Low-Resource Scenarios

Multi-view learning makes use of diverse models arising from multiple sources of input or different feature subsets for the same task. For example, a given natural language processing task can combine evidence from models arising from character, morpheme, lexical, or phrasal views. The most common strategy with multi-view learning, especially popular in the neural network community, is to unify multiple representations into one unified vector through concatenation, averaging, or pooling, and then build a single-view model on top of the unified representation. As an alternative, we examine whether building one model per view and then unifying the different models can lead to improvements, especially in low-resource scenarios. More specifically, taking inspiration from co-training methods, we propose a semi-supervised learning approach based on multi-view models through consensus promotion, and investigate whether this improves overall performance. To test the multi-view hypothesis, we use moderately lowresource scenarios for nine languages and test the performance of the joint model for part-of-speech tagging and dependency parsing. The proposed model shows significant improvements across the test cases, with average gains of -0.9 ∼ +9.3 labeled attachment score (LAS) points. We also investigate the effect of unlabeled data on the proposed model by varying the amount of training data and by using different domains of unlabeled data.

Introduction

Multi-view data consist of different manifestations of the same data, often in the form of different features, and such data are abundant in real-world applications [START_REF] Xu | A survey on multi-view learning[END_REF]. Character-, word-level representations, stem, prefix, and suffix are examples of multi-view data in Natural Language Processing (NLP).

The use of multi-view data has resulted in considerable success in various NLP problems. Combining different word representations at the character, token, or sub-word levels has proven to be helpful for dependency parsing [START_REF] Botha | Natural language processing with small feed-forward networks[END_REF][START_REF] Andor | Globally normalized transition-based neural networks[END_REF], Part-of-Speech (POS) tagging [START_REF] Plank | Multilingual part-of-speech tagging with bidirectional long short-term memory models and auxiliary loss[END_REF], and other NLP tasks.

Given multiple views, a simple but popular approach is to unify multiple representations into a combined one through concatenation, averaging, or pooling. This approach is especially popular in neural networks as it is very straightforward to concatenate multiple representations without any modification of the model. All the aforementioned work also considered this approach. However, is it the best usage of multi-view data? A simple input concatenation can lead to overfitting problem as the model might ignore the specific statistical property of each view [START_REF] Zhao | Multi-view learning overview: Recent progress and new challenges[END_REF].

Recently, META-BILSTM [START_REF] Bohnet | Morphosyntactic tagging with a meta-bilstm model over context sensitive token encodings[END_REF]) was proposed to extend the naive solution of concatenating input representations in the context of POS tagging, and it showed superior performance compared to simple view concatenation on input representations. META-BILSTM builds a singleview model of each view (lower layer) and concatenates the series of single-view-model outputs to form an input to the meta layer, as shown in Figure 2. All the components of META-BILSTM (per-view models and meta layer) are trained jointly, as expressed in Eq.( 2).

In this study, we first examine whether META-BILSTM can be beneficial in the context of more complex tasks, namely multi-tasking in POS tagging and dependency parsing. The study then proposes Co-meta, a semi-supervised approach, to improve each single-view model through the consensus promotion of the multiple single-view models on unlabeled data. The proposed Co-meta is motivated by Co-Training (Blum and Mitchell 1998), a classic approach similar to multi-view learning, which enables exploration of unlabeled data and is known to be helpful in low-resource settings. Overall, Co-Training and many of its variants improve the multi-view models by maximizing agreement between the multi-view models on unlabeled data, and thus can improve performance in low-resource settings.

Thus, this study raises the question of whether classical Co-Training style approaches can further improve the META-BILSTM model in low-resource settings. Specifically, we explore two questions: (1) can respective models from different views learn from each other on unlabeled data? Moreover, (2) can this help the performance of low-resource models? We study whether improving each multi-view model by promoting the consensus in a Semi-Supervised Learning (SSL) fashion can lead to learning better meta models in the context of joint tagging and dependency parsing.

Once we apply META-BILSTM, we obtain several parsing models trained by each view. Then the main challenge that arises with regard to our SSL approach (Co-meta) is deciding what and how much a single view should learn from other views. We suggest three different methods for determining what to learn from each other, namely, Entropy, Voting, and the Ensemble-based approach. Then, to determine how much to learn from the determined example, we introduce confidence score in section 3.3.

We employ our SSL methods and META-BILSTM on top of the graph-based parser with a bi-affine classifier proposed by [START_REF] Dozat | Stanford's graphbased neural dependency parser at the CoNLL 2017 shared task[END_REF], and investigate the effectiveness of our approach on both low-and high-resource scenario experiment setups using the Universal Dependency 2.3 dataset (Zeman and others 2018). Co-meta, the proposed model shows consistent improvement across the test cases, with an average of -0.9 ∼ +9.3 Labeled Attachment Score (LAS) gains in low-resource and 0.2 ∼ 1.1 in highresource settings, respectively. The study also investigates whether the proposed method depends on unlabeled data by changing the amount and varying the domains of unlabeled data, and its effect on the proposed model. In summary, our contributions to joint parsing are as follows: 2 Related Work

Dependency Parsing with Multi-Task Structure

Dependency parsing is an essential component of many NLP applications because of its ability to capture complex relational information in a sentence. Typically, the goal of dependency parsing is to derive a tree structure for a sentence x = (w 1 , w 2 ...w n ) following a given dependency grammar. A syntactic dependency tree consists of dependency arcs (each arc is a relation between a Head, w h , and one or more dependent words w m ); each arc is labeled Dep to define the relation between w m and w h . Dependency parsing is widely used for tasks such as named entity recognition [START_REF] Kazama | Inducing gazetteers for named entity recognition by large-scale clustering of dependency relations[END_REF], discourse understanding [START_REF] Sagae | Analysis of discourse structure with syntactic dependencies and data-driven shift-reduce parsing[END_REF], and information extraction [START_REF] Fares | The 2018 shared task on extrinsic parser evaluation: On the downstream utility of English universal dependency parsers[END_REF].

Recent breakthroughs in multi-task learning have made it possible to effectively perform different tasks with the same model. The multi-task approach enriches context-sensitive feature representations by learning different tasks using shared parameters [START_REF] Hashimoto | A joint many-task model: Growing a neural network for multiple NLP tasks[END_REF]. In NLP, this approach has been widely used to learn joint models performing tagging and parsing simultaneously, and all state-of-the-art (SOTA) models now use a multi-task structure. In general, given an input sentence x and a set of gold labels y = (l 1 , l 2 ...l n ), where each l i consists of labels for tagging and parsing, the goal of the multi-task structure is to train a joint model that can provide at the simultaneously a POS tagger and a dependency parser.

There are many variants of multi-task learning for tagging and parsing. These variants consist in models sharing parameters between the tasks [START_REF] Straka | UDPipe 2.0 prototype at CoNLL 2018 UD shared task[END_REF]) and models sharing variants [START_REF] Che | Towards better UD parsing: Deep contextualized word embeddings, ensemble, and treebank concatenation[END_REF][START_REF] Lim | SEx BiST: A multi-source trainable parser with deep contextualized lexical representations[END_REF]. On top of this, recent systems trained with Language Model (LM) representations have shown even better results. One of these models, ELMo [START_REF] Peters | Deep contextualized word representations[END_REF], which is trained with unsupervised textual representations using BiLSTM. Models with ELMo obtained the best performance in the 2018 CoNLL shared task [START_REF] Che | Towards better UD parsing: Deep contextualized word embeddings, ensemble, and treebank concatenation[END_REF][START_REF] Lim | SEx BiST: A multi-source trainable parser with deep contextualized lexical representations[END_REF]. Another more-recent and cuttingedge LM, BERT [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF], which is trained by bidirectional transformers with a masked language model strategy, shows outstanding results in parsing [START_REF] Kondratyuk | 75 languages, 1 model: Parsing universal dependencies universally[END_REF][START_REF] Kulmizev | Deep contextualized word embeddings in transition-based and graph-based dependency parsing-a tale of two parsers revisited[END_REF]. While many variants exist, all these models basically produce a single parser and tagger based on a single concatenated view. In contrast, Bohnet et al. proposed an approach to build several POS taggers trained by individual lexical representations and generated a multi-view model only for POS tagging.

Co-Training

The standard multi-view learning approaches try to learn a model by jointly optimizing all the multi-view models arising from different views as opposed to combining input level multi-view data. The most representative and one of the earliest multi-view learning methods is Co-Training [START_REF] Blum | Combining labeled and unlabeled data with co-training[END_REF]. Co-Training and many of its variants [START_REF] Nigam | Analyzing the effectiveness and applicability of co-training[END_REF]Muslea, Minton, and Knoblock 2002;[START_REF] Yu | Bayesian co-training[END_REF]) try to maximize the mutual agreement of multi-view models on unlabeled data promoting consensus principle. The unified model is said to have improved when each view provides some knowledge that the other views do not possess; that is, different views hold complementary information.

Proposed Approach

We first consider the baseline model introduced by [START_REF] Lim | SEx BiST: A multi-source trainable parser with deep contextualized lexical representations[END_REF]) and extend it so as to get a multi-view model structure following [START_REF] Bohnet | Morphosyntactic tagging with a meta-bilstm model over context sensitive token encodings[END_REF]. Then, in section 3.3, we propose a new semi-supervised learning (SSL) approach called Co-meta. We detail the proposed loss function (Section 3.4) for Co-Training while taking into account the provided meta structure.

The BASELINE Model

As it is known that using information from multiple views yield better performance, most SOTA multi-task parsers use both word-level and character-level views to get a lexical embedding v (wc) 1:n from a sequence of n words w 1:n . Most of these approaches simply concatenate word embedding v (w) i . For example, Figure 1 shows a multi-task parsing architecture for low-resource scenarios proposed by [START_REF] Lim | SEx BiST: A multi-source trainable parser with deep contextualized lexical representations[END_REF]. It obtained good results on the CoNLL 2018 shared task [START_REF] Zeman | CoNLL 2018 shared task: Multilingual parsing from raw text to universal dependencies[END_REF]. Specifically, the parser transforms the sequence of shared lexical representation v (wc) i to a contextsensitive vector contextualized by BiLSTM with a hidden layer r 0 as:

h (pos) i = BiLST M (r (pos) 0 , (v (wc) 1 , .., v (wc) n )) i h (dep) i = BiLST M (r (dep) 0 , (v (wc) 1 , .., v (wc) n )) i
The system uses vector h where (x j , y j ) ∈ T denotes an element from the training set T , y is a set of gold labels (l P OS , l Head , l Dep ), and ŷ is a set of predicted labels. The model of Lim et al. 1 is subsequently used as the BASELINE model.

Supervised Learning on META(META-BASE)

In order to examine whether a multi-view learning approach similar to that of Bohnet et al. would also be helpful to perform tagging and parsing jointly, we propose the meta structure shown in Figure 2. We use Lim et al.'s multi-task structure of tagging and parsing as our default single-view model and call the overall system META-BASE.

We define a model M vi for each view vi ∈ V , where V is the set of all views. For example, Figure 2 ]. Each M vi is parameterized by the network parameter θ vi , and the overall network parameter θ is defined as the union of the network parameters of all views, that is, θ = ∪ vi∈V θ vi .

During supervised learning, we train θ to maximize the probability P (y j |x j , θ) for the input and labeled instance pair (x j , y j ) in the training set T by optimizing over the supervised loss (S_loss) as follows:

S_loss = (xj ,yj )∈T -log P (y j |x j , θ) (2)
which is simply the standard cross entropy loss, where log P (y j |x j , θ) stands for vi∈V log P (y j |x j , θ vi ) for brevity. Note that the predicted POS results are added to the parser's classifier as an embedding (learnable parameters) during training.

Co-meta

Co-meta stands for the Co-Training approach on the meta structure. The main idea of Co-Training is to augment training data with each model's confident prediction on unlabeled data so that each model can learn from other models' predictions. While not exactly following the Co-Train approach, we adopt the idea of one model teaching other models. We propose to extract the best possible parsing result using all models' predictions as ŷ * on a given instance x in unlabeled set U , and make each single-view model learn from ŷ * by optimizing over the proposed unsupervised loss (C_loss) as follows:

C_loss = - vi∈V \{meta} x∈U g(ŷ * , ŷvi ) log P (ŷ * |x, θ vi ). (3)
Here, ŷvi = arg max y P (y|x, θ vi ) stands for the output for view vi and the g(ŷ * , ŷvi ) stands for the confidence score, which measures the confidence of ŷvi with respect to ŷ * . The ways to obtain ŷ * can be divided into three variants depending on how one extracts ŷ * . We detail the notions of Entropybased, Voting-based, and Ensemble-based extraction.

• Entropy-based extraction selects the entire prediction of model M vi * and set ŷ * = ŷvi * where the prediction of view vi * has the lowest entropy for its prediction score , i.e. vi * = argmax vi∈V P (ŷ vi |x, θ vi ). In the entropybased approach, the view vi * only teaches other views and does not teach itself.

• Voting-based extraction selects the most popular label among the three models for each word w m . When there is no agreement between the output of each model, we select the prediction of M (meta) .

• Ensemble-based extraction selects ŷ * using an ensemble method, that is, ŷ * = sof tmax( vi∈V P (ŷ vi |x, θ vi )).

In addition, we scale the loss function with the confidence score g(ŷ * , ŷvi ), which measures the similarity between the two arguments. The idea is to assess how much confidence one should have in updating model θ vi with instance ŷ * . We hypothesize that if the prediction ŷvi has a similar structure to the extracted ŷ * , then the vi-view model is aligned with the extracted output and thus can confidently learn from ŷ * . In more detail, confidence score g(ŷ vi , ŷvj ) = n t=1 I(y vi t , y vj t )/n ranging from 0 to 1 is a simple agreement measure between ŷvi , ŷvj normalized by the sentence length n. Note that we update the parameters of each view model but do not update the parameters of M (meta) using C_loss to avoid overfitting.

The idea of learning from model's own prediction was explored by [START_REF] Dong | Ensemble-style self-training on citation classification[END_REF] in the context of selftraining but without the confidence score. In our experiments, all the models without a confidence score showed a decrease of performance for all the three variants of ŷ * .

Joint Semi-Supervised Learning

While labeled data T is small in low-resource scenarios, we often have larger unlabeled data U . We thus need to leverage the supervised model Eq.( 2) using unlabeled data. Since our C_loss only requires prediction result ŷ, we can train both T and U as a joint loss (J_loss) as follows:

J_loss = (xj ,yj )∈T -log P (y j |x j , θ) (4)
-

vi∈V x k ∈U g(ŷ * k , ŷvi k ) log P (ŷ * k |x k , θ vi )
where T ⊆ U might apply to U , T when using T without labels. During the joint learning phase, we use the individual CrossEntropy objective function to compute all the losses with an Adam optimizer. In what follows, let's call Co-meta the training process with J_loss on the meta-LSTM structure.

Experiments

Data Sets

We evaluate Co-meta on the Universal Dependency 2.32 test set for nine languages, following the criteria from (de Lhoneux, Stymne, and Nivre 2017), with regard to typological variety, geographical distance, and the quality of the treebanks. Our testing languages are thus Ancient Greek, Chinese, Czech, English, Finnish, Greek, Hebrew, Kazakh, and Tamil. During training, we use pre-trained word embeddings3 and unlabeled data4 from the CoNLL 2018 shared task to initialize our word embedding v (w) and the SSL presented in the previous section. When we employ Language Models, we use pretrained models provided by [START_REF] Lim | SEx BiST: A multi-source trainable parser with deep contextualized lexical representations[END_REF] for ELMo and Google5 for BERT. We use the gold segmentation result for the training and test data.

Evaluation Metrics

There are two major evaluation metrics in dependency parsing. The Unlabeled Attachment Score (UAS) is used to evaluate the structure of a dependency graph. It measures to what extent the structure of the parsed tree is correct, without taking into account the labels on the different arcs of the tree. The Labeled Attachment Score (LAS) is the same as UAS, but takes into account dependency labels.

As for POS tagging, we measure the percentage of words that are assigned the correct POS label. We evaluate our tagger and parser based on the official evaluation metric provided by the CoNLL 2018 shared task6 .

Experimental Setup

We sample 50 instances from labeled data as a training set to test the low-resource scenario following [START_REF] Guo | A representation learning framework for multi-source transfer parsing[END_REF]. In addition, we test our models on extremely lowresource scenarios to investigate the effect of our semisupervised approach. We borrow hyperparameter settings from the BASELINE and apply them on the single-view layers in the META-BILSTM structure. In each epoch, we run over the training data with a batch size of 2 and run only a batch from randomly chosen unsupervised data. We evaluate our models on the test sets, and report the average of the three best performing results, trained with different initial seeds, within 1,000 epochs. All the reported scores without any mention are based on the scores from the meta-layer output.

Results

Our study has different goals: (1) study the impact of multiview based learning, META-BASE Co-meta, on tagging and parsing in low-resource scenarios, (2) check whether Co-meta can increase the consensus between single-view models and the effect of this promoted consensus on the performance of each-view model and on the overall Table 1: LAS and UPOS scores of M (meta) model output on the test set using 50 training sentences and unlabeled sentences based using Co-meta, META-BASE, and our BASELINE model [START_REF] Lim | SEx BiST: A multi-source trainable parser with deep contextualized lexical representations[END_REF]. We report META-BASE to decompose the performance gain into the gains due to META-BASE (supervised) and Co-meta (SSL). *Kazakh only has 31 labeled instances. Thus we use only 31 sentences and its unlabeled data are sourced from Wikipedia whereas other languages take the unlabeled data from the given training corpus after removing label information. 3) study the effect of unlabeled data on Co-meta, and finally (4) investigate to what extent the efficacy of Co-meta remains when the approach is applied to high-resource scenarios.

Results in Low-Resource Settings

Impact of Multi-View Learning. Table 1 shows the experimental results of M (meta) on the test data of each language. We see that the proposed Co-Training method shows average performance gains of -0.9 ∼ +9.3 LAS points in parsing and +1.7 ∼ 6.9 points in tagging compared to BASELINE. Note that the proposed META-BASE approach also shows a LAS improvement of -0.6 ∼ +6.5 for BASELINE as well. Breaking down the contribution of improvement, Co-meta shows -0.3 ∼ +2.8 LAS improvement over META-BASE and this improvement is comparable to the improvement of META-BASE over BASELINE.

Comparison of Co-meta Variants. When we compare the three proposed Co-Training approaches, one can see that the ENSEMBLE approach seems to work better than EN-TROPY, and VOTING is always worst. This is because the best-voted labels for each token do not guarantee to get an optimal structure over the parse tree at the sentence-level, since the VOTING model has a relatively high chance of learning from the inconsistent graph that has multi-roots and cycling heads among tokens.

Lastly, we also try running Co-meta experiments without confidence scores, i.e., we set the confidence score as 1. We find that, with this configuration, performance always decreases in comparison to META-BASE, and thus, we conclude that the proposed confidence score plays a major role in stabilizing the Co-Train approach.

Interaction among the layers? More detailed per-layer analysis of the LAS scores is available in Table 2 for the case of Greek corpus. Among the three views, CHAR always outperforms WORD, and all three views improve after using Co-meta: improvements of 1.6-1.7 LAS point for META, 1.5-1.7 for CHAR and 3.8-4.2 for WORD.

We make three interesting observations. First, we note that the model with lower performance, namely WORD view in our example, always benefits the most from other betterperforming views. Second, the evolution of low-performing views towards better results has a positive effect on META view, and thus on the overall performance. While the score CHAR increases by 1.5, META increases by 1.7. If the lowerperforming-view model was not helping, then the improvement would be upper-bounded by the performance gain of the higher-performing model. Note that we do not update the META layer θ (meta) when using Co-meta, and all the gains result from the improvements of the single-view layers. Lastly, we can observe the CONFIDENCE scores between word and char views on the last column increase when we apply Co-meta. As the Higher CONFIDENCE denotes that models predict a similar tree structure, we can confirm that Co-meta indeed promotes the consensus between the views.

Sensitivity to the Domain of the Unlabeled Set. In Table 3, we investigated a more realistic scenario for our semisupervised approach for two languages, Chinese and Greek, by using out-of-domain data: Wikipedia and a crawled corpus. In the case of Chinese, the crawled out-domain corpus shows better results than the in-domain corpus for both ENTROPYbased and ENSEMBLE-based Co-meta, by up to 1.1 UAS and 0.9 POS points. In contrast, for Greek, the in-domain corpus (el_bdt) shows a better result than the out-domain corpus even when the size of el_bdt is only about 13% of the others. We conjecture that as the Chinese has large character sets, the exposure to diverse characters helps learning regardless of the domain.

Effect of Training Size on Performance. Table 1 shows positive results for the Co-meta given fixed size train data. However, would Co-meta be useful even with extremely low resource scenarios (<50 sentences)? And also in a more favorable scenario, when more resources are available for training (e.g. >1000 sentences)? To answer these questions, we conducted an experiment using the zh_gsd (Chinese) corpus with training sets of different sizes, but with a fixed set of 12k unlabeled data. The results are visible in Figures 3(A,B). Figure 3(A) shows our results for the lower resource scenario (with less than 50 sentences for training). Co-meta outperforms META-BASE and BASELINE except when only five sentences are used for training. We conjecture that this result is attributable to the fact that too little vocabulary is used to allow meaningful generalization. A similar behavior was observed for fi_ftb in Table 1: in this experiment, there is only 241 token available for fi_ftb, whereas other languages had on average ∼1388. However, as observed in Figure 3, once we expand the labeled instances (>20 sentences), Co-meta and META-BASE always outperform BASELINE, both in lower (3A) and higher resource (3B) settings. Also note that Co-meta always outperforms META-BASE, including when one only has 5 labeled instances for training.

We can refine our analysis by examining the different layers of META-BASE and Co-meta that appear on Figure 3 A-1. META-BASE is detailed on Figure 3 C-2 and Co-meta on Figure 3 C-1. In most cases, META stays close to the highest performing view (the WORD layer for most cases). One interesting fact is that the WORD as well as meta layer of META structures outperform the BASELINE which is built on a combined view.

The biggest contrast between Co-meta and META-BASE is the gap between the performances of the WORD and the CHAR layers. A closer look at META-BASE(C-2) seems to indicate that the performance of the META layer cannot differ too much from the lower-performing layer (CHAR in our case). When the gap between WORD and CHAR becomes too large (>5 points), then the performance gain of META layer is parallel to that of CHAR layer for train size of 10-50 even when the WORD layer makes steeper performance gains. In contrast, the Co-meta's META layer from 3(C-1) shows more stable performance as the gap between CHAR and WORD is minimal as the two layers learn from each other.

To summarize from Table 1 and Figure 3, the proposed SSL approach is always beneficial for the META-BILSTM structure when comparing the LAS scores between Co-meta and META-BASE. However, the META-BILSTM structure itself might not benefit when too few tokens exist in the train set. In general, we hypothesis that for META-BILSTM structure to be useful, the train set should consist of more than 300 tokens (more than 20 sentences) to provide generality.

Results in High-Resource Settings

Although the lack of annotated resources for many languages has given rise to low-resource approaches, several languages exist with plenty of resources. We thus need to examine whether our approach is also effective in more favorable setting, when large scale resources are available. A comprehensive overview is shown in Table 4, where different systems using no language model (first part of the table), or ELMo [START_REF] Peters | Deep contextualized word representations[END_REF] or BERT [START_REF] Devlin | BERT: Pre-training of deep bidirectional transformers for language understanding[END_REF]) language models are evaluated.

Table 4 includes a comparison of our results using the approach presented in this paper with four state-of-the-art systems. The first system is BASELINE (introduced in section 3.1), which obtained the best LAS measure for English in the 2018 CoNLL shared task. The second is UDPIPE [START_REF] Straka | UDPipe 2.0 prototype at CoNLL 2018 UD shared task[END_REF][START_REF] Kondratyuk | 75 languages, 1 model: Parsing universal dependencies universally[END_REF] which was one of the best performing systems during the 2018 CoNLL shared task (best MLAS score, that combines tagging and parsing, and 2nd for the average LAS score). UDPIPE uses a multi-task learning approach with a loosely-joint LSTM layer between tagger and parser. The third system is UDIFY [START_REF] Kondratyuk | 75 languages, 1 model: Parsing universal dependencies universally[END_REF]) (derived from UDPIPE), where the LSTM layer is replaced with BERT embedding, which is in turn fine-tuned during training. The fourth system is UUPARSER wherein concatenated word, character and BERT embedding serves as an input, i.g.,

h i =[v (wc) ;v (bert) ].
Effect of Co-meta On High-Resource Settings without LMs. By expanding the baseline with our meta-LSTM and SSL approach, we observe a slight improvement of up to 0.19 and 0.04 points against the BASELINE and UDPIPE, respectively. In contrast, we find that both META-BASE and Co-meta slightly underperform the BASELINE in tagging, which goes against our intuition. One possible reason might be that there is enough data to get accurate results using a supervised learning approach while SSL suffers from unexpected surface sequences. Another evidence of this is that SSL did not bring further improvement when using more than 10,000 training sentences. In contrast, interestingly, Chinese for which we had a relatively small train set (3,997), is positively affected by SSL, with a gain of up to 0.21 LAS points comparing to UDPIPE, 1.12 points with BASELINE. We assume that the main reason for this is the character set. Languages with a bigger character set size and little training data gain more influence with SSL.

Effect of Co-meta On High-Resource Settings with LMs. While we train our model with a LM, we concatenate the last layer of the LM embedding with the input of the BiLST M (meta) presented in the previous section. Finally, the input of our meta model consists of three different contextualized features as [h

(word) i ;h (char) i ;v (lm) i ].
On average, adding a LM provides excellent results for both dependency parsing and POS tagging outperforming cases without LMs by large margins, up to 1.27 LAS for ELMo and 2.97 for BERT. Furthermore, our parser with Co-meta globally shows better results than the state-ofthe-art parsers that use ELMo [START_REF] Lim | SEx BiST: A multi-source trainable parser with deep contextualized lexical representations[END_REF] and BERT-Multilingual model [START_REF] Kondratyuk | 75 languages, 1 model: Parsing universal dependencies universally[END_REF]). However, it should be noted that the UDIFY model used by [START_REF] Kondratyuk | 75 languages, 1 model: Parsing universal dependencies universally[END_REF]) (that includes Bert-Multilingual as a LM) was first trained with 75 different languages using Universal Dependency corpora and then tuned for English, and it is not clear how this training process affects the performance. Thus, we add the results of UUPARSER and BASELINE with BERT to represent fine-tuning in a monolingual way only and still found that CO-META+BERT-MULTI shows better performance.

We generalized Co-meta by adding an additional view: LM embedding. We conclude that Co-meta can, surprisingly, result in positive effects by more than 1-1.7 points compared to competing models and by 0.2 compared to the BASELINE even in a high-resource setting.

Conclusion

In this paper, we have presented a multi-view learning strategy for joint POS tagging and parsing using Co-Training methods. The proposed entropy and ensemble-based Co-meta yield the best result. This strategy is especially well suited for low-resource scenarios, when only a very small sample of annotated data is available, along with larger quantities of unlabeled data. Our experiment shows statistically significant gains (-0.9 to +9.3 points compared to the baseline), largely due to the proper integration of unlabeled data in the learning process. As future research, we wish to apply Co-meta to other sequence-labeling tasks such as Named Entity Recognition and semantic role labeling.

Acknowledgments

We want to thank the AAAI reviewers for their useful comments. Thierry Poibeau is a member of PrAIRIe (Paris Artificial Intelligence Research Institute) that partially funded this research. KyungTae Lim is supported by the ERA-NET Atlantis project.

  1. Proposal of a new formulation Co-meta that leverages consensus promotion on top of a META-BILSTM model. 2. Analysis of the relation of each multi-view model performance to that of the meta model. 3. Exploring different semi-supervised scenarios, where the amount of unlabeled data and the domains of unlabeled data are varying. 4. Generalization of META-BILSTM and Co-meta by expanding an additional-view model on top of the existing model using external word embedding.

Figure 1 :

 1 Figure 1: Overall structure of our baseline model.

  predict P OS with a Multi-layer Perceptron (MLP) classifier, and h (dep) i for Head and Dep with a bi-affine classifier (Dozat and Manning 2016). During training, it learns the parameters of the network θ that maximize the probability P (y j |x j , θ) from the training set T based on the conditional negative log-likelihood loss of our baseline B_loss(θ). Thus, B_loss = (xj ,yj )∈T -log P (y j |x j , θ)

  contains1 The parser achieved the 2nd and 4th ranks with regard to UAS and LAS, respectively, out of 27 teams in the CoNLL 2018 shared task.

Figure 2 :

 2 Figure 2: Overall structure of our Co-meta model.

Figure 3 :

 3 Figure 3: Evaluation results for Chinese (zh_gsd) based on the different sizes of the train set and proposed models. We apply ENSEMBLE based Co-meta with the fixed size of 12k unlabeled sentences while varying training set size.

Table 2 :

 2 LAS on Greek(el_bdt) corpus for each model, with the average confidence score g(ŷ) comparing M (word) and M (char) over the entire test set using 100 training sentences.

	VOTING	ENTROPY	ENSEMBLE	META-BASE	BASELINE

META-BILSTM system, (

Table 3 :

 3 Scores of Co-meta with the ENSEMBLE method on different domains of unlabeled data with 100 training sentences.

	Labeled	Unlabeled size	LAS UAS POS
	el_bdt	el_bdt	1162	69.0 75.6	88.5
	(Greek)	wikipedia	12000 68.7 75.1	88.7
		crawl	12000 68.3 74.8	88.4
	zh_gsd	zh_gsd	3997	45.3 57.9	76.9
	(Chinese) wikipedia	12000 46.3 59.1	77.6
		crawl	12000 46.1 59.0	77.8

Table 4 :

 4 LAS for the English (en_ewt) corpus for each model, with the external language models with the entire train set.

	Model	LM	LAS	UAS	POS
	UDPIPE (2019)	-	86.97 89.63 96.29
	BASELINE (2018)	-	86.82 89.63 96.31
	METABASE	-	86.95 89.61 96.19
	CO-META	-	87.01 89.68 96.17
	BASELINE (2018)	ELMo	88.14 91.07 96.83
	METABASE	ELMo	88.28 91.19 96.90
	CO-META	ELMo	88.25 91.19 96.84
	UDIFY (2019)	BERT-MULTI	88.50 90.96 96.21
	UUPARSER (2019) BERT-MULTI 87.80 -	-
	BASELINE	BERT-MULTI	89.34 91.70 96.66
	METABASE	BERT-MULTI	89.49 92.01 96.75
	CO-META	BERT-MULTI	89.52 91.99 96.80
	CO-META	BERT-BASE	89.98 92.25 97.03

http://hdl.handle.net/11234/1-2895

http://hdl.handle.net/11234/1-1989

https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-1989#

5 https://github.com/google-research/

bert 6 https://universaldependencies.org/conll18/evaluation.html


C Boutry

Lafond

Evaluation of a new portal dosimetry solution for dose quality control of Elekta and Varian linacs

Purpose or Objective

The aim of this study is to evaluate a new portal dosimetry solution (ARTISCAN Beam QA, AQUILAB -France) for independent QA of photon and electron beams on Elekta and Varian linacs.

S912 ESTRO 38

Material and Methods ARTISCAN Beam QA software is based on EpiDream method (Boutry et al, Med Phys 2017) converting EPID signal in air into absorbed dose in reference conditions. The product is developed to perform dose quality control of photon and electron beams using EPID. Our study was performed on 6 MV and 6 MV FFF photon beams from Elekta linac (Versa HD with iViewGT v3.4.1) and on 6 MV photon beam and 6 MeV electron beam from Varian linac (Clinac 2100C with EPID AS500). Intrinsic characteristics of the solution were first evaluated, including repeatability, reproducibility, dose linearity and dose consistency over the time. Then, we performed tests of our current internal QA program: daily X-Ray and electron output constancy, beam profiles constancy, wedge transmission factor constancy, monitor chamber linearity. Results were compared to those obtained with ionization chamber (0.6 cc, PTW) or with ionization chamber 2D-matrix (StarCheck, PTW).

Results

The reproducibility of 10 EPID measurements was 0.09% for 6 MV (Elekta). Dose linearity for photon and electron beams for the Varian linac was better than 1% for MUs higher than 20 with EPID solution. From 3 to 15 MUs, dose linearity was between 1.3% and 2.4% for photon beam and 0.7% and 1.9% for electron beam EPID solution. A 4 months analysis showed a dose deviation for a square field between +0.05% and -1.25% for 6 MV beam and between +0.39% and -1% for 6 MV FFF beam (Elekta). For the daily output constancy on Elekta linac, the mean difference obtained for 32 QAs between EPID dosimetry and ionization chamber was -0.1% (σ=0.3%) for 6 MV and -0.6% (σ=0.3%) for 6 MV FFF beam. Beam profiles obtained with ARTISCAN Beam QA software and the StarCheck were plotted for 6 MV, 6 MV FFF and 6 MeV beams in Fig 1 . The difference on wedge transmission factor obtained with EPID solution and ionization chamber was 0.2% for 6 MV (Varian). The maximal difference on monitor chamber linearity for 6 MV beam (Varian) between EPID solution and ionization chamber was 1.4% for MUs higher than 3, reaching 5% for 1 MU.

Conclusion

Our results showed that the EPID dosimetry solution proposed by AQUILAB seems to be promising to perform QA programs on Elekta and Varian linacs for both photon and electron beams.

EP- 1694

 1694 Evaluation of a new portal dosimetry solution for dose quality control of Elekta and Varian linacs S. Couespel 1 , N. Delaby 1 , S. Sorel 1 , C. Boutry 2,3 , C. Lafond 1,4 1 Centre Eugène Marquis, Ille-et-Vilaine, Rennes, France ; 2 Groupe Oncorad Garonne, Tarn-et-Garonne, Montauban, France ; 3 Dream SAS, Haute-Garonne, Toulouse, France ; 4 Univ Rennes-CLCC Eugène Marquis-Inserm-LTSI-UMR 1099, Ille-et-Vilaine, Rennes, France

  Reference A simple algorithm to convert EPID gray values into absorbed dose to water without prior knowledge. Boutry C, Sors A, Fontaine J, Delaby N, Delpon G. Med Phys. 2017 Dec; 44(12):6647-6653.


Out of field dose for three imaging modalities in case of image guided prostate cancer radiotherapy

PV-104 Out of field dose for three imaging modalities in case of image guided prostate cancer radiotherapy C. Le Deroff 1 , R. Lefeuvre 1 , J. Bouvier 1 , R. De Crevoisier 1,2 , C. Lafond

Purpose or Objective

In prostate image guided radiotherapy (IGRT), a recent randomized trial reported an occurrence of other malignancies two times higher in case of a daily imaging control compared to a weekly control (de Crevoisier, IJROBP 2018). The purpose of this study was to measure the additional out of field dose delivered by various imaging modalities.

Material and Methods

An anthropomorphic phantom was used to mimic a total dose of 78 Gy delivered in the prostate by IMRT/IGRT. We measured the dose related to three imaging techniques (a pair of orthogonal portal images (PI) (6 MV, 2 UM/image), a pair of orthogonal kV images (75 kV, 10 mAs and 105 kV, 80 mAs) and a standard full kV-CBCT (125 kV, 676 mAs)). The doses were measured for 21 points along the central axis of the phantom with thermoluminescent dosimeters (GR-200A). These doses were compared to the measured doses related to the treatment (IMRT, 5 beams, 6 MV, sliding window technique). Finally, the doses for various kV-CBCT parameters were computed: a low dose (260 mAs) and a high dose protocol (1300 mAs).

Results

Figure 1 shows the measured out of field dose for the three imaging modalities for one fraction/control. Orthogonal kV imaging (2D-kV) provides the minimum dose inside (1.12 mGy at 0 cm) and outside the field (0.11 mGy at 20 cm, corresponding to 10 cm from the imaging field edge). Standard kV-CBCT imaging provides less dose inside the field (18.6 mGy at 0 cm) than PI (28.8 mGy at 0 cm) but more dose outside the field (1.36 mGy at 20 cm) compared to PI (0.87 mGy at 20 cm). The high dose kV-CBCT protocol provides the maximum out of field dose (2.62 mGy at 20 cm), while using a low dose kV-CBCT protocol provides less dose (0.52 mGy at 20 cm) than PI. The out of field doses related to IMRT are superior to all the imaging control modalities (8.65 mGy at 20 cm). Considering a weekly control by 2D-kV imaging (8 controls) and a daily control by a standard kV-CBCT imaging (39 controls), the doses at 20 cm were 0.90 mGy and 53.1 mGy, respectively. However, when considering a daily control by 2D-kV imaging and a weekly control by a low dose kV-CBCT imaging the doses are similar with 4.3 mGy at 20 cm.

Conclusion

Both imaging modality and control frequency as well as treatment fields have an impact on the out of field dose. Regarding one imaging control session, the out of field dose ranges from 0.1 to 2.6 mGy at 10 cm from the imaging field edge.

Figure 1 :

 1 Figure 1: Measured dose profiles for one fraction of IMRT (SW: sliding window technique), one pair of orthogonal portal images (6 MV, 2 MU/image), one pair of orthogonal kV images (75 kV 10 mAs, 105 kV 80 mAs) and one full kV-CBCT (125 kV) with either 260 mAs (low dose), 676 mAs (standard) and 1300 mAs (high dose). The origin represents the treatment and imaging isocenters and is located at the prostate barycenter. Conclusion Both imaging modality and control frequency as well as treatment fields have an impact on the out of field dose. Regarding one imaging control session, the out of field

  1,2

1 Centre Eugène Marquis, Radiotherapy, Rennes, France; 2 Univ Rennes-CLCC Eugène Marquis-Inserm-LTSI-UMR 1099, Radiotherapy, Rennes, France