Virginie Grand-Perret
email: virginie.grand-perret@u-psud.fr

Jean-René Jacquet

Ingrid Leguerney

Baya Benatsou

Jean-Marc Grégoire

Georges Willoquet

Ayache Bouakaz

Nathalie Lassau

Stephanie Pitre- Champagnat

A Novel Microflow Phantom Dedicated to Ultrasound Microvascular Measurements

Keywords: ultrasound microvasculature, microfluidics, flow phantom, flow imaging, microvascular Doppler, superb microvascular imaging, power Doppler imaging

Tumor microvascularization is a biomarker of response to antiangiogenic treatments and is accurately assessed by ultrasound imaging. Imaging modes used to visualize slow flows include Power Doppler imaging, dynamic contrast-enhanced ultrasonography, and more recently, microvascular Doppler. Flow phantoms are used to evaluate the performance of Doppler imaging techniques, but they do not have a steady flow and sufficiently small channels. We report a novel device for robust and stable microflow measurements and the study of the microvascularization. Based on microfluidics technology, the prototype features wall-less cylindrical channels of diameters ranging from as small as 147 up to 436 µm, cast in a soft silicone polymer and perfused via a microfluidic flow pressure controller. The device was assessed using flow rates from 49 to 146 µL/min, with less than 1% coefficient of variation over three minutes, corresponding to velocities of 6 to 142 mm/s. This enabled us to evaluate and confirm the reliability of the Superb Microvascular Imaging Doppler mode compared with the Power Doppler mode at these flow rates in the presence of vibrations mimicking physiological motion.

Introduction

Assessment of microvascularization in tissues is clinically relevant in cardiology 1,2 and oncology. [START_REF] Lassau | Validation of dynamic contrastenhanced ultrasound in predicting outcomes of antiangiogenic therapy for solid tumors: the French multicenter support for innovative and expensive techniques study[END_REF] Angiogenesis is among the key processes involved in tumor growth. [START_REF] Carmeliet | Molecular mechanisms and clinical applications of angiogenesis[END_REF] As a therapeutic target for antiangiogenic drugs, angiogenesis is also a promising biomarker for the evaluation of patient response to treatment. [START_REF] O'connor | Imaging biomarker roadmap for cancer studies[END_REF] In this context, ultrasound imaging is an attractive technique for exploring flow in vascular networks at different scales. Color Doppler and pulsed Doppler modes are usually used to image fast flows, while microvascular flows are typically imaged using Power Doppler mode (PD), which is able to visualize perfusion in vessels with diameters of a few hundred micrometers. [START_REF] Lassau | Evaluation of con-trastenhanced color Doppler ultrasound for the quantification of angiogenesis in vivo[END_REF] Microvasculature assessment in vessels below the limit of a few hundred micrometers can be performed by specific modes. Dynamic contrast-enhanced ultrasound (DCE-US), which is based on the injection of contrast agents (encapsulated microbubbles) to enhance the scattered signal, enables quantification of the microvasculature. New microvascular Doppler modes based on advanced signal processing both in "conventional" imaging as well as in ultrafast plane-wave imaging were recently developed to visualize microvascularization without the need for contrast agent injection. [START_REF] Desailly | Contrast enhanced ultrasound by real-time spatiotemporal filtering of ultrafast images[END_REF][START_REF] Forsberg | Microvascular blood flow in the thyroid: preliminary results with a novel imaging technique[END_REF][START_REF] Gabriel | Comparison of superb micro-vascular ultrasound imaging (SMI) and contrast-enhanced ultrasound (CEUS) for detection of endoleaks after endovascular aneurysm repair (EVAR)[END_REF][START_REF] Yongfeng | Application of a novel microvascular imaging technique in breast lesion evaluation[END_REF] In particular, Superb Microvascular Imaging® (SMI) available on Aplio 500 (Toshiba Medical Systems, Japan) is based on high-resolution Doppler imaging with high-density beamformer architecture and a powerful and adaptive algorithm that effectively separates flow signals from tissue motion artifacts.

Phantoms are commonly used to characterize and calibrate ultrasound imaging systems and to verify whether their performance meets quality standards. [START_REF]Ultrasonics-Flow measurement systems-Flow test object[END_REF] They are also used to assist the development of new transducers and diagnostic techniques, as well as to assess new imaging modalities and image processing techniques. Moreover, they can be used to demonstrate clinical improvements. [START_REF] Franchi-Abella | Ultrasound elastography: advantages, limitations and artefacts of the different techniques from a study on a phantom[END_REF] Specific flow phantoms have been developed to perform reproducible tests to evaluate the performance of new microvascular flow imaging modalities and microflow Doppler image processing techniques. The phantoms described below are test objects made out of tissuemimicking materials (TMM) with acoustic properties mimicking those of human tissues and vessels, specified by the International Electrotechnical Commission 61685. [START_REF]Ultrasonics-Flow measurement systems-Flow test object[END_REF] The geometries are mainly straight channels, given the fact that they study the resolution and detection limits of the scanners and their associated imaging modes. More tortuous geometries that are more physiologically relevant would be interesting to assess quantitative imaging realistically, with wash-in and wash-out phases that would be sensitive to the tortuous geometry of the flow : such phantoms have been made for three-dimensional (3D) ultrasound flow imaging, using rapid prototyping and investment casting techniques [START_REF] Ho | Wall-less flow phantoms with tortuous vascular geometries: design principles and a patient-specific model fabrication example[END_REF] as well as lost-core casting principles [START_REF] Yiu | Spiral flow phantom for ultrasound flow imaging experimentation[END_REF] to assess the efficacy of ultrasound flow estimation methods.

Doppler flow phantoms with a channel of 1 to 2 mm can be made by casting TMM around a rubber tube, [START_REF] Browne | A review of Doppler ultrasound quality assurance protocols and test devices[END_REF] or around a rod that is removed after solidification of the TMM to obtain a wallless design. [START_REF] Kenwright | Wall-less flow phantom for highfrequency ultrasound applications[END_REF] However, blood capillary diameters are typically 5 to 500 µm, 17 so microvascularization phantoms need to contain thinner channels. Thinner tubes of 200 to 700 µm-diameter can also be encapsulated in TMM, [START_REF] Camfferman | Calibrating Doppler imaging of preterm intracerebral circulation using a microvessel flow phantom[END_REF] however TMM has a limited shelf life (a few months) and the wall around the channel can cause reflection artifacts. To downsize the channel, a dialysis cartridge composed of a large number of cellulose capillaries of 200 µm-diameter has been used, [START_REF] Hindle | A perfusion phantom for the evaluation of ultrasound contrast agents[END_REF] however the capillaries become porous over time, and only the top capillaries can be imaged because of ultrasound attenuation. This setup is thus not reproducible, and capillaries cannot be imaged individually.

New approaches including the use of microfluidic chips [START_REF] Desailly | Resolution limits of ultrafast ultrasound localization microscopy[END_REF][START_REF] Kim | A microfluidics-based pulpal arteriole blood flow phantom for validation of doppler ultrasound devices in pulpal blood flow velocity measurement[END_REF] appear to be a promising solution to mimic complex microvascular networks. These chips allow manipulation of fluids in channels ranging from less than a micrometer to a few millimeters, including complex geometries. They possess valuable properties such as slow and laminar flows, wall-less channels and the possibility of coating them with endothelial cells for biological tests. They are easy to prepare in a clean room environment, inexpensive and easily molded. Desailly et al. and Kim et al. [START_REF] Desailly | Resolution limits of ultrafast ultrasound localization microscopy[END_REF][START_REF] Kim | A microfluidics-based pulpal arteriole blood flow phantom for validation of doppler ultrasound devices in pulpal blood flow velocity measurement[END_REF] used standard polydimethylsiloxane (PDMS) microfluidic chips with rectangular channels to test their ultrasound imaging modes. However, PDMS material is highly absorbent which limits the propa-gation of ultrasound waves to superficial depths only. The aim of this study was to develop a new generation of microflow phantoms. To this purpose, the first objective was to identify a material less absorbing that PDMS and with a simple fabrication process so that we could make a microfluidic chip without photolithography in a clean room environment. In addition, this new phantom was connected to a setup that precisely controlled the microflow within the channels over time. Flow variability during acquisitions has also been studied. Finally, the potential interest of this new device was illustrated by comparing three microvascular Doppler imaging modes.

Materials and Methods

Phantom Design

Ecoflex 00-30, a soft silicone, was cast into a 2.5 × 5 cm custom-made mold. Four holes of 500 µm-diameter, spaced 2 to 5 mm were drilled from either side in alignment between each border of the mold. They were placed 2 mm from the bottom of the mold (the top of the phantom) to place the channels 2 mm from the incident surface (Figure 1). Strings were placed through the holes, ballasted at both ends, and guided by the screw threads. This setup allowed straight strings parallel to the bottom of the mold. The channels were then prepared using different diameters strings: 436 µm metal fishing line, 318 nylon fishing line, 147 and 242 µm sutures (monocryl 4-0 and monocryl 5-0). The diameter sizes were measured optically with an AZ-100 multizoom macroscope (Nikon, Japan) and the values indicated are the means of the two extremum values observed with the macroscope along the string. The macroscope supplier's software (Nikon, Japan) gave measurements with a 0.01 µm precision.

Once the strings were in place, the Ecoflex 00-30 material was cast with a thickness of 1 cm after degassing in a vacuum chamber for five to 10 minutes to remove air bubbles. It was left for four hours at room temperature for complete curing of the polymer. Finally, the strings were removed from the mold. The resulting phantom was a 2.5 × 5 × 1 cm 3 block composed of seven cylindrical straight channels of diameters ranging from 147 to 436 µm (Figure 1), including the four channels we are going to study here.

For microflow experiments, we used a blood-mimicking fluid (BMF) consisting of small particles which mimic ultrasound scatterers such as red blood cells, that are essential in microvascular Doppler modes. The home-made BMF [START_REF] Oates | Towards an ideal blood analogue for Doppler ultrasound phantoms[END_REF][START_REF] Ramnarine | Validation of a new blood-mimicking fluid for use in Doppler flow test objects[END_REF][START_REF] Samavat | An ideal blood mimicking fluid for Doppler ultrasound phantoms[END_REF] was composed of nylon beads (Orgasol, Arkema, France) of 5 µm-diameter mixed with distilled water (83.86%), glycerol (10.06%, Merck, Germany), 185 000 D dextran (3.36%, Sigma-Aldrich, Switzerland), and ICI synperonic N surfactant (0.9%, BDH, England). Before each use, BMF was filtered through a paper sieve with 30 µm pores (Whatman d113, GE Healthcare, UK) with a Büchner device to remove clots that could block the phantom channels.

Materials for Acoustic Characterization

Six platinum-cure silicones were chosen as they are robust and give good mechanical support, despite their acoustical attenuation: Ecoflex® 00-10, Ecoflex 00-20, Ecoflex 00-30, Dragonskin®, Sortaclear® 18, and Moldstar® (Smooth-On, Macungie, Pennysylvania, USA). These materials were made following the supplier's instructions with very similar fabrication processes, namely 1:1 ratio and five minutes degassing at a pressure under 29 inches of mercury. The silicones were placed in a vacuum chamber connected to a N145.1.2AT.18 diaphragm vacuum pump (KNF, Germany; Maximum pressure: 7 bars, Delivery rate: 50 L/min, Ultimate vacuum: 100 mbar) and degassed five minutes to remove entrapped air. They were then compared so that the least attenuating material was chosen. They differed in hardness and chemical composition. Their acoustical properties were measured using a broadband substitution technique [START_REF] Aium | Methods for specifying acoustic properties of tissue mimicking phantoms and objects, Stage I[END_REF][START_REF] Sun | The speed of sound and attenuation of an IEC agar-based tissue-mimicking material for high frequency ultrasound applications[END_REF][START_REF] Kenwright | Acoustic assessment of a konjac-carrageenan tissue-mimicking material at 5-60 MHz[END_REF] on 2 mm-thick silicone samples as shown in Figure 2.

Each sample was placed in front of a broadband 15-30 MHz composite piezoelectric transducer (Imasonic, Voray-sur-l'Ognon, France). A high acoustic impedance stainless steel piece was placed beneath the sample, acting as a perfect reflector: the reflection is considered total. The transducer was used in pulse echo mode, acquiring the backscattered signal. First, the s2 and s3 signals were acquired, s2 being the signal backscattered from the front of the sample and s3 being the signal reflected from the interface sample/stainless steel at the back of the sample. Second, the s1 signal was acquired independently, without the sample.

Using the substitution method, the speed of sound V s in the sample was calculated using:

Vw (T °).(t1 -t2 ) Vs (T °) = (t3-t2 ) , ( 1
)
s with V w being the speed of sound in water at a temperature T°2 8 , t 1 , t 2 , and t 3 were the times of flight corresponding to maximum magnitude of the received signals s1 (t), s2 (t), and s3 (t), respectively.

The density of the sample r was measured using the Archimedes principle and the acoustic impedance Z s of the material was calculated using: Z s =r.V s .

(

) 2
The attenuation as of the sample was calculated with the following equation 29 :

20 log ae S 1 ´ A ö ç S ÷ (3)
a = è 3 ø , 2T s
where S1 (S3) is the Fourier transform of the signal s1 (s3), A is the total amplitude transmission coefficient, taking into account the transmit and return transmission values at the interface water/ sample, and T s is the thickness of the sample extracted from the previous calculated speed of sound in the sample using:

T s = V s (T °).(t 3 -t 2 ). (4)

Setup

For imaging experiments, the phantom was placed in a tank filled with degassed water and layered with absorbing material (Precision Acoustics, UK). A frame of absorbing material was used to maintain the phantom in the bottom, holding it from floating. The BMF was circulated by a MFCS-EZ microfluidic flow control system (Fluigent SA, France) used as a pressure-based flow controller. This device, commonly used in the microfluidic field, allows the circulation of fluids by pressure regulation. The MFCS operates over a wide pressure range, up to seven bars. To optimize precision, a high resistance circuit was placed upstream to adapt the pressure range displayed by the flow control system. The upstream circuit was composed of 160 cm of 200 µm inner diameter plastic tubes followed by 48 cm of 250 µm inner diameter plastic tubes. The MFCS-EZ system controlled the constant flow of fluid volume in the micro and nano-liter range.

The setup was composed of a compressor, a manual pressure regulator and an air dryer module, the MFCS-EZ, and the reservoir (Figure 3). BMF was put in the reservoir, and connected to the phantom via plastic tubes. PEEK tubes of 250 µm (0.01 inch) inner diameter and 0.79 mm (1/32 inch) outer diameter (IDEX Health & Science, Rohnert Park, California) are plugged into the channel inlets and outlets, and held in place by the silicone elasticity. A flow-rate platform measures the flow rate in real-time using a thermal sensor. Four different overpressure values were applied to each channel to obtain velocities between 6 and 142 mm/s: 1500, 2000, 3000, and 4000 mbar. The flow rate was controlled in real-time with the flow-rate platform. This pressure range was adequate for studying velocities in the range of physiological blood velocity.

A Toshiba Aplio 500 ultrasound scanner (Toshiba Medical Systems, Tokyo, Japan) equipped with a 12 MHz linear array probe 1204 BT was used to perform imaging sequences. The center of the channel was placed at the optimal focal depth of the probe (1.3 cm). Vibrations were applied on the tank to simulate the physiological motions which are a source of image artifacts. The vibrations originated from the compressor, which was not working in continuous mode: the compressor stopped working when the pressure in the circuit was high enough, and started working again when the pressure was below a certain level. We thus used the compressor as a reproducible vibration source, and we could start the vibrations by opening the pressure circuit. The vibrations were measured using a tri-axis KX023-1025 accelerometer (Kionix, Ithaca, New York, USA; Sensitivity: 16 to 16384 counts/g) fixed on the setup.

Ultrasound Data Acquisition

Flow was measured over time by the flow-rate platform for a given overpressure in the channels. For the four different pressures and the four channels sizes, 10-second video clips were acquired using Power Doppler Mode (PD), and SMI modes, both with and without the vibrations. Each video clip was rated by two physicists and a small animal U.S. imaging specialist, as figured in Table 2. Monochromatic Superb Microvascular Imaging (mSMI) and Color Superb Microvascular Imaging (cSMI) are two microvascular Doppler modes developed specifically on the Aplio 500 scanner. [START_REF] Hata | Seeing the unseen new techniques in vascular imaging[END_REF] They are based on an algorithm that allows visualization of low-velocity microvascular flow without injection of contrast agent. None of these three modes available on the Aplio 500 scanner, namely, PD, cSMI and mSMI, displayed the flow velocity.

Acquisition parameters were optimized and kept constant throughout the experiment: applied acoustic pressure was set to maximum (100%), mechanical index (MI) to 1.1, gain to 88, and dynamic range to 70 dB. Parameters used for PD mode were: Pulse repetition frequency (Prf) 3.4 kHz (Velocity bar range 0.6), MI 1.5, 5 fps; parameters used for mSMI mode were: Prf 3.7 kHz (Velocity bar range 0.4), MI 1.5, 37 fps, SMI 7.2 MHz; and parameters used for cSMI mode were: Prf 3.7 kHz (Velocity range bar 0.4), MI 1.5, 37 fps, SMI 7.2 MHz.

Results

The acoustic properties of the different silicone materials including Sylgard 184 PDMS (Dow Corning, Midland, Michigan) were measured and compared with the International Electrotechnical Commission flow test object standards 11 (Table 1): none of the materials tested had acoustic properties matching the acoustic properties of tissue. The sound velocities were low ranging from 1024 to 1049 m/s, the acoustic impedance was about 1.1 MRayl and high attenuations between 34 and 57 dB/cm were measured at 10 MHz. Among the tested materials, Ecoflex 00-30 and 00-20 had the lowest attenuation at 10 MHz, implying that for vessels placed at 2 mm depth, the attenuation was equivalent to 1.3 cm of tissue. Ecoflex 00-30 was ultimately chosen because it had a higher Shore A hardness (a physical parameter measuring the hardness of a polymer material) according to the manufacturer and was thus easier to handle compared with Ecoflex 00-20.

For the flow studies, the setup makes it possible to use extremely slow flow rates around 50 µL/min. To study the variability of the setup, the flow rate was measured for each channel and for each pressure over a period of three minutes. The measured flow rates as a function of applied pressure are shown in Figure 4 for the different channel diameters. For each channel and each pressure, the coefficient of variation was less than 1% over three minutes including 1800 measurement points. A linear relationship was observed between flow rate and pressure with a proportional coefficient ranging from 0.034 to 0.037 µL.min -1 .mbar -1 (R² > 0.995), independent of the channel size. This coefficient represented the hydrodynamic resistance provided by the whole circuit. The local resistance R h in a portion of channel of length L and radius R is given by the formula:

R h = 8.µ.L (p .R 4 ) , ( 5
)
where µ is the viscosity of the BMF. The total resistance of the circuit is given by the sum of the local resistances. In our setup, the majority of the fluidic resistance was provided by the circuit upstream of the phantom, and the phantom channel only contributed to less than 5% of the total resistance. Therefore, the channel size in the phantom did not change significantly the total resistance. The corresponding velocities can be calculated from the flow rate and the diameter of the channel: a minimum velocity of 6 mm/s is obtained with the 436 µm tube at a flow rate of 58 µL/min, and a maximum velocity of 142 mm/s is obtained with the 147 µm tube at 141 µL/min flow rate. The spectrum of the vibrations has been acquired. The amplitudes of the vibrations were less than 50 µm at a low frequency of 24 Hz.

Comparison of the results obtained with the PD and SMI (cSMI and mSMI) imaging modes was performed for each velocity and each channel diameter, as shown in Table 2.

For the smallest channel of 147 µm, and regardless of the presence of vibrations, the PD mode has no signal for flows between 49 and 141 µL/min, while cSMI has no signal for flows between 49 and 71 µL/min and mSMI had signal for all tested flows.

In the absence of vibrations, for the larger channels ranging from 242 to 318 µm, the PD mode has no signal for slow flows of about 54 up to 75 µL/min, unlike SMI modes. In the remaining On the contrary, in the presence of vibrations, for larger channels ranging from 242 to 436 µm, the artifacts made it impossible to correctly visualize the signal in PD mode, but this was possible in the cSMI and mSMI modes (Figure 5 b, d, andf).

Discussion

We developed a novel flow phantom which can be used for microvascular assessment, a key parameter in tumor imaging. It was based on a silicone material (Ecoflex 00-30) including circular channels of diameters ranging from 147 to 436 µm, which were easily made without photolithography in a clean room platform. The simple fabrication process made it easier to reproduce the procedure and thus make similar phantoms. A microfluidic pressure controlled setup enabled precise control and stable flow rates between 49 and 146 µL/min. This device demonstrated that both the cSMI and mSMI modes could better filter mild vibrations than Power Doppler in channels from 147 to 436 µm with velocities ranging from 6 mm/s to 142 mm/s.

The silicone used in this phantom, Ecoflex 00-30, was the best compromise among the silicone materials that were acoustically characterized. It shared the advantages and the drawbacks of silicones. Indeed, it is easily molded and has a long shelf life compared with agarose phantoms. However, the speed of sound at ultrasound frequencies at 10 MHz was significantly lower than in biological tissues, resulting in a distortion in the displayed image, because the speed of sound cannot be adjusted on the clinical ultrasound imaging device. However, this was not an important limit since the region of interest was not the surrounding material but the channel itself. This channel was filled with BMF having the acoustical properties required by the International Electrotechnical Committee, 11 so the image of the channel was displayed with cor-rect dimensions. Moreover, the attenuation of Ecoflex, another crucial acoustical property, was not a limiting factor to detect ultrasound signal in the channel placed 2 mm under the surface of the phantom. Indeed, 2 mm of Ecoflex is equivalent in attenuation to 1.4 cm of TMM. This is consistent with the depth of the regions of interest used in clinical examinations made with this high-frequency probe. As regard to mechanical properties, Ecoflex enables to make microflow geometries whereas widely used agarose phantoms cannot. PDMS is another silicone material known to be used to make microfluidic chip featuring microflow channels, and has thus been included in our study. PDMS had a higher acoustic attenuation than Ecoflex, which would imply using under 1 mm material above the channel to measure the same ultrasound signal as over 1.4 cm of TMM. This thickness is a limit to mechanical strength of the channel. Furthermore, it is more expensive and has a more complex fabrication process including a dedicated mixing device to avoid gas bubbles and postcuring equipment. Being the standard material used in microfluidics, [START_REF] Anderson | Fabrication of microfluidic systems in poly (dimethylsiloxane)[END_REF][START_REF] Whitesides | The origins and the future of microfluidics[END_REF] PDMS has been used to make various types of chips for ultrasound imaging characterizations. Kim et al. proposed chips suitable to mimic teeth 21 including a glass slide that mimicked teeth enamel. The channel dimensions and flows were similar to those used in our study. The design we developed had no glass slide, and thus enabled better penetration of the ultrasound waves. Desailly et al. have used microfluidic chips to study ultrafast ultrasound localization microscopy. [START_REF] Camfferman | Calibrating Doppler imaging of preterm intracerebral circulation using a microvessel flow phantom[END_REF][START_REF] Desailly | Resolution limits of ultrafast ultrasound localization microscopy[END_REF] The channel dimension of 80 × 60 µm 2 was smaller than our channels, but the velocities were significantly faster (500 µL/min flow rate). Moreover, the flow within the chip was maintained using a syringe pump. The stepper motor inside the syringe pump is reported to be a source of fluctuations in the flow rate [START_REF] Li | Syringe-pump-induced fluctuation in all-aqueous microfluidic system implications for flow rate accuracy[END_REF] and the use of a syringe pump is limited to a small reservoir to generate precise slow flow rates. By contrast, our use of a microfluidic flow pressure controller with less than 1% coefficient of variation of relative flow rate allowed us to have a stable flow using a large volume of BMF.

The interest of this setup was illustrated by performing ultrasound imaging on micro channels embedded in Ecoflex. First, the ultrasound image in B-mode was able to display the channels for every dimension. It could be noticed on the ultrasound images that artifacts were present at the interfaces between the silicone surrounding material and the channels filled with BMF. They were probably due to strong reflection because of acoustic impedance mismatches between the silicone rubber and the BMF as previously reported in a wall-less design similar to ours. [START_REF] Desailly | Resolution limits of ultrafast ultrasound localization microscopy[END_REF] Furthermore, it has been reported that channel size was overestimated by ultrasound, and that this measure had high variability [START_REF] Kenwright | Wall-less flow phantom for highfrequency ultrasound applications[END_REF] : the diameter of a 200 µm channel was measured to be 600 µm with a standard deviation of 400 µm, which makes an overestimation of 200%. That is why our velocities were calculated from the channel sizes measured precisely on the macroscope. Second, ultrasound modes dedicated to microflow imaging, precisely PD and SMI, were investigated, especially their performances with the presence of vibrations. We addressed this issue since the physiological movements frequently encountered during clinical examinations, mainly breathing of the patient and heartbeats, are known to cause image artifacts. The applied vibrations had a higher frequency than those of physiological movements (around a few Hertz), [START_REF] Thakral | taking the motion out of physiological motion[END_REF][START_REF] Ginhoux | Active filtering of physiological motion in robotized surgery using predictive control[END_REF] but were expected to have less impact on the image since their amplitude was much smaller than physiological motion: less than 50 µm compared with a few millimeters. [START_REF] Kolen | Characterization of cardiovascular liver motion for the eventual application of elasticity imaging to the liver in vivo[END_REF] Our experimental setup showed that Power Doppler was not able to detect the signal in the small channel of 147 µmdiameter, suggesting a limitation in spatial resolution, and in bigger channels for the smallest flow rates, suggesting a limitation in sensitivity. In the presence of vibrations, artifacts covered up the signal: for the 147 µm channel, there was no signal in the channel but there were some artifacts in the surrounding material; for larger diameters, the artifacts could not be distinguished from the signal.

Overall, our new microvascular flow phantom setup demonstrated that the SMI modes exhibited good sensitivity in the detection of microvessel flow distribution patterns, as observed and described in some clinical cases. [START_REF] Forsberg | Microvascular blood flow in the thyroid: preliminary results with a novel imaging technique[END_REF][START_REF] Gabriel | Comparison of superb micro-vascular ultrasound imaging (SMI) and contrast-enhanced ultrasound (CEUS) for detection of endoleaks after endovascular aneurysm repair (EVAR)[END_REF] Moreover, the PD mode was very sensitive to the vibrations we applied, limiting the detection of microcirculation. This microvascular flow setup could also be used for dynamic contrast-enhanced ultrasound modes to develop new imaging modalities, [START_REF] Eisenbrey | Recent experiences and advances in contrast-enhanced subharmonic ultrasound[END_REF][START_REF] Errico | Transcranial functional ultrasound imaging of the brain using microbubble-enhanced ultrasensitive Doppler[END_REF] new transducers [START_REF] Fouan | Investigation of classical pulse sequences for contrast-enhanced ultrasound imaging with a cMUT probe[END_REF] or new quantification methods. [START_REF] Cheung | Attenuation correction and normalisation for quantification of contrast enhancement in ultrasound images of carotid arteries[END_REF]

Conclusion

We designed and fabricated a new prototype of flow phantom based on a microfluidic pressure controlled setup, with circular channels of dimensions ranging from 147 µm to 436 µm surrounded by an easy-to-handle, acoustic compatible soft silicone material (Ecoflex 00-30). It enabled precise control of 49 to 146 µL/min flow rates with less than 1% coefficient of variation during the acquisition. Experiments led using microflow Doppler imaging modes have been able to demonstrate better visualization in SMI compared with PD, in accordance with what was clinically reported, due to reduced motion artifacts in the SMI mode versus the PD. Future developments include the use of a less ultrasound-absorbing material and more com-plex channel geometries.

Figure 1 .

 1 Figure 1. Top: scheme of the process used to make the mold. Strings (red and blue) were placed through the holes of the tank and ballasted at both ends to straighten them. This setup allowed straight strings parallel to the bottom of the mold. Bottom: homogeneous phantom composed of channels including the four channels of diameters 147, 242, 318, and 436 µm. PEEK tubes are plugged into the channel inlets, shown by the white arrows.

Figure 2 .

 2 Figure 2. Pulse-echo substitution technique used to characterize the sample acoustical properties. s1, s2, and s3 are the sound waves echoed on the stainless steel reflector alone (s1), the sample alone (s2), and on the stainless steel reflector after going through the sample (s3). The water temperature is measured with a thermometer to estimate the speed of sound in water. Ts is the sample thickness.

Figure 3 .

 3 Figure 3. Experimental setup: compressor, pressure controller controlled by Maesflow (Fluigent) software, flow sensor, and microflow phantom.

Figure 4 .

 4 Figure 4. Flow rate as a function of applied pressure, for the seven channels. Standard deviation bars are not visible at this scale.

Figure 5 .

 5 Figure 5. Ultrasound images of the 242 µm channel at 750 mbar of the flow phantom without (left) and with (right) vibrations. a, b: PD; c, d: cSMI; e, f: mSMI. Vascular signal is shown in the selected region of interest, displayed on top of black and white B-mode. In the case of monochrome Superb Microvascular Imaging, the vascular signal is shown in twin view display. PD = Power Doppler; cSMI = color Superb Microvascular Imaging; mSMI = monochrome Superb Microvascular Imaging.

Table 1 .

 1 Acoustical Properties of Silicones, Soft Tissues, TMM and BMF Using a Broadband Substitution Technique.

			Speed of		Attenuation
		Density (g/cc)	sound (m/s)	Z (MRayl)	(dB/cm) at 10 MHz Reference
	BMF	1.050-1.055	1570 -1595	1.65 -1.68	1.5 -2.2	11
	Liver		1568 -1588		3.6 -5.7	31,32
	TMM		1540 ± 15	1.60 ± 0.16	5	11
	Ecoflex 00-10	1.06	1024	1.086	45.2
	Ecoflex 00-20	1.05	1043	1.074	33.9
	Ecoflex 00-30	1.05	1025	1.076	34.4
	Dragonskin	1.07	1049	1.080	45.8
	Sortaclear18	1.07	1027	1.099	41.8
	Moldstar	1.13	1051	1.082	57.6
	PDMS (Sylgard 184)	1.05	1027	1.040	47.5

TMM = tissue-mimicking material; BMF = blood-mimicking fluid; PDMS = standard polydimethylsiloxane.

Table 2 .

 2 Comparison of Power Doppler and Superb Microvascular Imaging (Color and Monochrome) for Each Velocity and Each Channel Diameter.

		Pressure	Flow rate	PD without	PD with	cSMI/mSMI	cSMI/mSMI
	Channel	(mbar)	(µL/min)	vibrations	vibrations	without vibrations	with vibrations
	147 µm	1500	49	NS	NS-	NS/++	NS/++
		2000	71	NS	NS-	NS/++	NS/++
		3000	107	NS	NS-	++	++
		4000	141	NS	NS-	++	++
	242 µm	1500	55	NS	-	++	++
		2000	71	NS	-	++	++
		3000	105	++	-	++	++
		4000	137	++	-	++	++
	318 µm	1500	57	NS	-	++	++
		2000	74	++	-	++	++
		3000	109	++	-	++	++
		4000	145	++	-	++	++
	436 µm	1500	58	++	-	++	++
		2000	71	++	-	++	++
		3000	102	++	-	++	++
		4000	135	++	-	++	++

Each video clip was rated "++" if signal and artifacts could be distinguished, "-" if not, "NS" if there was no signal and "NS-" if there were some artifacts outside the channel but no signal inside. PD = Power Doppler Mode; cSMI = Color Superb Microvascular Imaging; mSMI = Monochromatic Superb Microvascular Imaging.

Acknowledgments

The authors thank the optical Platform PFIC who provided help for channel diameter measurements.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study was supported by grants from the research program in physics, mathematics and engineering sciences applied to cancer research, of the French Plan Cancer 2013, the Canceropôle Ilede-France, and the ARC foundation.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


Caroline Caramella

Adrien Allorant

Fanny Orlhac

Francois Bidault

Bernard Asselain

Samy Ammari

Patricia Jaranowski

Aurelie Moussier

Corinne Balleyguier

Nathalie Lassau

Stephanie Pitre-Champagnat

Can we trust the calculation of texture indices of CT images? A phantom study

Keywords: computed tomography, phantom study, texture analysis 1

Purpose: Texture analysis is an emerging tool in the field of medical imaging analysis. However, many issues have been raised in terms of its use in assessing patient images and it is crucial to harmo-nize and standardize this new imaging measurement tool. This study was designed to evaluate the reliability of texture indices of CT images on a phantom including a reproducibility study, to assess the discriminatory capacity of indices potentially relevant in CT medical images and to determine their redundancy. Methods: For the reproducibility and discriminatory analysis, eight identical CT acquisitions were performed on a phantom including one homogeneous insert and two close heterogeneous inserts. Texture indices were selected for their high reproducibility and capability of discriminating different textures. For the redundancy analysis, 39 acquisitions of the same phantom were performed using varying acquisition parameters and a correlation matrix was used to explore the 2 9 2 relationships. LIFEx software was used to explore 34 different parameters including first order and texture indices. Results: Only eight indices of 34 exhibited high reproducibility and discriminated textures from each other. Skewness and kurtosis from histogram were independent from the six other indices but were intercorrelated, the other six indices correlated in diverse degrees (entropy, dissimilarity, and contrast of the co-occurrence matrix, contrast of the Neighborhood Gray Level difference matrix, SZE, ZLNU of the Gray-Level Size Zone Matrix). Conclusions: Care should be taken when using texture analysis as a tool to characterize CT images because changes in quantitation may be primarily due to internal variability rather than from real phy-sio-pathological effects. Some textural indices appear to be sufficiently reliable and capable to dis-criminate close textures on CT images.

INTRODUCTION

Texture is defined as the quantitation of the spatial distribution of repeating patterns. It is used to translate the homogeneous or nonhomogenous appearance of the surface of an object on an image. It is also defined as a two-dimensional phenomenon: the first dimension is a description of the basic elements, or the "primitive" (the pattern), from which the texture is formed; the second dimension relates to the description of the spatial organization of these primitives. [START_REF] Castellano | Texture analysis of medical images[END_REF] Radiomics is a relatively new discipline for the clinical integration of the quantitative features of digital medical images as determined through mathematical analysis. One potential quantitative image feature of clinical significance is tumor tissue heterogeneity. Many studies have already explored the contribution of texture analysis in the field of oncology, addressing a range of topics (diagnosis, prognosis, correlation with histological or biological characteristics of the tumors) with wide-ranging results. [START_REF] Gillies | Radiomics: images are more than pictures, they are data[END_REF][START_REF] Limkin | Promises and challenges for the implementation of computationnal medical imaging (radiomics) in oncology[END_REF] The main difficulty associated with this area is that there are currently as many ways of calculating texture indices as there are research groups focusing on texture analysis. Several issues have already been raised: the definition of texture algorithms, which texture indices are relevant in medical images and the lack of reproducibility of texture calculations [START_REF] Fave | Preliminary investigation into sources of uncertainty in quantitative imaging features[END_REF][START_REF] Hunter | High quality machine-robust image features: identification in non-small cell lung cancer computed tomogra-phy images[END_REF][START_REF] Zhao | Exploring variability in CT characteriza-tion of tumors: a preliminary phantom study[END_REF][START_REF] Mackin | Measuring computed tomography scanner variability of radiomics features[END_REF] It is crucial to the success of this method-ology to harmonize the procedures and textural features and to identify any inadequate parameters which could jeopardize the chances of obtaining useful results in clinical practice. Our study is a first step toward the standardization of this new imaging measurement tool. It was designed to evaluate the reliability of texture indices of CT images on a phantom including a reproducibility study, to assess the discriminatory capacity of indices potentially relevant in CT medical images, and to determine their redundancy.

2.

MATERIALS AND METHODS

2.A. Phantom

We built a phantom based on the commercially available CIRS ® Electron Density Phantom Model 062M (Norfolk, VA, USA) which allows adequate maneuverability and the option of filling 17 holes with commercial homogeneous plugs as well as with "home-made" inserts.

The cylindrical phantom dimensions are 180 mm in diameter and 50 mm in thickness. Plug dimension is 30 mm in diameter and 50 mm in thickness. A homogenous plug with the same density as that of muscle (physical density of 1.06 g/cc) was used (named C1). In order to mimic human tissue, we designed two additional heterogeneous mixtures based on Ecoflex ® (BASF, Ludwigshafen, Germany) which is a stable polymer, composed of two pure carbon fragments in different proportions randomly arranged in an agarose support (named C2 and C3), these two inserts have the advantage of having a similar range of physical density and histograms as those found in human soft tissue, such as tumoral lung tis-sues. Figure 1 shows the inserts and the corresponding image of a CT acquisition slice.

2.B.

Reproducibility study

Eight identical CT acquisitions were made with a Discovery CT750 HD with 64 multidetector arrays (GE Healthcare, Milwaukee, WI, USA) using parameters routinely used for standard thoracic CT acquisition in patients treated in our center: voltage 120 kVp; x-ray tube current 150 mAs; 1.25 mm slice thickness; 1.375 pitch; rotation time 0.6 s, field of view 360 mm; reconstruction algorithm ASIR 0%; standard filter. Images were acquired sequentially and the phantom setup was not altered.

2.C.

Correlation of the texture index

For assessment of redundant indices, we performed 39 supplementary CT acquisitions on the same phantom and the same CT device, which differed for the acquisition parameters [voltage, intensity, slice thickness, pitch, -rota-tion time, field of view, reconstruction algorithm, and fil-ter (Table I The description of texture indices is provided in Data S1. An additional absolute discretization pixel intensity in Hounsfield units (HU) was applied to promote differentiation of intensities before texture index extraction. [START_REF] Nioche | A freeware for tumor hetero-geneity characterization in PET, SPECT, CT, MRI and US to accelerate advances in radiomics[END_REF] We chose 200 gray values, by steps of 10 HU, between the extreme values of 1000 and +1000 HU. Two-dimensional circular ROI were drawn on each of the three inserts. The size of these ROI was identical for each insert (458 mm², 758 pixels) (Fig. 2).

2.E. Statistical analyses

The eight CT acquisitions resulted in eight tables of 34 texture indices from the three ROI. The reproducibility of the texture analysis was estimated from the coefficient of variation (CV). CVs were calculated for each texture index according to the following formula:

where r is the standard deviation and l the mean value of the data sample. Variation in CV relating to imaging biomarkers is not standard, 10 in this study, a CV < 20% was considered acceptable. Dot plots were performed to show the dispersion of values of the different indices among C1, C2, and C3. A nonparametric Mann-Whitney test was used to exclude the indices which could not differentiate C1 from C2/C3, and C2 from C3. Indices that would be unable to distinguish a homogeneous area from a heterogeneous area were considered to be of no interest in the assessment of tumoral heterogeneity, as were indices that would not be able to distinguish two "visible to the naked eye" heterogeneous areas.

A Pearson correlation between the texture indices was assessed to evaluate their redundancy on the 39 different CT acquisitions. A correlation matrix was used to explore the 2 9 2 relationships between the retained parameters. Principal component analysis was used to represent the different retained indices and their pattern of correlation on a simple graph. Prin cipal component analysis is a multivariate technique that analyzes a data table in which observations are described by several intercorrelated quantitative dependent variables. Its goal is to extract the important information from the table, to represent it as a set of new orthogonal variables called principal components, and to display the pattern of similarity of the observations and of the variables as points in maps. 11

RESULTS

The characteristics of intensities (in HU) obtained within each ROI (C1, C2, and C3) are reported in Table II. As expected, the homogeneous ROI C1 exhibits a very narrow distribution spectrum of the intensity values of the pixels. The maximal variability was observed on intensities for the minValues at 16% and decreased to 4% for maxValues and meanValues. Heterogeneous ROIs C2 and C3 exhibited a close histogram distribution of intensity values of pixels, with a range close to those of human tissues. Mean values of C2 and C3 were close (<6%) despite differences in the minimal and standard pixel values. The maximal variabilities were also observed for the minValues at 19% and were less than 1% for the other values. The fluctuation of the mean value of C1 is larger than in C2 and C3, this may be explained by the fact that the stochastic noise effect may be more marked in a homogeneous area than in heterogeneous areas. The CVs of texture indices for the three ROI pooled from the eight identical CT acquisitions are shown in Table III.

Values obtained from the homogeneous insert C1 were more variable than the C2 and C3 inserts, with CVs ranging from 1% to 26%. Of note, the high variations of skewness and busyness in CV values could be explained by the variations around 0 of the mean l values of these indices. In the case of busyness, the strong variability implied a lack of reliability. The maximum variations observed for C2 and C3 were between 0% and 18%. Figures 3,4, and 5 were built to assess which indices per-mitted discrimination of each insert. The three indices deter-mined as unable to discriminate C1 from C2 and C3 were busyness, GLNU from the Gray-Level Size Zone Matrix GLSZM (Fig. 3), and SZLGE. The following parameters were identified as unable to discriminate C2 from C3: entropy and energy derived from histogram; homogeneity, energy, and correlation (Fig. 4) from the cooccurrence matrix; SRE, LRE, LGRE, HGRE, SRLGE, SRHGE, LRLGE, LRHGE, RLNU, RP from the Gray-Level Run Length Matrix; and SZE, LZE, LGZE, HGZE, SZHGE, LZLGE, LZHGE, GLNU, ZP from the Gray-Level Size Zone Matrix.

From the repeatability study, eight indices were selected because they allow the discrimination of the three textures between them. These indices are skewness (Fig. 5), kurtosis derived from histogram, contrast, and entropy from the co-occurrence matrix, dissimilarity and contrast from the Neighborhood Gray-Level difference matrix, SZE and ZLNU from the Gray-Level Size Zone Matrix. All eight indices were associated with highly significant P-values (<0.0002; Mann-Whitney test) when comparing C1 to C2, C2 to C3, and C1 to C3, as there was no overlap between the distributions for any of the eight parameters. Finally, redundant indices were identified with a 2 x 2 correlation matrix (See Table S1 in Data S1) and a principal component analysis derived from the 39 supplementary acquisitions described in Table II. Based on the analysis of the 2 x 2 correlation matrix, skewness and kurtosis were independent of the six other indices but were intercorrelated for C2 and C3, and not for C1. The other six indices correlated with various degrees: four indices (contrast of the co-occurrence matrix, contrast of the Neighborhood Gray-Level difference matrix, entropy, and dissimilarity -were all associated with correlation coefficients >0.8, even for C1. SZE was highly correlated with these four indices for C2 and C3, but to a lesser extent for C1. ZLNU was highly correlated with the five other indices for C1, but to a lesser extent for C2 and C3. Principal component analyses performed separately for each phantom pattern (C1, C2, C3) confirmed the 2 x 2 correlation analyses (fig. S1a-c ) to the first principal component. A "size effect" was observed, that is, highly positively correlated variables are on the same side of the axis, with these six indices on the right side of the first axis. The second principal factor is determined by skewness and kurtosis whose cumulative contribution to the second axis is above 85% for C1, C2 and C3. The figures show the percentage of variance due to each component and the contribution of each index.

DISCUSSION

The use of texture analyses in medical imaging is an attractive option, potentially allowing the development of a novel form of disease "biomarkers". Several teams worldwide have developed software for texture analysis and have published in vivo data in widely varying fields, supporting potential for major advances in our understanding of diseases. [START_REF] Bashir | Imaging heterogeneity in lung cancer: techniques, applications, and challenges[END_REF] It is currently very difficult to draw a coherent position or recommendations from this abundant literature especially in order to select the proper texture indices that could be relevant in clinical practice.2In particular, the reproducibility of the calculation of textural features is currently a subject of debate. Our study indicates that many textural indices show a lack of reproducibility, even under the same experimental conditions. We can hypothesize that the variations we observed are due to the complexity of helical CT principles which involve detector properties, filtered back projection algorithm, and intrinsic noise. We also observed higher fluctuations within the homogenous area C1 than within C2 and C3. This could be explained by the use of an absolute discretization pixel intensity by steps of 10 HU in our experimental conditions which would have a greater influence on the smaller range of values of C1 (around 40 HU) compared to C2 and C3 (both around 300 HU). To our knowledge, this is the first study exploring the reproducibility of textural indices from CT images acquired eight times on a single device and a dedicated phantom. Some authors have already worried about this subject in vivo but the analysis of the literature is rendered difficult because the softwares and mathematical definition of each textural index used for textural indices often differ to some extent as no consensus currently exists. Our study has a number of limitations. We studied the indices extracted from the histogram and four matrices (co-occurrence, Gray-Level Run Length Matrix, Neighborhood Gray-Level difference matrix and Gray-Level Size Zone Matrix) from a single software (LIFEx), which is not as exhaustive as some published research, 4,14,15 however, our findings are in agreement with data published by other groups in terms of the fact that only a few textural indices are likely to be sufficiently robust to be handled in clinical practice. In addition, we did not evaluate the influence of the size of the ROI. In the study of Fave et al. [START_REF] Fave | Can radiomic features be reproducibly measured from CBCT images for patients with non small cell lung can-cer?[END_REF] the authors excluded the following features: energy of the histogram, correlation of the co-occurrence matrix, LRHGE, SRLGE of the RLM matrix, busyness and contrast of the NGLDM matrix because their values were found to be volume dependent. We did not evaluate the influence of 3D vs 2D evaluation, but 2D images are more robust to variability than 3D images in patient studies. [START_REF] Zhao | Exploring intra-and inter-reader variabil-ity in uni-dimensional, bidimensional, and volumetric measurements of solid tumors on CT scans reconstructed at different slice intervals[END_REF] In clinical routine and especially in oncology, tumor images are often ill-defined and subject to approximation in their segmentation, hence tumor segmenta tion is an important step to address before analysis of textural features is possible. Finally, we chose a discretization of 200 gray levels, following results from other groups with LIFEx software in PET-CT imaging. [START_REF] Nioche | A freeware for tumor hetero-geneity characterization in PET, SPECT, CT, MRI and US to accelerate advances in radiomics[END_REF][START_REF] Orlhac | 18F-FDG PET-derived textu-ral indices reflect tissue-specific uptake pattern in non-small cell lung cancer[END_REF] In conclusion, our preliminary study highlighted a need for close attention when using texture analysis as a tool to characterize CT images because changes in quantitation may be due to internal variability rather than due to actual physio-pathological effects. Some textural indices appear to be broadly influenced by noise, whereas 8 of 34 indices such as skewness, kurtosis derived from histogram, contrast, and entropy from the co-occurrence matrix, dissimilarity and contrast from the Neighborhood Gray-Level difference matrix NGLDM, and SZE and ZLNU from the Gray-Level Size Zone Matrix GLSZM appear to be sufficiently reliable and capable of discriminating close textures on CT images. Further research should be carried out to confirm and extend these results.

a) Author to whom correspondence should be addressed. Electronic mail: caroline.caramella@gustaverousy.fr

FIG. 1 .

 1 FIG. 1. C2 and C3 inserts compacted into two syringes adapted to precisely fill the holes of the CIRS ® 062M phantom. Resulting histogram of the three ROI. [Color figure can be viewed at wileyonlinelibrary.com]

FIG. 2 .

 2 FIG. 2. Designed phantom with different inserts and resulting CT image of one slice of the phantom: 2D identical regions of interest were drawn on each of the three inserts: C1 for the homogeneous insert (upper right), C2 for the insert made of Ecoflex ® + carbon1 (central circle), and C3 for the insert made of Ecoflex ® + carbon2 (above C2).

FIG. 4 .FIG. 5 .

 45 FIG. 4. Dot plot of the close distribution of values of the calculation of the correlation index between C2 and C3.

  Index extraction was performed with LIFEx (Local Image Features Extraction) open access software (http://www.lifex soft.org; Orsay, France)[START_REF] Orlhac | 18F-FDG PET-derived textu-ral indices reflect tissue-specific uptake pattern in non-small cell lung cancer[END_REF] which calculates the minimum and maximum density values, as well as the mean and standard deviation of the gray values of the region of interest (ROI). From these primary calculations, the software accesses histogram values (skewness, kurtosis, entropyH, energyH), the co-occurrence matrix values (homogeneity, energy, contrast, correlation, entropy, dissimilarity), the Gray-Level Run Length Matrix GLRLM (SRE/LRE, LGRE/HGRE, SRLGE/ SRHGE, LRLGE/LRHGE, GLNU/RLNU, RP), the Neighborhood Gray-Level difference matrix NGLDM (coarseness, busyness, contrast), and the Gray-Level Size Zone Matrix GLSZM (SZE/LZE, LGZE/HGZE, SZLGE/SZHGE, LZLGE/LZHGE, GLNUz/ZLNU, ZP).

		)].
	2.D.	Texture analysis

TABLE I .

 I Design of the 40 different CT acquisitions performed for the redundancy analysis. The first CT acquisition corresponds to the acquisition repeated eight times for the reproducibility analysis.

	Acquisition	kVp	mAs	Slice thickness (mm)	Pitch	Rotation time (s)	DFOV (mm)	ASIR (%)	Filter
	Base	120	150	1.25	1.375	0.6	360	0	Standard
	2	80	150	1.25	1.375	0.6	360	0	Standard
	3	100	150	1.25	1.375	0.6	360	0	Standard
	4	140	150	1.25	1.375	0.6	360	0	Standard
	5	120	100	1.25	1.375	0.6	360	0	Standard
	6	120	200	1.25	1.375	0.6	360	0	Standard
	7	120	250	1.25	1.375	0.6	360	0	Standard
	8	120	300	1.25	1.375	0.6	360	0	Standard
	9	120	350	1.25	1.375	0.6	360	0	Standard
	10	120	400	1.25	1.375	0.6	360	0	Standard
	11	120	500	1.25	1.375	0.6	360	0	Standard
	12	120	150	0.625	1.375	0.6	360	0	Standard
	13	120	150	2.5	1.375	0.6	360	0	Standard
	14	120	150	3.75	1.375	0.6	360	0	Standard
	15	120	150	5	1.375	0.6	360	0	Standard
	16	120	150	1.25	0.516	0.6	360	0	Standard
	17	120	150	1.25	0.984	0.6	360	0	Standard
	18	120	150	1.25	1.375	0.4	360	0	Standard
	19	120	150	1.25	1.375	0.5	360	0	Standard
	20	120	150	1.25	1.375	0.7	360	0	Standard
	21	120	150	1.25	1.375	0.8	360	0	Standard
	22	120	150	1.25	1.375	0.9	360	0	Standard
	23	120	150	1.25	1.375	1	360	0	Standard
	24	120	150	1.25	1.375	0.6	160	0	Standard
	25	120	150	1.25	1.375	0.6	260	0	Standard
	26	120	150	1.25	1.375	0.6	460	0	Standard
	27	120	150	1.25	1.375	0.6	360	50	Standard
	28	120	150	1.25	1.375	0.6	360	10	Standard
	29	120	150	1.25	1.375	0.6	360	20	Standard
	30	120	150	1.25	1.375	0.6	360	30	Standard
	31	120	150	1.25	1.375	0.6	360	40	Standard
	32	120	150	1.25	1.375	0.6	360	60	Standard
	33	120	150	1.25	1.375	0.6	360	70	Standard
	34	120	150	1.25	1.375	0.6	360	80	Standard
	35	120	150	1.25	1.375	0.6	360	90	Standard
	36	120	150	1.25	1.375	0.6	360	100	Standard
	37	120	150	1.25	1.375	0.6	360	0	Soft
	38	120	150	1.25	1.375	0.6	360	0	Detail
	39	120	150	1.25	1.375	0.6	360	0	Bone
	40	120	150	1.25	1.375	0.6	360	0	Lung

TABLE II

 II

	maxValue	265.0	265.0	271.0	258.0	265.0	266.0	264.0	267.0
	stdValue	65.5	64.6	66.8	66.4	63.9	65.7	66.1	66.1
	meanValue	156.2	157.7	156.5	156.5	157.5	155.6	156.8	156.4

. Descriptive values of each ROI in Hounsfield units for the eight identical CT acquisitions (1 to 8).

TABLE III .

 III Coefficient of variation (CV) of the values of each index calcu-lated from the eight identical CT acquisitions for C1, C2, and C3 areas. Dot plot of the close distribution of values of the calculation of GLNU index from the Gray-Level Size Zone Matrix GLSZM of C1, C2, and C3.

	GLNUz	11.10	3.73	2.65
	ZLNU	14.30	5.08	3.59
	ZP	6.72	2.56	1.41
	LIFEx	CV	CV	CV
	Name of ROI	C1	C2	C3
	minValue	15.05	-18.65	-3.72
	meanValue	3.60	0.47	0.40
	stdValue	3.49	0.90	1.38
	maxValue	3.66	1.51	1.27
	SkewnessH	-375.13	-4.44	-1.80
	KurtosisH	5.11	3.37	2.28
	EntropyH	1.00	0.54	0.27
	EnergyH	3.17	3.20	1.45
	Homogeneity	1.12	0.88	0.80
	Energy	9.63	4.02	2.43
	Contrast (co-occurrence) FIG. 3.	6.14	2.49	3.00
	Correlation	8.50	0.80	0.38
	Entropy	3.50	0.35	0.34
	Dissimilarity	3.92	0.84	1.30
	SRE	1.85	0.42	0.44
	LRE	10.70	2.29	2.25
	LGRE	0.30	0.17	0.18
	HGRE	0.30	0.17	0.14
	SRLGE	2.01	0.38	0.41
	SRHGE	1.73	0.54	0.54
	LRLGE	10.82	2.33	2.19
	LRHGE	10.58	2.25	2.30
	GLNU	2.34	1.22	2.48
	RLNU	4.23	1.59	1.54
	RP	2.53	0.67	0.59
	Coarseness	4.46	2.21	1.43
	Contrast (NGLDM) 26.22	3.98	5.99
	Busyness	-744.65	-4039.39	-202.31
	SZE	8.37	1.68	1.28
	LZE	12.21	11.87	17.87
	LGZE	0.22	0.28	0.31
	HGZE	0.22	0.27	0.25
	SZLGE	8.29	1.50	1.35
	SZHGE	8.46	1.91	1.44
	LZLGE	12.19	11.77	17.51
	LZHGE	12.24	11.94	18.14

  [START_REF] Bashir | Imaging heterogeneity in lung cancer: techniques, applications, and challenges[END_REF][START_REF] Buvat | Tumor texture analysis in PET: where do we stand?[END_REF] Fave et al.[START_REF] Fave | Can radiomic features be reproducibly measured from CBCT images for patients with non small cell lung can-cer?[END_REF] studied the reproducibility of test-retest cone beam CT images of 10 patients explored with 68 textural features (histogram, co-occurrence matrix, run length matrix, NGLDM) computed with Ibex software. They excluded 23 features because they were not reproducible on the test-retest images; unfortunately, with the exception of skewness derived from a histogram and contrast derived from the co-occurrence matrix, it is very difficult to compare their data with our study as we did not extract identical indices. Hunter et al.[START_REF] Hunter | High quality machine-robust image features: identification in non-small cell lung cancer computed tomogra-phy images[END_REF] studied the reproducibility of test-retest of unenhanced CT images of 56 patients with lung tumors using three different devices (15, 16, and 25 patients for each device) explored with 328 textural features calculated by Ibex software (including histogram, co-occurrence matrix and gray-level run length matrix). They found a high concordance correlation coefficient (>0.90) between the two acquisitions for 61.0%-94.5% of the features, and a high concordance correlation coefficient for 138 image features when interpolating the reproducible features of each device. Of them, they found 23 non redundant features, including kurtosis, skewness derived from the histogram, and entropy derived from the co-occurrence matrix according to our results. Balagurunathan et al.[START_REF] Balagurunathan | Reproducibility and Prognosis of Quantitative Features Extracted from CT Images[END_REF] studied the reproducibility of 219 3D textural indices derived from test-retest unenhanced CT scans of 32 patients with lung tumors and confirmed the robustness of contrast derived from the co-occurrence matrix. Moreover, this is the first study to assess the usefulness of indices for characterizing CT medical images on a dedicated phantom. Our study shows that some textural indices are unable to discriminate homogeneous from heterogeneous fea tures, likely rendering these indices unfeasible for use in clin ical evaluation. Taking into account that our experimental conditions did not evaluate the influence of the size of the ROI, some indices are unable to discriminate two heterogeneous ROIs.


L Cossonnet

E Fouhal

A Fourré

S B Henry

M Kim

A Mokili

G Olivier

Granier

N Baglan

• E Ansoborlo

• C Cossonnet

L Fouhal

E Fourré

• A Henry

S B Kim

M Mokili

A Olivier

G Granier

ASSESSMENT AND INTERPRETATION OF A ROUND-ROBIN EXERCISE FOR ORGANICALLY BOUND TRITIUM DETERMINATION

HAL is

INTRODUCTION

In the 1960s, tritium levels in the Northern Hemisphere's environment were closely related to the atmospheric testing of nuclear weapons. Since the nuclear test ban treaty, environmental tritium levels are slowly returning to their pre-bomb testing level. Therefore, the main anthropogenic sources are now related to nuclear facility releases into the surrounding environment. Tritium occurs in environmental samples in 3 forms: as tissue-free water tritium (TFWT) (i) and within living organisms, where it may label the organic matter either as exchangeable organically bound tritium (ii) or nonexchangeable organically bound tritium (iii). Component (i) is easily removed by evaporation at low temperature or by freeze-drying, and is in the form of tritiated water. Component (ii) tritium is exchangeable with the system's water to varying degrees, depending on the molecular structure. It is designated as exchangeable OBT (E-OBT). Type (iii) tritium is on the contrary tightly held by the organic structure, hence its designation as non-exchangeable (NE-OBT).

To monitor tritium bioaccumulation in environmental samples, the sensitivity and accuracy of the analytical procedure are of primary importance. When considering environmental samples, the tritium activity of several fractions can be measured. Namely, the total amount that can be divided between the free water and the OBT fraction, the latter possibly separated between exchangeable and non-exchangeable OBT [START_REF] Ware | Review of methods for the an alysis of total and or gani cally bou nd tritium[END_REF]. At present, regular legally required monitoring is carried out based on water and on environmental samples (mainly grass and milk) for which tissue-free water tritium (TFWT) is measured. However, no OBT assessments are included in these obligatory analyses, and very few intercomparison exercises have been organized since that of Atomic Energy Canada Limited (AECL) [START_REF] Workman | Interlaboratory comparison of organically bound tritiu m in environmental samples[END_REF]. Nevertheless, over the past few years, interest has been focused on OBT, which may be of greater radiological importance (Inoue et al. 1992;[START_REF] Diabate | Organically bound tritium[END_REF]Choi et al. 2005). Moreover, the data relative to OBT concentrations in environmental samples is scarce and difficult to compare, as the fractions measured could be different (Vichot et al. 2008). Indeed, to analyze any tritium fraction in an environmental sample, the suitable analytical procedure has to be used (Baglan et al. 2005). Such procedures are now employed by several laboratories in charge of environmental tritium monitoring or carrying out research on tritium behavior in the biosphere.

An intercomparison exercise was therefore prepared by the CETAMA to assess laboratories' capabilities for OBT determination. To avoid any misinterpretation of the results, all participating facilities were asked to determine the OBT activity in the sample delivered to them. The chosen matrix was grass that was sampled on the same day from a single location, prior to homogenization. The grass was then divided into 2 aliquots: (i) fresh, which was homogenized, sealed under vacuum, and sent to the analytical laboratories; and (ii) dry, which was processed in a single laboratory prior to being sent to the participants.

The results obtained are primary elements in further enhancing the accuracy of experimental data for OBT activities in environmental samples, and could be of importance in improving the input data used to compute tritium impact.

EXPERIMENTAL PROCEDURE

Participants

Eleven laboratories, numbered from 1 to 11, were registered for this exercise. Six returned data for the fresh aliquot, and 8 for the dry sample. Their results are hereafter marked as lab number-1 and lab number-2, respectively, for the fresh and dry aliquots.

Sampling and Packaging

The samples were collected inside a French reprocessing plant zone where tritium is handled during the reprocessing process. The sampling site was chosen by the site's environmental monitoring laboratory to ensure sufficient activity for intercomparison purposes. The chosen sample was grass, owing to the ease of sampling and also its availability.

A total amount of about 15 kg of grass was collected and divided into 2 aliquots (Figure 1) after homogenization. The fresh aliquot was sent directly to the participating laboratories after freezing, while the material for the dry sample was sent to a laboratory that carried out the drying prior to transfer to the other laboratories. The lyophilization device used to prepare the dry aliquot was a Heto Drywinner (THERMO Eletron Corporation).

Sample Analysis

All laboratories received 2 aliquots of the grass sample, 1 fresh and 1 dry. The general flowsheet used to perform the analysis is summarized in Figure 2. On reception, the fresh aliquot was dried by the participating laboratories. After the drying stage, the sample was prepared for measurement using a suitable analytical procedure. Measurement was mainly performed with liquid scintillation counting, as only 1 lab used 3 He detection via mass spectrometry. The drying stage was performed by lyophilization. The total removal of water was confirmed by weighing residual samples. Special attention was given to the washing of the freeze-drying chambers, and tests were performed to ensure the absence of contamination. Then, depending of the analytical tool, the dry sample was burned in a special oven (i) or sealed in a glass vial after helium purging (ii). The principle of the combustion apparatus has already been described in the literature [START_REF] Sokal | Tritium in pine needles and its significant sources in the environ ment[END_REF][START_REF] Pointurier | A method for determination of low level organic bound tritiu m activities in environ mental samp les[END_REF]. A quartz tube goes through the 2 heating zones of the oven (Figure 3). In the first zone, the sample is held in a quartz vessel containing 5 to 50 g of dry sample, depending on the tube diameter. The part of the quartz tube that is inside the second heating zone contains catalyst to oxidize combustion gases. The temperatures of both zones are independently adjustable, and the 1st heating zone is mobile. The quartz vessel containing the sample is placed just before the 1st heating part, so that the leading edge of the sample is heated by the furnace. When the sample starts to ignite, a flame appears at the extremity of the sample and the heating unit is moved towards the sample so that the flame moves slowly across the whole sample. The 2nd heating zone is fixed and held at a constant temperature of 850 ± 20 C. The combustion gases, swept over the catalyst by a stream of pure oxygen, are oxidized by the catalyst and the water vapor trapped in the cold trap. Only a small part of the water vapor escapes from the trap and is absorbed by a desiccant trap at the cold trap exit. At the end of combustion, the glassware containing the frozen combustion water is removed from the combustion apparatus and immediately closed with appropriate caps. However, the combustion water is not directly suitable for liquid scintillation counting due to its acidity and the various impurities also collected in the cold trap. Therefore, the water was neutralized and a distillation performed under atmospheric pressure using conventional laboratory glassware.

For the combustion step, an alternative to the combustion oven is to employ an oxidizer. In this case, a very small amount of sample (<1 g) is placed in the combustion cone prior to introduction into the oxidizer. After combustion, the water recovered is condensed and added automatically to the mixture. The combustion yield is estimated under the same conditions, using a tritium standard.

The main drawback of this technique lies in the low amount usable (<1 g), which leads to high detection limits not suited for environmental purposes. However, it exhibits a strong potential as a screening tool due to its rapidity.

Tritium determination in combustion water is performed through liquid scintillation counting, a wellestablished and documented technique [START_REF] Birks | The Theory and Practice of Scintillation Counting[END_REF][START_REF] Horocks | Applications of Liquid Scintillation Counting[END_REF], using the following equation:

n -n 0  A b = --------------------- (1)
where b is the mass (kg) or the volume (L) of combustion water in the counting vials; n is the counting rate for the sample (cpm); n0 is the counting rate for the associate blank sample (cpm); and  is the counting yield (dimensionless number between 0 and 1).

The relative expanded uncertainty in the result is given with a coverage factor of 2:

u c A b  ----------------= 2 A b (2) n --+ ----- n 0 t t 0 +  ---- c -------- + ---- c -----  u b 2 u  2  n b   -------- n 0  2      t t 0   % H water A d A f
where t is counting time for the sample (min) and t0 is the counting time for the associated blank sample (min).

For environmental samples, the values obtained in Bq/kg or L of combustion water have to be converted into Bq/kg sample (dry or fresh). For OBT, the equations are:

A OBT A b  % H sample   and d = -----  -------------------------------------for the dry material (3) OBT A O BT  m A  % sample  m A f = --- d -------------------- d -= ---- b ----------------- H ------------------------------------- d -for the fresh material (4) m f % H water  m f
where A OBT is the activity of the OBT per kg of dry sample; A OBT the activity of the OBT per kg

d f
of fresh sample; mf is the fresh sample mass; and md is the dry sample mass.

The expanded uncertainty on the result is given with a coverage factor of 2:

U OBT d = 2  A OBT 
(5)

U OBT f = 2  A OBT  (6)
These equations were used by the participants measuring tritium by liquid scintillation during the exercise.

Tritium was measured using the helium-3 ingrowth method (developed by [START_REF] Clarke | Determination of tritiu m by mass spectrometric measurement of 3 He [END_REF]) for water samples. The principle of the method is to remove the atmospheric 3 He initially present in the sample by degassing under high vacuum, and then to store it in a closed container (usually a low Hediffusivity glass bulb) to allow for the accumulation of tritiumgenic 3 He. The tritium content of the sample is subsequently deduced from the mass spectrometric determination of the amount of 3 He produced during the storage time. Since helium diffusivity is very high in organic compounds, allowing the tritiumgenic 3 He to diffuse out of the biological tissues and to accumulate in the storage container, there is no obstacle to applying this highly sensitive technique for measuring tritium to environmental samples. The samples were analyzed using the procedure detailed by Jean-Baptiste et al. (1992). Briefly, each sample is put in a Corning 1724 glass bulb and pumped down to 10 5 Torr.

Then, the bulb is flame-sealed and stored at 20 °C in a freezer for 3 He ingrowths. Although Corning 1724 glass was selected for its low helium diffusivity, the low temperature is intended to further reduce 3 He diffusion through the glass wall during storage. After a sufficient storage time, the bulb is placed on the inlet line of a MAP 215-50 noble gas mass spectrometer and reopened by breaking its tip with a special pneumatic actuator. 3 He  ions are detected on an electron multiplier operated in the pulse mode. The 3 He blank (<10 19 mol), which is almost entirely due to the residual helium dissolved in the sample after pumping, is deduced from the measurement of 4 He applying the atmospheric helium isotopic ratio: 3 He/ 4 He  1.38 × 10 6 .

 u A  2  -------b  + ----------H ---sa -m -p -l -e - + ----------H --w --a -te -r -  % u   2  % u   2  A b   % H sample  % H sample  u A   u m  2  u m  2 2 -------d  + -------f  +  ------d   A d   m f   m d  2 2 m
The T/H value of the sample is deduced from the measured amount of tritiumgenic 3 He (in mol) following the formula: ----------------------------------------------------(7)

T 3 He ---= -
H m  H  1 -e -t 
where m is the mass of the sample (in kg), [H] is its hydrogen content (in mol/kg),  is the decay constant of tritium, and t is the storage time. The hydrogen content of each sample was measured on a CHN analyzer at the Micro-analysis Laboratory (CNRS/ICSN, Gif sur Yvette). All measurements were made against an atmospheric air standard ( 4 He  5.24 ppm, 3 He/ 4 He  1.38 × 10 6 ). Overall relative uncertainty on the tritium determination was better than 5.

Statistical Processing Methods

The statistical tests classically used to interpret the results of a round-robin refer to a population meeting the criteria of a normal distribution defined by the mean and the variance. To check this specific point, the Shapiro-Wilk test was used for both aliquots. However, the insufficient number of measurements and/or the presence of abnormal values reported in this round-robin led to the use of robust statistical methods for which no hypothesis concerning the distribution is necessary. These methods take into account all the results, including those considered abnormal according to normal distribution criteria: none of the results are disregarded in calculating the mean and standard deviation. These methods based on calculation algorithms described in standards ISO 5725-6 and ISO/ DIS 13528 allow the determination of the robust means, and of the reproducibility and repeatability standard deviations. The objective of the following calculations is to estimate the standard deviations of repeatability (sr) and reproducibility (sR). Repeatability implies that the measurements are performed by the same operator with the same instrument using a single method within a short time period to obtain results under conditions as similar as possible. Reproducibility conditions occur when any of these conditions changes: operator, instrument, method variant, time frame, or any other cause of variability. The results obtained can therefore be used to calculate the standard deviation of repeatability (or intralaboratory reproducibility), which measures the scattering of the results obtained in 5 independent tests by each laboratory, and the standard deviation of interlaboratory reproducibility, which measures the scattering of the mean results obtained by all the laboratories.

To compare the results obtained for the 2 aliquots, a general approach was adopted. This meant first checking for outliers (Grubbs, Dixon), discarding results that were not obtained under repeatabiltity conditions within a single laboratory, homoscedasticity (Fisher), then verifying the null hypothesis: that the mean values for each aliquot are equal (Student). Moreover, the repeatability (r  k sr) and reproducibility (R  k sR) limits were determined.

RESULTS AND DISCUSSION

Depending on their analytical means, the various laboratories used experimental or tabulated values for the hydrogen percentage in the sample. The mean experimental value, Hm  6.2 ± 0.2 is characterized by a scattering low enough to have only a very slight impact on the activity values determined.

Among the data given by the facilities for the 2 aliquots, the mean and standard deviation associated with the fresh to dry mass ratio (Rm) and the water content (W) were determined: R fresh  3.41 ± 0.06 and Rm dry  3.47 ± 0.12; W fresh  70.64 ± 0.55 and W dry  71.01 ± 1.30 The values thus obtained are consistent, and also with those known for grass samples.

Moreover, 2 guideline values, the ratio for the combustion water recovered from the burned dry sample compared to the combustion yield were followed throughout the combustion stage to assess any departure from standard conditions. For all the participating laboratories, these values remained normal, i.e. around 50 for the combustion water to burned sample ratio and 90 for the combustion yield.

The consistency of all these technical parameters for all the different participants showed that the analytical tools are efficient. For both aliquots, the activity results are only given for the combustion water and the dry material, as those for the fresh material were derived from the previous one (Equation 4). Moreover, the main statistical results including consensus values (NF ISO 13528) are summarized for the various fractions.

Fresh Aliquot

The treatment to perform OBT analysis is more tedious for this aliquot, which led to a lower participation with only 4 facilities giving results for the combustion water (Figure 4) and 6 for the dry material (Figure 5). The results reported for the combustion water are close to the consensus value and lie within the reproducibility limit (17 to 17). The results reported for the dry mass are also close to the consensus value and most lie within the reproducibility limit (19 to 19). The larger interval in terms of reproducibility may originate from:

• The use by laboratory 6 of a hydrogen percentage value from the literature lower than that actually observed in the sample, which would explain the relative position of the laboratory mean regarding the consensus value for the 2 fractions; • The presence of a new laboratory whose results exhibit higher scattering due to the analysis of very low amounts, leading to an increased influence of the counting statistic.

Figure 5 OBT activity in Bq kg 1 of dry material for the fresh aliquot

Dry Aliquot

The treatment necessary to perform OBT analysis is shorter for this aliquot; therefore, 6 laboratories gave results for the combustion water (Figure 6) and 8 for the dry material (Figure 7). The results reported for the combustion water are close to the consensus values and lie within the reproducibility limit (19 to 19). However, the scattering of the values is larger than that observed for the fresh aliquot. Two hypotheses could explain this behavior: the use of the robust method, in which outliers are not rejected; and the fact that 1 laboratory did not meet the repeatability criteria for the 5 replicates that were used.

The results reported for the dry mass are also close to the consensus value and most lie within the reproducibility limit (22 to 22). The origins of the wider interval in term of the reproducibility may combine the reasons given for the fresh aliquot on the dry material and those previously explained regarding combustion water.

DISCUSSION

All the results are summarized for both aliquots (Table 1) of the grass sample. At first glance, the observed means using robust methods seem to be lower for the dry aliquots. However, it is difficult to reach straightforward conclusions, as for the various aliquots the results are always found within the reproducibility limits. Under these conditions, it is obvious that the scattering of the results would be further reduced by sampling larger amounts to avoid any counting statistic impact, both by limiting the influence of environmental conditions on the value of the hydrogen percentage and by decreasing interlaboratory scattering. Nevertheless, the values obtained by all the laboratories meet the reproducibility criteria, underlining the progress made since the first exercise, when the reproducibility standard deviations were between 50 and 80.

However, as both aliquots came from the same grass sample, the results obtained should be identical. To check the H0 hypothesis, i.e. that the determined means are identical for both aliquots, suitable statistical tests were performed. The means determined for the 2 aliquots were compared for the different laboratories, using only the values obtained under repeatability conditions within each facility. As the values in Bq kg 1 of fresh material were derived from those of dry material (Equation 4) by a simple mass ratio, only the activities in Bq kg 1 of combustion water (i) and dry material (ii) were taken into account: For the combustion water, both experimental values were lower than the discriminating value, which demonstrates the homogeneity of the variance for a risk chosen equal to 5, and confirm the H0 hypothesis. For the dry grass, the experimental value is lower than the discriminating value, leading to the same conclusion. These results confirm that the drying stage was performed well by the laboratories. However, the hydrogen percentage value can play a role, especially when literature data are used. Indeed, very little data is available, which means it is not completely representative of environmental variability. Therefore, experimental assessment of this important parameter is recommended.

As both sets of values are identical, a consensus value for the grass sample itself could be obtained by calculating the weighted mean and the associated uncertainty for each fraction (Table 3). Nevertheless, to prepare certified reference material, the following are needed:

• Larger samples, as 15 kg is only just enough for the purpose of such a round-robin exercise;

• A guarantee of sample stability over a time period long enough to ensure the usefulness of such material; rather difficult criteria for 3 H.

It is known that isotopic exchange with hydrogen present in the atmosphere can modify the amount of tritium in the sample [START_REF] Belot | Le tritium de l'environne m ent à l'hom m e[END_REF], even when it is stored in a freezer (where the exchange is very much slower, but not halted).

CONCLUSION

In order to assess and improve the performances of several laboratories dealing with OBT determination, an intercomparison exercise was organized by the CETAMA. The results obtained are very encouraging, as most of the differences observed can be explained by variations in the experimental conditions. Nevertheless, the scattering observed for each facility is consistent with this round-robin repeatability limit, and with the reproducibility limit when considering all the values. Therefore, consensus values are proposed for the dry and fresh aliquots. As they came from the same grass sample, the determined mean activities should be identical. This hypothesis was assessed using suitable statistical tests, which allowed a consensus value to be proposed for each fraction, namely 274 ± 12 Bq kg 1 for the combustion water, 157 ± 7 Bq kg 1 for the dry grass, and 46 ± 4 Bq kg 1 for the fresh material.

The results confirm that several stages of the process, such as the drying and the measurement, are performed well by the participating laboratories. However, the use of literature data for the hydrogen percentage employed to convert activity values from Bq kg 1 for the combustion water to Bq kg 1 of dry material may have a negative influence on the activity results. Indeed, environmental variations are not correctly matched using this data, and an experimental determination is therefore recommended.

As part of its objective regarding validated analytical procedures for OBT determination, the CETAMA will organize a new exercise next year. Simultaneously, a tentative assessment of the mathematical relation Ttotal  HTO  OBT will be performed. From these exercises, an analytical procedure for OBT and NE-OBT determination will be proposed for validation, to obtain environmental Certified Reference Materials for future OBT determination.

Figure 1

 1 Figure 1 Sample pretreatment

Figure 3

 3 Figure 3 Co mbustion oven. A: dry and pure o xygen flu x; B: quartz tube; C: sample; D: first heating unit (mobile); E: co mbustion gases; F: second h eating unit (fixed); G: catalyst with quartz bead; H: "U" tube for water vapor trapping; I: co ld trap; J: desiccant; K: controllers.

Figure 4

 4 Figure 4 OBT activity in Bq L 1 of combustion water for the fresh aliquot

Figure 6

 6 Figure 6 OBT activity in Bq L 1 of combustion water for the dry aliquot

Table 1

 1 Round-robin summary. The population is small (n < 30); therefore, the homoscedasticity of the variance (F test) was checked prior to carrying out a t test, to verify that the means were equal. (ii) The population is large enough to do the t test directly.The experimental values were then compared to that found in the literature (Neuilly 1999; Sokal and Rohlf 1998) (Table2).

		Fresh aliquot			Dry aliquot
		Combustion	Dry	Fresh	Combustion	Dry	Fresh
	Activity	water	material	material	water	material	material
	per kg or L	(Bq L 1 )	(Bq kg 1 )	(Bq kg 1 )	(Bq L 1 )	(Bq kg 1 )	(Bq kg 1 )
	Number of	4	6	6	6	8	8
	participating
	labs
	Data	20	30	30	26	35	35
	Grand mean	282.8	163.1	47.7	266.5	151.8	43.8
	sr	6.7	4.7	1.6	10.1	6.4	2.0
	sR	27.4	18.1	4.5	24.6	17.1	4.1
	r	18.9	18.1	4.4	28.6	13.3	5.5
	R	78.6	48.4	12.7	69.6	51.2	11.7
	consensus value 283 ± 16	163 ± 8	48 ± 4	267 ± 9	152 ± 6	44 ± 4
	(i)

Table 2

 2 Mean comparison.

			Experimental Discriminating
		Test	value	value (  5)	Conclusions
	Bq kg 1 of combustion	Fisher	0.85	2.08	Homogeneity of
	water				the variance
	Bq kg 1 of combustion	Student	1.74	2.02	Identical mean
	water
	Bq kg 1 of dry material Student	1.91	2.00	Identical mean

Table 3

 3 Consensus values and associated uncertainties (k  2) for the analyzed grass.

	Fraction	Activity (Bq kg 1 )	Uncertainty (Bq kg 1 )
	Combustion water	274	12
	Dry material	157	7
	Fresh material	46	4