Catherine Feliu

Celine Konecki

Laurent Binet

Damien Vautier

Cyril Haudecoeur

Olivier Oget

Aurelie Fouley

Hélène Marty

Claire Gozalo

Yoann Cazaubon

Zoubir Djerada
email: zoubir.djerada@univ-reims.frzdjerada@chu-reims.fr

Dr Zoubir Djerada Pharmd

Quantification of methadone, buprenorphine, naloxone, opioids, and their derivates in whole blood by liquid chromatography-high-resolution mass spectrometry: Analysis of their involvement in fatal forensic cases

Keywords: high-resolution mass spectrometry, opioids, toxicological analysis, fatal forensic cases analysis

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

Introduction

Opioids represent a large family of compounds that can be used in several indications: analgesics, antitussives, opioid substitution therapy (e. g. methadone, buprenorphine...).

When these molecules are misused, it is often addictive. In the case of overuse, the clinical consequences associated with opioid intoxication or overdose will be observed: respiratory depression, reduced heart rate, drowsiness, myosis, coma, and possible death. For the past 15 years, the United States and Europe have been confronted with an ever-increasing opiate crisis [START_REF] Rutkow | Emergency Legal Authority and the Opioid Crisis[END_REF][START_REF] Koenig | The Opioid Crisis in America: Too much, too little, too late[END_REF][3]. The number of deaths due to opioid overdose in the United States has continued to increase, with more than 50,000 deaths reported in 2016 [START_REF]Overdose Death Rates[END_REF]. In Europe, opioids are present in 81% of cases of fatal overdose [3]. Heroin or its metabolites are implicated in the majority of declared fatal overdoses, frequently in combination with other substances. Other opioids, mainly methadone and high-dose buprenorphine, are also often reported in toxicological reports of fatal overdoses [START_REF] Rutkow | Emergency Legal Authority and the Opioid Crisis[END_REF][START_REF] Koenig | The Opioid Crisis in America: Too much, too little, too late[END_REF][3].

According to the most up-to-date data, methadone-related deaths have exceeded heroinrelated deaths in Croatia, Denmark, France, and Ireland [3]. In response to this global public health crisis, many countries have been working on their health policies, mainly to improve prescription regulation and limit the misuse of these substances. The quantification of opioids and their derivatives in biological fluids is of considerable importance in clinical and forensic toxicology in many situations such as driving under the influence, understanding the cause of death, research into doping agents or chemical submission [START_REF] Mura | Drogues et accidentalité[END_REF].

Different biological matrices can be used for the analysis of opioids and derivatives [START_REF] Baselt | Disposition of toxic drugs and chemicals in man[END_REF][START_REF] Feliu | Clinical and analytical toxicology of opiate, cocaine and amphetamine[END_REF][START_REF] Samanidou | Novel strategies for sample preparation in forensic toxicology[END_REF].

Whole blood is the matrix of choice for the quantification of illicit drugs [START_REF] Mura | Drogues et accidentalité[END_REF][START_REF] Feliu | Clinical and analytical toxicology of opiate, cocaine and amphetamine[END_REF]. As blood concentrations can be very low, analytical methods must be very sensitive. The recommended quantitation limits are between 5 and 10 µg/L for all these types of analytes in the blood [START_REF] Mura | Drogues et accidentalité[END_REF]. In the 1990s, opioid quantification was performed by gas chromatography coupled with mass spectrometry (GC-MS) [START_REF] Gaillard | Identification and quantification of benzoylecgonine, cocaine, methylecgonine-ester, codeine, morphine and 6-acetylmorphine in whole blood[END_REF]. In recent years, thanks to the improvement of technology in terms of specificity, precision, and sensitivity, liquid chromatography coupled with mass spectrometry has replaced the "old gold standard", GC-MS [START_REF] Peters | Recent advances of liquid chromatography-(tandem) mass spectrometry in clinical and forensic toxicology[END_REF]. LC-MS is widely used in clinical and forensic toxicology for screening and quantification of drugs in different complex biological matrices [START_REF] Viaene | Comparison of a triple-quadrupole and a quadrupole time-of-flight mass analyzer to quantify 16 opioids in human plasma[END_REF][START_REF] Rosano | Multi-drug and metabolite quantification in postmortem blood by liquid chromatography-high-resolution mass spectrometry: comparison with nominal mass technology[END_REF][START_REF] Feliu | Validation of a fast UPLC-MS/MS method for quantitative analysis of opioids, cocaine, amphetamines (and their derivatives) in human whole blood[END_REF][START_REF] Ahsman | Quantification of midazolam, morphine and metabolites in plasma using 96-well solid-phase extraction and ultra-performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Al-Asmari | Method for quantification of opioids and their metabolites in autopsy blood by liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Bjørk | Determination of 19 drugs of abuse and metabolites in whole blood by high-performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Christoffersen | Quantification of morphine, morphine 6-glucuronide, buprenorphine, and the enantiomers of methadone by enantioselective mass spectrometric chromatography in whole blood[END_REF][START_REF] Coles | Simultaneous determination of codeine, morphine, hydrocodone, hydromorphone, oxycodone, and 6acetylmorphine in urine, serum, plasma, whole blood, and meconium by LC-MS-MS[END_REF][START_REF] Eckart | Development of a new multi-analyte assay for the simultaneous detection of opioids in serum and other body fluids using liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Fernandez | Quantitative analysis of 26 opioids, cocaine, and their metabolites in human blood by ultra performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Moreno-Vicente | Development and validation of a bioanalytical method for the simultaneous determination of heroin, its main metabolites, naloxone and naltrexone by LC-MS/MS in human plasma samples: Application to a clinical trial of oral administration of a heroin/naloxone formulation[END_REF][START_REF] Sartori | The development of a high-performance liquid chromatography-tandem mass spectrometric method for simultaneous quantification of morphine, morphine-3-β-glucuronide, morphine-6-β-glucuronide, hydromorphone, and normorphine in serum[END_REF][START_REF] Dawson | A rapid and sensitive highperformance liquid chromatography-electrospray ionization-triple quadrupole mass spectrometry method for the quantitation of oxycodone in human plasma[END_REF][START_REF] Ghassabian | Highthroughput assay for simultaneous quantification of the plasma concentrations of morphine, fentanyl, midazolam and their major metabolites using automated SPE coupled to LC-MS/MS[END_REF]. In recent years, a new physical and analytical approach using high-resolution mass spectrometry (HRMS) has emerged in toxicology laboratories [START_REF] Maurer | Perspectives of liquid chromatography coupled to low-and high-resolution mass spectrometry for screening, identification, and quantification of drugs in clinical and forensic toxicology[END_REF][START_REF] Jiwan | HPLC-high resolution mass spectrometry in clinical laboratory?[END_REF][START_REF] Ojanperä | Isotopic pattern and accurate mass determination in urine drug screening by liquid chromatography/timeof-flight mass spectrometry[END_REF][START_REF] Wu | Role of liquid chromatography-high-resolution mass spectrometry (LC-HR/MS) in clinical toxicology[END_REF][START_REF] Maurer | What is the future of (ultra) high performance liquid chromatography coupled to low and high resolution mass spectrometry for toxicological drug screening?[END_REF][START_REF] Meyer | Current position of high-resolution MS for drug quantification in clinical & forensic toxicology[END_REF][START_REF] Maurer | High-resolution mass spectrometry in toxicology: current status and future perspectives[END_REF]. Thanks to this technology, the identification, and quantification of compounds were based on the precise measurement of mass and fragment ions. The objective of this study was, therefore, to develop and validate a rapid, sensitive and quantitative method for the analysis of 16 opioids and derivatives: 6monoacetylmorphine (6-MAM), buprenorphine, codeine, dihydrocodeine, 2-ethyl-1,5dimethyl-3,3-diphenylpyrrolidine (EDDP), ethylmorphine, heroin, methadone, morphine, nalbuphine, naloxone, norbuprenorphine, norcodeine, norpoxyphene, oxycodone and propoxyphene in high-performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS).

Materials and methods

LC-MS analysis

Chemicals

All reference standards and their internal standards (IS) were purchased from Cerilliant (Round Rock, Texas, USA). Acetonitrile, methanol, formic acid, and water, all hypergrade LC-MS for mobile phase, were obtained from Biosolve (Dieuze, France). Zinc sulfate and ammonium formate were obtained from Sigma-Aldrich (Saint-Louis, Missouri, USA).

Whole blood from healthy donors was purchased from the Etablissement Français du Sang (EFS, Reims, France).

Chromatographic and mass-spectrometric conditions

An ultra-high performance liquid chromatography system with a Dionex Ultimate 3000 high-pressure pump (ThermoFisher Scientific, San Jose, USA) coupled with a quadrupole hybrid mass spectrometer QExactive Orbitrap (ThermoFisher Scientific, San Jose, USA) was used to develop and validate this method.

Chromatographic separation was achieved using a UHPLC Waters Acquity HSS T3 1.8 μm (2.1 × 50 mm) column (Waters Corp; Milford, MA, USA), maintained at 50°C. The mobile phases were composed of water + formic acid 0.1% (V/V) (mobile phase A) and acetonitrile + formic acid 0.1% (V/V) (mobile phase B). A programmed mobile-phase gradient was used at a flow rate of 0.5 mL/ min during 5 min of run: 0 min, 3% B, following concave curve; 3 min, 95% B; 4 min, 95% B, following concave curve; 4.3 min, 3% B following linear curve. The analysis and acquisition time were 5.10 minutes.

Detector coupling was performed using a heated electrospray ionization source (HESI) operating in positive ionization mode, with the following settings: sheath gas at 45 arbitrary units (AU), auxiliary gas at 15 AU, sweep gas flow rate at 1 AU, spray voltage at 3.50 kV, ion transfer capillary temperature at 300 °C, S lens RF level at 70 AU and heating temperature at 350 °C. The acquisition was performed using a parallel reaction monitoring mode (PRM). The QExactive Orbitrap mass spectrometer can acquire masse range from 50 to 2000 m/z. The execution acquisition was from 0 to 5.25 min. The settings for the acquisition were as follows: high resolution from 17,500 (resolution was calculated at full width at maximum mid-height at m/z 200); target 1 e5 of the automatic gain control (AGC); maximum injection time (IT) 50 ms. Collision energies have been optimized for each analyte. Two PRM transitions were selected: the first for quantification and the second for analytes confirmation (Table 1).

Mass calibration was performed once a week in a positive and negative mode according to the manufacturer's recommendations, using an external calibration solution (Pierce®, ThermoScientific, San Jose, USA). TraceFinder Forensic 3.3 was used for LC-MS monitoring, library management, acquisition, and processing.

Preparation of stock solutions, calibration standards and quality control samples

These solutions were stored for one month at -20 °C. To prepare the calibration standards, a working solution (higher calibrator) was prepared with an appropriate volume of methanol and then diluted in series by half. Calibration standards ranged from 0.98 to 1000 µg/L except for buprenorphine (0.39-100 µg/L) and norbuprenorphine (0.20-100 µg/L).

Ten microlitres of each dilution were then added to 100 µL of whole blank blood and treated as a patient. Quality controls (QC) were prepared in our laboratory at different concentrations corresponding to 3 times LLOQ (low level, QCL), 40% (medium level, MCQ) and 80% (high level, QCH) of the highest standards. These quality control samples were freshly prepared before analysis with stock solutions different from those used for standard preparation. Besides, four internal quality controls (BTMF A, BTMF B, BTMF C, and STM A) were purchased from ACQ Science (Rottenburg-Hailfingen, Germany) (supplemental table 1). Internal quality controls were prepared in accordance with the manufacturer's instructions.

Sample processing

A stock solution was prepared in acetonitrile, containing all analytes at a final concentration of 10 mg/L, except buprenorphine and norbuprenorphine at 1 mg/L. An internal standard solution was prepared under the same conditions to obtain a final concentration of 1 mg/L each. Ten microlitres of the internal standard solution were added to 100 µL of the whole blood sample. Deproteinization with 100 µL of 5% zinc sulfate (w/v), 100 µL of methanol and 200 µL of acetonitrile was performed. After 60 seconds of vortex mixing, the sample was centrifuged at 10,000 g for 5 minutes. The supernatant (150 µL) was evaporated under nitrogen at 40°C. The dry extract was reconstituted with 100 μL of water (LC-MS hypergrade) containing 0.1% (V/V) formic acid. Twenty microlitres of this extract were injected into the UHPLC-HRMS system.

Validation procedure

Validation was conducted in accordance with international recommendations [START_REF]Guideline on bioanalytical method validation[END_REF][START_REF]Bioanalytical method validation[END_REF][START_REF] Matuszewski | Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS[END_REF].

Linearity, precision, accuracy, selectivity, matrix effect (ME), carry-over and stability were assessed as previously described [START_REF] Djerada | Validation of a fast method for quantitative analysis of elvitegravir, raltegravir, maraviroc, etravirine, tenofovir, boceprevir and 10 other antiretroviral agents in human plasma samples with a new UPLC-MS/MS technology[END_REF].

Linearity

Following the guidelines, the lower limit of quantification (LLOQ) was determined for each analyte as the lowest concentration for which the accuracy was between 80% and 120% and the precision below 20% (n = 6). The upper limit of quantification (ULOQ) was also determined as the high concentration with an accuracy between 80 % and 120 % and precision below 20%. Different weighting functions were tested in 10 calibration ranges for each analyte to select the regression calibration (linear, 1 / X, 1 / X², 1 / Y ...). The accuracy of quality controls, the analysis of the distribution of deviations from the prediction of regression or residual value (Gaussian distribution verified by the D'Agostino Pearson test) and the accuracy of the standards to ±15% (±20% at LLOQ) of the nominal concentration were criteria for determining the regression model [START_REF] Djerada | Validation of a fast method for quantitative analysis of elvitegravir, raltegravir, maraviroc, etravirine, tenofovir, boceprevir and 10 other antiretroviral agents in human plasma samples with a new UPLC-MS/MS technology[END_REF].

Precision and accuracy

Three QC prepared in our laboratory (QCL, QCM, QCH) and four levels of internal quality controls (BTMF A, BTMF B, BTMF C and STM A purchased from ACQ Science, Supplemental Table 1) were used to evaluate precision and accuracy of the method.

Within-run and between-run accuracy and precision were assessed by analyzing 10 samples per level of control. Precision was expressed as the relative standard deviation and had to be within ±15%. For accuracy, the mean concentration had to be within ±15% from target concentration at each tested level of control.

Selectivity

Six whole blood samples from donors were pre-treated and analyzed individually as blanks to investigate interferences. As recommended, the absence of interfering components was accepted when the blanks' responses were lower than 20% of the LLOQ for the analytes and 5 % for the corresponding internal standard [START_REF]Guideline on bioanalytical method validation[END_REF][START_REF]Bioanalytical method validation[END_REF].

Matrix effect

The matrix effect (ME) was evaluated with six whole blood samples from different sources, according to Matuszewsky et al. [START_REF] Matuszewski | Strategies for the assessment of matrix effect in quantitative bioanalytical methods based on HPLC-MS/MS[END_REF]. Two sets of the matrices were spiked after extraction with internal standards and all the analytes at a low or a high concentration: 2.94 µg/L or 800 µg/L respectively for all analytes, except for buprenorphine (1.17 µg/L or 80 µg/L) and norbuprenorphine (0.6 µg/L or 80 µg/L).

The matrix effect factor was calculated by comparing the area under the peak derived from the matrix spiked after extraction and the area under the peak of a pure solution at the same concentration. The normalized factor of the matrix effect is defined for each matrix and analyte by comparing the matrix factor of the analyte and the matrix factor of the appropriate internal standard. The relative standard deviation of the normalized factors must be less than 20%.

Stability

The stability of illicit drugs in whole blood has been described in the literature [START_REF] Mura | Drogues et accidentalité[END_REF][START_REF] Baselt | Disposition of toxic drugs and chemicals in man[END_REF][START_REF] Høiseth | Long-term stability of morphine, codeine, and 6-acetylmorphine in real-life whole blood samples, stored at -20°C[END_REF][START_REF] Karinen | Long-Term Storage of Authentic Postmortem Forensic Blood Samples at -20°C: Measured Concentrations of Benzodiazepines, Central Stimulants, Opioids and Certain Medicinal Drugs Before and After Storage for 16-18 Years[END_REF][START_REF] Drummer | Postmortem toxicology of drugs of abuse[END_REF].

Opiates, morphine and codeine are stable at -20°C even for several years [START_REF] Høiseth | Long-term stability of morphine, codeine, and 6-acetylmorphine in real-life whole blood samples, stored at -20°C[END_REF]. Under the same storage conditions, 6-MAM degrades more quickly [START_REF] Høiseth | Long-term stability of morphine, codeine, and 6-acetylmorphine in real-life whole blood samples, stored at -20°C[END_REF].

The stability of the solution of the calibration standards and the IS solution was evaluated by comparing 1-month solutions and freshly prepared solutions.

The stability of ACQ science internal controls was evaluated over 3 months by comparing controls stored at -20°C for 3 months and freshly reconstituted control.

Post-preparation stability was assessed by keeping the treated samples (STDs) placed in glass vials in the autosampler (+10°C) for 24 hours.

Carry-over effects

The carry-over was evaluated by analyzing blank samples (n=6) immediately after the highest concentration standard. As recommended, the signal should be less than 20% of the LLOQ and less than 5% of the internal standard.

Forensic fatal cases review

A two-year review of fatal forensic cases was conducted in our laboratory. The samples were analyzed using this analytical method. The involvement of opioids or their derivatives has been studied. Other compounds that may increase toxicity have also been reported.

Statistical analysis

GraphPad Prism 5.0 (GraphPad Software, San Diego, CA, USA) was used for statistical analysis.

Results and discussion

Optimization of the method

In a previous study, extraction procedure was performed with chloroform: ethyl acetate 50/50 (v/v) and 20 μL of sodium carbonate buffer solution (20%, w/v) at pH 11, and optimized to ensure enough signals for opioids and in particular morphine [START_REF] Feliu | Validation of a fast UPLC-MS/MS method for quantitative analysis of opioids, cocaine, amphetamines (and their derivatives) in human whole blood[END_REF]. This extraction was then assayed for the determination of other opioids and was not enough for buprenorphine, methadone and their metabolites. Deproteinization using 100 µL of 5% zinc sulfate (w/v), 100 µL of methanol and 200 µL of acetonitrile was performed. This procedure was chosen because it allowed obtaining satisfactory signals for all compounds.

Column choice, curvilinear gradient and chromatographic parameters were optimized as described [START_REF] Feliu | Validation of a fast UPLC-MS/MS method for quantitative analysis of opioids, cocaine, amphetamines (and their derivatives) in human whole blood[END_REF]. After that, different mass spectrometry acquisition modes were tested: full scan and parallel reaction monitoring (PRM). Full scan mode was unable to discriminate isomeric compounds (morphine/norcodeine and 6-MAM/naloxone), so PRM mode was chosen. For each compound, the energy of collision was optimized, and two fragment ions were selected, the first one for the quantification and the second one for the confirmation of the analytes (Table 1). Figure 1 depicts the reconstructed chromatogram of standard 6 for all analytes.

Validation of the method

Linearity, precision and accuracy

For all analytes, quadratic regression (Y = ax² + bx) without weighting satisfied all the predefined criteria [START_REF] Djerada | Validation of a fast method for quantitative analysis of elvitegravir, raltegravir, maraviroc, etravirine, tenofovir, boceprevir and 10 other antiretroviral agents in human plasma samples with a new UPLC-MS/MS technology[END_REF]. Within the considered concentration range, regression coefficient (r 2 ) of the calibration curves were always higher than 0.997 (n = 10) with back-calculated concentrations for the calibration samples within ± 15 % (± 20 % at LLOQ) of nominal concentration.

The precision and accuracy (n = 6) of the LLOQ and ULOQ for each analyte were within the recommended limits (Table 2). The relative standard deviations of quality controls (commercial QC Table 3, home-made QC table 4) were comprised between 0.87 and 14.98% for both intra-and inter-assay precision (n = 10) and were within acceptance criteria [START_REF] Drummer | Postmortem toxicology of drugs of abuse[END_REF][START_REF] Makarov | Mass spectrometer[END_REF]. Evaluation of the accuracy of the quality controls showed a relative standard deviation (n = 10) less than ± 15% (85.01% -109.96%) from target concentration at each tested level (Table 3, 4) except for 6-MAM and EDDP in commercial quality control. For both compounds, accuracy for intra-and inter-assay was less than 85%, but the result was still in the confidence range of the quality control with exact precision.

Specificity and selectivity

Analysis of 6 different blank whole blood samples did not show any interference at the retention time windows for each specified PRM. For each sample, the response was less than 20% of the lower limit of quantification for analytes and 5% for internal standards.

Supplemental figure 1 represents overlapped chromatograms of LLOQ and blank samples for all compounds.

Matrix effect

Matrix factor (MF) and the internal standard normalized MF ranged from 0.8 to 1.85 (Supplemental Table 2). As recommended, the relative standard deviation of normalized matrix factors of each analyte was below 15% (n = 6) [START_REF]Guideline on bioanalytical method validation[END_REF].

Stability

All analytes were stable (± 15%, n = 6) in the processed samples (all standards) stored for 24 h in the autosampler (+10°C).

Long-term storage at -20°C, for 3 months, did not show any degradation of analytes in the stock solution or the ACQ quality controls (±15%, n = 6).

Carry-over effects

The signals observed for all analytes in the blank samples processed immediately after the highest standard was less than 20% of the signals of a LLOQ sample and less than 5% of the signal of the corresponding internal standard.

These results confirm the absence of contamination and therefore, the choice of appropriate chromatographic conditions.

Applicability

This method has been applied for the quantification of opioids and their derivatives in whole blood for clinical and forensic toxicology purposes. Due to a complex metabolism and pharmacokinetic parameters of each substance, it was essential to propose a method to quantify these metabolites [START_REF] Mura | Drogues et accidentalité[END_REF][START_REF] Baselt | Disposition of toxic drugs and chemicals in man[END_REF]. For example, heroin is the most commonly used illicit opioid. Still, considering its very short half-life (3-9 min in blood), it will only be detectable in blood for a few minutes after administration. On the contrary, its metabolites, 6-MAM and mainly morphine, with longer half-lives, will be detectable for 1-2 h and for more than 8 h, respectively. Considering pharmacokinetics parameters, heroin will only be detectable in the blood for a few minutes, 6-MAM 1 to 2 hours, while morphine and codeine will be detectable more than 8 hours after intake. Other synthetic opioids such as methadone and buprenorphine are also being misused [3]. Methadone and buprenorphine, synthetic opioids used in substitution treatment, are metabolized to EDDP and norbuprenorphine, respectively [START_REF] Mura | Drogues et accidentalité[END_REF][START_REF] Baselt | Disposition of toxic drugs and chemicals in man[END_REF]. It therefore seemed appropriate to develop a method to quantify these various opioids and their metabolites. A two-year review of the fatal forensic cases analyzed at the toxicology laboratory of the Reims hospital was carried out. Opioids and their derivatives were implicated in 16.2 % of fatal cases (n=186). Morphine was the most commonly detected opioid (41 %), followed by methadone (26 %), codeine (21 %), 6-MAM and buprenorphine (both 6 %). In 86 % of cases, the opiates detected were associated with other substances: benzodiazepines (50 %), THC (15.5 %), cocaine (7.7 %), or with each other. Opioids were also associated with opioid substitution therapies, methadone (23 %) and buprenorphine (3.8 %). The median, first and third quartiles concentrations for the different compounds were as follows: 6-MAM (3 µg/L, 3-8), buprenorphine (2.8 µg/L, 2.7-2.9), codeine (12 µg/L, 5-88.25), EDDP (18 µg/L, 9.2-30.5), methadone (139 µg/L, 128.15-188.7), morphine (22.4 µg/L, 6.3-129.5), norbuprenorphine (5.3 µg/L, 3.15-5.45) and norcodeine (29 µg/L, 13.25-52.25).

Comparison with reported methods

Ultra-high-performance liquid chromatography coupled with tandem mass spectrometry (UHPLC-MS/MS) has become a well-established analytical technique for the determination of drugs of abuse in clinical and forensic toxicology laboratories in whole blood and serum. The first application using high-resolution mass spectrometry for the quantification of opioids and their derivatives was performed with a Time of Flight (TOF) mass spectrometer [START_REF] Viaene | Comparison of a triple-quadrupole and a quadrupole time-of-flight mass analyzer to quantify 16 opioids in human plasma[END_REF][START_REF] Rosano | Multi-drug and metabolite quantification in postmortem blood by liquid chromatography-high-resolution mass spectrometry: comparison with nominal mass technology[END_REF].

Concerning sample pre-treatment, solid-phase extraction (SPE) is the most common in the literature [START_REF] Viaene | Comparison of a triple-quadrupole and a quadrupole time-of-flight mass analyzer to quantify 16 opioids in human plasma[END_REF][START_REF] Rosano | Multi-drug and metabolite quantification in postmortem blood by liquid chromatography-high-resolution mass spectrometry: comparison with nominal mass technology[END_REF][START_REF] Ahsman | Quantification of midazolam, morphine and metabolites in plasma using 96-well solid-phase extraction and ultra-performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Al-Asmari | Method for quantification of opioids and their metabolites in autopsy blood by liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Bjørk | Determination of 19 drugs of abuse and metabolites in whole blood by high-performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Christoffersen | Quantification of morphine, morphine 6-glucuronide, buprenorphine, and the enantiomers of methadone by enantioselective mass spectrometric chromatography in whole blood[END_REF][START_REF] Coles | Simultaneous determination of codeine, morphine, hydrocodone, hydromorphone, oxycodone, and 6acetylmorphine in urine, serum, plasma, whole blood, and meconium by LC-MS-MS[END_REF][START_REF] Eckart | Development of a new multi-analyte assay for the simultaneous detection of opioids in serum and other body fluids using liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Fernandez | Quantitative analysis of 26 opioids, cocaine, and their metabolites in human blood by ultra performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Ghassabian | Highthroughput assay for simultaneous quantification of the plasma concentrations of morphine, fentanyl, midazolam and their major metabolites using automated SPE coupled to LC-MS/MS[END_REF]. Many procedures use SPE columns for whole blood analysis, despite the risk of obstruction of the SPE column. To avoid this problem, some authors propose to work at low temperatures [START_REF] Coles | Simultaneous determination of codeine, morphine, hydrocodone, hydromorphone, oxycodone, and 6acetylmorphine in urine, serum, plasma, whole blood, and meconium by LC-MS-MS[END_REF] or to add a step of sonication of the sample by centrifugation before loading the column with the supernatant. [START_REF] Rosano | Multi-drug and metabolite quantification in postmortem blood by liquid chromatography-high-resolution mass spectrometry: comparison with nominal mass technology[END_REF][START_REF] Bjørk | Determination of 19 drugs of abuse and metabolites in whole blood by high-performance liquid chromatography-tandem mass spectrometry[END_REF]. Feliu et al. propose a liquid-liquid extraction of whole blood with chloroform and ethyl acetate [START_REF] Feliu | Validation of a fast UPLC-MS/MS method for quantitative analysis of opioids, cocaine, amphetamines (and their derivatives) in human whole blood[END_REF].

Moreno-Vicente et al. [START_REF] Moreno-Vicente | Development and validation of a bioanalytical method for the simultaneous determination of heroin, its main metabolites, naloxone and naltrexone by LC-MS/MS in human plasma samples: Application to a clinical trial of oral administration of a heroin/naloxone formulation[END_REF], and Sartori et al. [START_REF] Sartori | The development of a high-performance liquid chromatography-tandem mass spectrometric method for simultaneous quantification of morphine, morphine-3-β-glucuronide, morphine-6-β-glucuronide, hydromorphone, and normorphine in serum[END_REF] propose methanol plasma precipitation.

In this work, we describe the precipitation of whole blood using acetonitrile, zinc sulfate and methanol.

For the mass spectrometer, many procedures referenced in the literature use a triplequadrupole mass spectrometer with multiple-reaction-monitoring (MRM) mode. Due to its high sensitivity and specificity, MRM is considered as a reference for quantitative analysis [START_REF] Viaene | Comparison of a triple-quadrupole and a quadrupole time-of-flight mass analyzer to quantify 16 opioids in human plasma[END_REF][START_REF] Feliu | Validation of a fast UPLC-MS/MS method for quantitative analysis of opioids, cocaine, amphetamines (and their derivatives) in human whole blood[END_REF][START_REF] Ahsman | Quantification of midazolam, morphine and metabolites in plasma using 96-well solid-phase extraction and ultra-performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Al-Asmari | Method for quantification of opioids and their metabolites in autopsy blood by liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Bjørk | Determination of 19 drugs of abuse and metabolites in whole blood by high-performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Christoffersen | Quantification of morphine, morphine 6-glucuronide, buprenorphine, and the enantiomers of methadone by enantioselective mass spectrometric chromatography in whole blood[END_REF][START_REF] Coles | Simultaneous determination of codeine, morphine, hydrocodone, hydromorphone, oxycodone, and 6acetylmorphine in urine, serum, plasma, whole blood, and meconium by LC-MS-MS[END_REF][START_REF] Eckart | Development of a new multi-analyte assay for the simultaneous detection of opioids in serum and other body fluids using liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Fernandez | Quantitative analysis of 26 opioids, cocaine, and their metabolites in human blood by ultra performance liquid chromatography-tandem mass spectrometry[END_REF][START_REF] Moreno-Vicente | Development and validation of a bioanalytical method for the simultaneous determination of heroin, its main metabolites, naloxone and naltrexone by LC-MS/MS in human plasma samples: Application to a clinical trial of oral administration of a heroin/naloxone formulation[END_REF][START_REF] Sartori | The development of a high-performance liquid chromatography-tandem mass spectrometric method for simultaneous quantification of morphine, morphine-3-β-glucuronide, morphine-6-β-glucuronide, hydromorphone, and normorphine in serum[END_REF]. Rosano et al. proposed a multi-drug and metabolite quantification by liquid chromatography-high-resolution mass spectrometry in whole blood using a UHPLC-MS E /TOF [START_REF] Rosano | Multi-drug and metabolite quantification in postmortem blood by liquid chromatography-high-resolution mass spectrometry: comparison with nominal mass technology[END_REF]. Quantification was performed using precursor ion data obtained with a mass extraction window of ±5 ppm. Fragment and residual precursor ion acquisitions at ramped collision energies were evaluated as additional analyte identifiers. In parallel, UHPLC-MS/MS analysis showed comparable precision and bias. Viaene et al. also compared a triple-quadrupole and a quadrupole time-of-flight mass analyzer to quantify 16 opioids in human plasma [START_REF] Viaene | Comparison of a triple-quadrupole and a quadrupole time-of-flight mass analyzer to quantify 16 opioids in human plasma[END_REF]. They concluded that the most efficient system depends on the compound and even the concentration and that the best calibration model often differs by compounds and method.

Conclusion

In this work, we describe the development and the full validation of a precise, sensitive and accurate UHPLC-HRMS method which can simultaneously quantify 6-MAM, buprenorphine, codeine, norcodeine, dihydrocodeine, EDDP, ethylmorphine, heroin, methadone, morphine, nalbuphine, naloxone, norbuprenorphine, oxycodone, norpropoxyphene, and propoxyphene. The assay only requires a small volume of whole blood and a simple pre-treatment. After validation, this new assay was routinely applied for the analysis of clinical and forensic cases.

Figure 2

 2 Figure 2 illustrates an example of positive results obtained on a patient sample:

Table 1 :

 1 Retention time (RT), transition of precursors and fragments ions, mass spectrometry parameters for 16 opioids.

			Precursor ion		Quantification fragment ion (m/z)	Confirmation fragment ion (m/z)	Retention time
	Compound	Internal standard	(m/z) Theorical values	Collision energy (eV)	measured	Measured values theorical	fragment	Measured values measured theorical fragment	(min)
	6-monoacetylmorphine	6-monoacetylmorphine-D3	328,15433	35	211,07518	211,07590		C14 H11 O2 165,06974 165,07043	C13 H9	2,43
	buprenorphine	buprenorphine-D4	468,31084	35	414,26303	414,26443	C25 H36 N O4 396,21281 396,21748	C24 H30 N O4	3,48
	codeine	codeine-D3	300,15942	25	215,10667	215,10720		C14 H15 O2 243,10133 243,10212 C15 H15 O3	2,11
	dihydrocodeine	dihydrocodeine-D3	302,17507	30	245,11722	245,11642665 245,11777		C13 H15 N3 O2 C15 H17 O3	202,09387 202,0868	C12 H12 N O2 ???	2,05
	EDDP	EDDP-D3	278,19033	25	249,15085	248,14392		C18 H18 N	234,12741 234,12827	C17 H16 N	3,53
	ethylmorphine	morphine-D3	314,17507	25	229,12213	229,12151	C13 H15 N3 O 257,11703 257,11643	C14 H15 N3 O2	2,63
	heroin	heroin-D9	370,1649	25	268,1327	268,13375	C17 H18 N O2 328,15423 328,15488	C19 H22 N O4	3,12
	methadone	methadone-D3	310,21654	15	265,15823	265,15924023 265,15790		C19 H21 O C17 H19 N3	105,03381 105,03404	C7 H5 O	3,59
	morphine	morphine-D3	286,14377	25	201,09094	201,09155		C13 H13 O2	229,0861 229,08647 C14 H13 O3	1,09
	nalbuphine	morphine-D3	358,20128	25	340,19045	340,19127	C21 H26 N O3 254,11728 254,1181	C16 H16 N O2	2,7
	naloxone	morphine-D3	328,15433	25	310,14352	310,14432	C19 H20 N O3 253,10949 253,11028	C16 H15 N O2	2,13
	norbuprenorphine	norbuprenorphine-D3	414,26389	35	101,09634	101,09664		C6 H13 O	396,25326 396,25387	C25 H34 N O3	3,3
	norcodeine	morphine-D3	286,14377	25	268,1366	286,14432	C17 H20 N O3 225,09099 225,09155 C15 H13 O2	2
	norpropoxyphene	propoxyphene-D5	326,21146	10	308,20089	308,20144	C21 H26 N O 100,07619 100,07624 C5 H10 N O	3,59
	oxycodone	oxycodone-D6	316,15433	35	241,10925	241,11028	C15 H15 N O2 256,1331 256,13375	C16 H18 N O2	316
	propoxyphene	propoxyphene-D5	340,22711	15	58,06603	58,06567		C3 H8 N	266,19032 266,19087	C19 H24 N	3,57

Table 2 :

 2 Limit of detection (LOD), lower limit of quantification (LLOQ), upper limit of quantification (ULOQ), precision (relative standard deviation (RSD)) and accuracy of LLOQ and ULOQ (n = 6).

	Drug	LOD (µg/L)	LLOQ (µg/L)	Precision (RSD %) of LLOQ	Accuracy (%) of LLOQ	ULOQ (µg/L)	Precision (RSD %) of ULOQ	Accuracy (%) of ULOQ
	6-monoacetylmorphine	0.5	0.98	6.28	96.77	1000	6.64	91.24
	buprenorphine	0.15	0.39	19.80	82.48	100	7.76	103.06
	codeine	0.5	0.98	6.03	100.43	1000	5.70	92.73
	dihydrocodeine	0.5	0.98	7.88	99.47	1000	10.00	85.48
	EDDP	0.5	0.98	5.07	115.82	1000	5.70	99.35
	ethylmorphine	0.5	0.98	5.64	100.15	1000	7.90	109.64
	heroin	0.5	0.98	4.78	102.96	1000	5.30	96.94
	methadone	0.5	0.98	4.87	117.33	1000	3.51	96.01
	morphine	0.5	0.98	3.16	81.16	1000	0.84	99.42
	nalbuphine	0.5	0.98	2.63	106.51	1000	6.18	104.45
	naloxone	0.5	0.98	3.67	110.27	1000	3.19	102.39
	norbuprenorphine	0.1	0.20	14.26	106.75	100	7.59	90.19
	norcodeine	0.5	0.98	4.51	88.03	1000	3.02	103.46
	norpropoxyphene	0.5	0.98	10.72	88.06	1000	10.38	111.64
	oxycodone	0.5	0.98	8.64	103.25	1000	6.59	110.10
	propoxyphene	0.5	0.98	4.62	95.68	1000	6.39	108.50

Table 3 :

 3 Intra-and inter-assay precision and accuracy for 8 opiates and derivates determined on ACQ science quality controls (n = 10).

	RSD: relative standard deviation (%)
			BTMF A			BTMF B			BTMF C			STM A
		Intra-assay	Inter-assay	Intra-assay	Inter-assay	Intra-assay	Inter-assay	Intra-assay	Inter-assay
		Precision	Accuracy	Precision	Accuracy	Precision	Accuracy	Precision	Accuracy	Precision	Accuracy	Precision	Accuracy	Precision	Accuracy	Precision	Accuracy
		(RSD %)	(%)	(RSD %)	(%)	(RSD %)	(%)	(RSD %)	(%)	(RSD %)	(%)	(RSD %)	(%)	(RSD %)	(%)	(RSD %)	(%)
	6-monoacetylmorphine	2.78	77.41	6.58	75.55	0.87	85.30	7.14	86.94				Not available
	buprenorphine						Not available						10.64	92.22	12.93	89.79
	codeine	3.44	85.08	5.12	90.04	3.41	86.40	6.65	91.47	1.86	97.01	5.87	93.88		Not available
	dihydrocodeine	5.92	87.39	14.84	93.67	8.52	92.28	14.49	101.02	4.09	90.66	9.36	98.6
	EDDP						Not available						4.77	80.22	6.82	84.17
	methadone													4.27	85.01	3.13	87.8
	morphine	2.04	85.13	6.09	87.94	1.58	97.57	5.82	109.37	1.51	90.6	7.28	104.12		Not available
	norbuprenorphine													4.72	85.08	13.44	97.06

Table 4

 4 Intra-and inter-assay precision and accuracy for 16 opiates and derivates (n = 10) at three levels of concentrations (3xLLOQ, 40% and 80% of ULOQ) (n=10). RSD (%): relative standard deviation.

	QCL
	Compounds

Acknowledgements:

This work was supported by Reims University Hospital, France.

Conflict of interest

None to declare.

Table legends:

Table 1: Retention time (RT), transition of precursors and fragments ions, mass spectrometry parameters for 16 opioids and their respective internal standards.

Table 2:

Limit of detection (LOD), lower limit of quantification (LLOQ), upper limit of quantification (ULOQ), precision (relative standard deviation (RSD)) and accuracy of LLOQ and ULOQ (n = 6).

Table 3: Intra-and inter-assay precision and accuracy for 8 opiates and derivates determined on ACQ science quality controls (n = 15).

Table 4:

Intra-and inter-assay precision and accuracy for 16 opioids and derivatives (n = 15) at three levels of concentration (3xLLOQ, 40 % and 80 % of ULOQ).

Supplemental table 1: Target concentrations and confidence range of ACQ science internal quality controls

Supplemental table 2:

Matrix effect factor at two levels of concentration determined in 6 different blank whole blood samples


Alice Boilève
email: alice.boileve@gmail.com

Frédérique Kuhnowski

Thibaut Cassou Mounat

Nina Jehanno

Youlia Kirova

Hodkgin lymphoma concomitant of tuberculosis, a therapeutic challenge for multidisciplinary management Lymphome de Hodgkin concomitant d'une tuberculose, un challenge thérapeutique pour une prise en charge pluridisciplinaire

Keywords: Helical TomoTherapy®, PET/CT, Hodgkin lymphoma, tuberculosis Tomothérapie, PET/CT, Lymphome de Hodgkin, Tuberculose

Hodgkin lymphoma (HL) is a disease characterized by a high curability rate, and the treatment benefit-risk balance must be carefully addressed to achieve complete disease control with low risk of long-term toxicities. Most patients are treated with a combination of chemotherapy and radiotherapy, after disease staging and response to treatment evaluated by FDG PET/CT. We report the case of a 28-year-old patient concomitantly diagnosed of a Hodgkin lymphoma and active tuberculosis. Initial staging was difficult due to pulmonary and abdominal tuberculosis localization that induced FDG PET/CT hypermetabolism. Antituberculosis treatment was first started, allowing secondary an early accurate Hodgkin lymphoma staging by FDG PET/CT. The patient was then treated by chemotherapy and radiotherapy. Helical TomoTherapy® was used with involved site (IS) irradiation volume was performed to decrease the high doses to organs-at-risk (OAR), especially lungs in this context of tuberculosis.

Résumé (français)

Le lymphome de Hodgkin est une pathologie caractérisée par un taux de curabilité élevé, et il est indispensable d'étudier précisément la balance bénéfice-risque des traitements pour obtenir une rémission complète de la maladie avec le minimum de

Brief communication

Hodgkin lymphoma is a disease characterized by a high curability rate, with a 5-year relative survival close to 84% for all stages [START_REF] Miller | Cancer treatment and survivorship statistics[END_REF]. Treatment of localized disease is based on chemotherapy and radiotherapy. Benefit-risk balance must be carefully addressed to achieve complete disease control without long-term toxicities [START_REF] Hodgson | Long-term toxicity of chemotherapy and radiotherapy in lymphoma survivors: optimizing treatment for individual patients[END_REF]. The main challenge of radiotherapy is to reduce the risk of second cancer or cardiac disease, which can be achieved by reducing the total dose, reducing treatment volume and using highly performing radiotherapy techniques (3).

The Intensity-Modulated Radiotherapy (IMRT) as the Helical TomoTherapy®, is combining accurate patient positioning, improved homogeneous coverage of target volumes along with a sparing of OAR compared to three-dimensional conformal radiotherapy (3DCRT) for patients with supradiaphragmatic Hodgkin lymphoma (3). This paper reports the case of a 28-year-old patient concomitantly diagnosed of a Hodgkin lymphoma and active tuberculosis, treated by anti-tuberculosis antibiotherapy, chemotherapy and radiotherapy, after a challenging accurate Hodgkin lymphoma staging by 18F-FDG PET/CT (FDG PET/CT), as previously reported (4). Helical TomoTherapy® was performed to obtain homogeneous coverage of the treatment volume and decrease the doses to organs at risk.

Here, we report the case of a 28-year-old man without any comorbidity, presented a cervical tumefaction with a general alteration, asthenia and hemoptoic sputum. Cervical lymph node biopsy revealed Hodgkin scleronodular lymphoma. Initial CT-scan imaging showed multiple cervical and sus-clavicular bilateral lymph nodes, and mediastinal and right axillary lymph nodes as shown in Figure 1. The CT scan also revealed an alveolo-interstitial syndrome with a nodular lesion, none evocative of Hodgkin lymphoma (Figure 1). In this context, a bronchoscopy with bronchoalveolar lavage was performed and revealed the presence of mycobacterium tuberculosis, in favor of an active tuberculosis.

A first FDG PET/CT (General Electric Discovery ™710 ELITE (General Electric Healthcare) with time of flight (TOF) technology) confirmed an intense hypermetabolism of cervical and sub-clavicular lymph nodes. Interestingly, an hypermetabolism of the alveolointerstitial syndrome associated with mediastinal lymph nodes was observed, along with an hypermetabolism of the right colon (Figure 1, Figure 2A). A hypermetabolic subdiaphragmatic node, not visible on the CT-scan, was also observed, raising suspicion of below diaphragmatic Hodgkin lesion (Figure 2A). A biopsy of colon confirmed digestive tuberculosis localization, but peri-colonic lymph nodes could not be biopsied due to bleeding risk.

The stage of Hodgkin lymphoma (HL) was equivocal between stage II (only above diaphragm lymph nodes) or stage III (above and below diaphragm lymph nodes). After multidisciplinary discussion it was decided to start anti-tuberculosis quadruple drugs antibiotherapy (isoniazid, rifampin, pyrazinamide, and ethambutol). Secondary, a FDG PET/CT performed after one month of anti-tuberculosis treatment (Figure 2B) showed persisting hypermetabolism of cervical and sub-mandibular bilateral and right axillary lymph nodes (SUVmax 6.9 before antibiotherapy vs 10.9 after antibiotherapy), but a decreased metabolism of pulmonary (SUVmax 5.4 before antibiotherapy vs 2 after antibiotherapy), mesenteric and colic (SUVmax 10.9 before antibiotherapy vs 7.4 after antibiotherapy) tuberculosis localization. A complete metabolic response of infra diaphragmatic lymph nodes was achieved (SUVmax 6.5 before antibiotherapy vs none after antibiotherapy), making it likely to be due to tuberculosis (Figure 2B). Finally, Hodgkin lymphoma was staged II Aa, unfavorable group (>4 lymph nodes groups). FDG PET/CT performed after 4 cycles of ABVD (doxorubicin bleomycin-vinblastinedacarbazine) showed a complete metabolic response of lymphoma (Deauville 2 criteria) and a complete disappearance of tuberculosis initial localization.

Involved site (IS) radiotherapy of 30 Gy in 15 fractions was then performed (cervical and sub-clavicular bilateral, right axillary, hilar and mediastinal localization), as shown in Figure 3, while the patient was still under bitherapy for anti-tuberculosis treatment. The lungs V20 was 11.3%, and mean cardiac dose was 1.4 Gy. No early toxicity was observed.

Anti-tuberculosis antibiotherapy was given for six months. Currently, 30 months after the end of the treatment of both Hodgkin lymphoma and tuberculosis, the patient is asymptomatic, in complete remission without any late treatment related toxicity or tuberculosis relapse (Figure 2C).

Hodgkin lymphoma is a curable disease after modern treatment by chemotherapy and radiotherapy [START_REF] Miller | Cancer treatment and survivorship statistics[END_REF]. The main challenge is to reduce long term toxicity of both chemotherapy and radiotherapy. The modern HL management closely relies an accurate staging using FDG PET/CT currently considered as a standard for staging, assessment of the treatment response and in some cases: follow-up of HL [START_REF] Cheson | Recommendations for initial evaluation, staging, and response assessment of Hodgkin and non-Hodgkin lymphoma: the Lugano classification[END_REF]. The FDG PET/CT is not specific of malignancies and that hypermetabolism can be observed in inflammatory process [START_REF] Vaidyanathan | FDG PET/CT in infection and inflammation--current and emerging clinical applications[END_REF][START_REF] Mukherjee | Lymphoma and tuberculosis: temporal evolution of dual pathology on sequential 18F-FDG PET/CT[END_REF]. This case is a perfect example of the use of new imaging techniques as previously reported to do the differential diagnosis [START_REF] Jehanno | PET/CT imaging in management of concomitant Hodgkin lymphoma and tuberculosis -a problem solver tool[END_REF]. At the same time, this case represents also an example of the interest of multidisciplinary discussion and management of these rare cases.

Tuberculosis treatment was decided in collaboration with infectiology specialists and relied on a quadruple drug antibiotherapy (isoniazid, rifampin, pyrazinamide, and ethambutol) for 2 months, then 4 months with isoniazid and rifampicin. No drug interaction with the chemotherapy was reported with rifampicin (a powerful enzymatic inductor), allowing clinicians to start chemotherapy despite tuberculosis treatment. Isoniazid and rifampicin concentations were not monitored during chemotherapy course since the patient did not experience any grade 3/4 hematological and non-hematological complications.

Tuberculosis treatment resulted in a rapid decrease of abdominal hypermetabolism, allowing distinction between HL and tuberculosis nodes, which helped to stage accurately HL. Concomitant anti-cancer-therapy treatment and anti-tuberculosis treatment has already been proved effective and safe for treating cancer patients with active tuberculosis [START_REF] Hirashima | Efficacy and safety of concurrent anti-Cancer and anti-tuberculosis chemotherapy in Cancer patients with active Mycobacterium tuberculosis: a retrospective study[END_REF].

However, the choice of radiotherapy technique was also challenging in this context of pulmonary tuberculosis. Considering the risk of tuberculosis lung sequelae, avoiding to give high doses to lungs was crucial and was achieved by adapting the radiotherapy technique [START_REF] Besson | Evolution of radiation techniques in the treatment of mediastinal lymphoma: from 3D conformal radiotherapy (3DCRT) to intensitymodulated RT (IMRT) using helical tomotherapy (HT): a single-centre experience and review of the literature[END_REF][START_REF] Chargari | Potential of helical tomotherapy for sparing critical organs in a patient with AIDS who was treated for Hodgkin lymphoma[END_REF]. Several studies proved an improved target volumes coverage along with a better sparing of some organs at risk with Intensity-Modulated Radiotherapy (IMRT), compared to three-dimensional conformal radiotherapy (3DCRT) [START_REF] Goodman | Intensity-modulated radiotherapy for lymphoma involving the mediastinum[END_REF]. The IMRT can be delivered using HT as previously reported [START_REF] Chargari | Potential of helical tomotherapy for sparing critical organs in a patient with AIDS who was treated for Hodgkin lymphoma[END_REF]. On the other hand, treatment volumes were adapted using "involved-site" (IS) radiotherapy, following the ILROG Guidelines [START_REF] Portlock | Involved site radiation therapy for the treatment of early-stage Hodgkin lymphoma in adolescents and young adults[END_REF].

In conclusion, in a context of concomitant Hodgkin lymphoma and tuberculosis, accurate staging is crucial for optimal treatment of HL, which was possible with repeated FDG PET/CT imaging. An individualized radiotherapy management was also essential to decrease dose to OAR and obtain perfect coverage of the treatment volume.

  toxicités à long terme. La plupart des patients sont traités par une combinaison de chimiothérapie et de radiothérapie, après une stadification initiale et une évaluation de la réponse au traitement par FDG PET/CT. Nous rapportons le cas d'un patient de 28 ans, diagnostiqué concomitamment d'un lymphome de Hodgkin et d'une tuberculose active. La stadification initiale a été rendue difficile à cause de localisations pulmonaire et abdominale de la tuberculose, conduisant à un hypermétabolisme au FDG PET/CT. Un traitement anti-tuberculeux a été débuté en premier, permettant une stadification secondaire plus précise du lymphome de Hodgkin avec le FDG PET/CT. Le patient a ensuite été traité par chimiothérapie puis radiothérapie. La tomothérapie hélicoÏdale a été utilisée avec pour volumes d'irradiation les sites initialement envahis, pour diminuer les doses aux organes à risque, notamment les poumons dans ce contexte de tuberculose.

Informed consent statement

Written informed consent was obtained from the patient for publication of this case report, including accompanying images.

(1) Hypermetabolism of cervical, mediastinal and axillary lymph nodes, as well as pulmonary and colonic uptake with mesenteric lymph nodes (white arrow).

(2) One month after initiation of anti-TB antibiotherapy, with a decrease of pulmonary, colonic and mesenteric lymph nodes uptake, but increased uptake of above diaphragm nodes.

(3) Complete metabolic response after HL and TB treatment, with TB lung sequelae. (2) Un mois après le début du traitement anti-tuberculeux, avec une diminution de l'hypermétabolisme pulmonaire, colique et des ganglions mésentériques, mais une augmentation de l'hypermétabolisme des ganglions sus-diaphragmatiques.

(3) Réponse métabolique complete après traitement anti-tuberculeux et traitement du lymphoma de Hodgkin, avec séquelles pulmonaires de tuberculose.

Figure 3 (Anglais)

Tomotherapy plan with isodoses.

(A) Isodose from 15 Gy (50 % of the dose of PTV 30 Gy) to 32,5 Gy (107% of the dose of PTV 30 Gy). Colour-wash with high doses in red and low doses in blue.

(B) Isodose from 28,5 Gy (95% of the dose of PTV 30 Gy) to 32,5 Gy (107% of the dose of PTV 30 Gy). Colour-wash with high doses in red and low doses in blue.

(C) 3D-representation of CTV (4) Dose-volume histograms of the Tomotherapy plan, with organ-at-risk doses.

Constraint was maximal on lungs (green and purple).

Figure 3 (Français)

Plan de traitement de tomothérapie avec les isodoses.

(A) Isodose de 15 Gy (50 % de la dose du PTV 30 Gy) à 32,5 Gy (107% de la dose du PTV 30 Gy). Les fortes doses sont en rouge et les faibles doses en bleu.

(B) Isodose de 28,5 Gy (95% de la dose du PTV 30 Gy) à 32,5 Gy (107% de la dose du 30 Gy). Les fortes doses sont en rouge et les faibles doses en bleu.

(5) Représentation 3D du CTV.


Axel Largent
email: axel.largent@hotmail.fr

Louis Marage

Ivan Gicquiau

Jean-Claude Nunes

Nick Reynaert

Joël Castelli

Enrique Chajon

Oscar Acosta

Giulio Gambarota

Renaud De Crevoisier

Hervé Saint-Jalmes

Head-and-Neck MRI-only radiotherapy treatment planning: from acquisition in treatment position to pseudo-CT generation Planification de radiothérapie externe pour la sphère ORL à partir d'imagerie par résonance magnétique : de l'acquisition en position de traitement à la génération de pseudoscanographie

Keywords: radiotherapy treatment planning, MRI-only workflow, protocol optimization, pseudo-CT, Résumé planification de radiothérapie externe à partir d'IRM, optimisation de protocole, pseudo-CT

Purpose: In context of head-and-neck radiotherapy, this study aims to compare MR image quality according to diagnostic (DIAG) and radiotherapy (RT) setups; and to optimize an MRI-protocol (including 3D T1 and T2-weighted sequences) for dose-planning (based on pseudo-CT generation).

Materials and methods:

To compare DIAG and RT setups, Signal-to-Noise-Ratio (SNR) and percentage-image-uniformity (PIU) were computed on T1 images of phantoms and volunteers.

Influence of the sample conductivity on SNR was quantified using home-made phantoms. To obtain reliable T1 and T2 images for RT-planning, an experimental design was performed on volunteers by using SNR, Contrast-to-Noise-Ratio (CNR) and Mean-Opinion-Score (MOS). Further, pseudo-CTs were generated from 8 patients T2 images with a state-of-art deep-learning method. These pseudo-CTs were evaluated by mean-absolute-error (MAE) and mean-error (ME).

Results: SNR was higher for DIAG-setup compared to RT-setup (SNR-ratio = 1.3). A clear influence of the conductivity on SNR was observed. PIU was higher for DIAG-setup (38.8%) compared to RT-setup (33.5%). Regarding the protocol optimization, SNR, CNR, and MOS were 20.6, 6.16, and 3.91 for the optimal T1 sequence. For the optimal T2 sequence, SNR, CNR and MOS were 25.6, 44.46 and 4.0. In the whole head-and-neck area, the mean MAE and ME of the pseudo-CTs were 82.8 and -3.9 HU.

Conclusion:

We quantified the image quality decrease induces by using an RT-setup for head-andneck radiotherapy. To compensate this decrease, an MRI protocol was optimized by using an experimental design. This protocol of 15 minutes provides accurate images which could be used for MRI-dose-planning in clinical practice.

Introduction

In radiotherapy, X-ray imaging (CT-scan and CBCT) is the main imaging modality for treatment planning and delivery. CT-scan provides tissue electron density information required for dose calculation. CBCT allows fast imaging for patient positioning and tracking/gating of the tumor. However, X-ray imaging has a poor soft tissue contrast and is ionizing. MRI currently used for diagnosis has a better soft tissue contrast and is non-ionizing. MRI could improve patient positioning, delineations, and dose calculation. To take full advantage of this imaging, an MRI-only workflow has been proposed. This new workflow lead to new open research areas as MRI acquisition in treatment position and MRI dose calculation.

MR image acquisition in treatment position is the first step of an MRI-only radiotherapy workflow. This acquisition ensures reproducibility of patient positioning, which is required to deliver accurately the dose in the desired localization. Head-and-neck is one of the most challenging sites for this acquisition. The main difficulty is that the radiotherapy treatment (RT) equipment, such as radiation therapy flat couch, head-and-shoulder mask, head board, do not fit within the standard diagnostic coil system. To address this issue, specific receiving coils for RT planning (RT coil system) were designed by the manufacturers [START_REF] Devic | MRI simulation for radiotherapy treatment planning[END_REF][START_REF] Paulson | Comprehensive MRI simulation methodology using a dedicated MRI scanner in radiation oncology for external beam radiation treatment planning[END_REF][START_REF] Paulson | Consensus opinion on MRI simulation for external beam radiation treatment planning[END_REF] . However, these setups are composed of flexible coils poorly fitting the patient anatomy, which decreased the image quality. In a limited number of studies [START_REF] Liney | Commissioning of a new wide-bore MRI scanner for radiotherapy planning of head and neck cancer[END_REF][START_REF] Wong | Image quality assessment of a 1.5T dedicated magnetic resonance-simulator for radiotherapy with a flexible radio frequency coil setting using the standard American College of Radiology magnetic resonance imaging phantom test[END_REF] , the image quality achieved by a RT coil system was quantified, and compared with a diagnostic coil system. In Liney et al. [START_REF] Liney | Commissioning of a new wide-bore MRI scanner for radiotherapy planning of head and neck cancer[END_REF] , a commissioning of a 1.5 T MRI equipped with a RT headand-neck coils was conducted. Volunteer and phantom 2D scans were used to determine the image quality. However, the coverage of the RT coils was too small to acquire correctly images of the lower neck region, limiting its use to some specific tumor sites. In another study, Wong et al. [START_REF] Wong | Image quality assessment of a 1.5T dedicated magnetic resonance-simulator for radiotherapy with a flexible radio frequency coil setting using the standard American College of Radiology magnetic resonance imaging phantom test[END_REF] assessed the image quality of a 1.5 T MRI with a RT setting composed of two flexible coils and a customized bi-lateral coil holder. The image quality assessment was conducted by following the standard ACR MRI phantom test [START_REF]Phantom Test Guidance for the ACR MRI Accreditation Program[END_REF] . Nevertheless, these tests were not performed in clinical conditions on volunteers as we propose in our study. MRI dose calculation is the main challenge of an MRI-only workflow. As there is no direct mapping from MR intensities to electron density, this research area remains an outstanding problem. To address this issue, several pseudo-CT generation methods from MRI were proposed. These methods can be roughly divided in bulk density [START_REF] Lambert | MRI-guided prostate radiation therapy planning: Investigation of dosimetric accuracy of MRI-based dose planning[END_REF] , atlas-based [START_REF] Dowling | Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences[END_REF][START_REF] Burgos | Robust CT Synthesis for Radiotherapy Planning: Application to the Head and Neck Region[END_REF] , and learning-based methods (patch-based [START_REF] Andreasen | A patch-based pseudo-CT approach for MRI-only radiotherapy in the pelvis[END_REF][START_REF] Largent | Pseudo-CT Generation for MRI-Only Radiation Therapy Treatment Planning: Comparison Among Patch-Based, Atlas-Based, and Bulk Density Methods[END_REF] and deep learning methods (DLM) [START_REF] Nie | Medical Image Synthesis with Context-Aware Generative Adversarial Networks[END_REF][START_REF] Han | MR-based synthetic CT generation using a deep convolutional neural network method[END_REF][START_REF] Wolterink | Deep MR to CT Synthesis Using Unpaired Data[END_REF][START_REF] Largent | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning[END_REF][START_REF] Largent | Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods[END_REF] ). The DLMs appeared to be the most efficient and accurate methods in the literature. These methods aim to model the relations between HU values of the CTs and the MR intensities by training a neuronal network. Once the optimal network parameters are estimated, the model can be finally applied to a test patient MRI to generate its corresponding pseudo-CT.

The aim of this study was firstly to compare the image quality provided by a diagnostic (DIAG) and RT coil systems and to optimize an MRI protocol for head-and-neck RT planning with volunteers in treatment position and the RT setup; secondly, to evaluate the accuracy of pseudo-CTs obtained from our optimized MRIs. These pseudo-CTs were generated by a state-of-the-art DLM [START_REF] Largent | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning[END_REF][START_REF] Largent | Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods[END_REF] which was compared favorably with other methods in the literature.

Materials and Methods

All experiments were conducted according to the procedures approved by the local Institutional Review Board. Written informed consent was obtained from all participants.

Image acquisitions

Comparison and optimization studies

MRI of the manufacturer phantom (GE cylindrical phantom), a salted home-made phantom and three healthy volunteers in head-and-neck treatment position, was performed at 1.5 T (Optima MR 450W, GE Healthcare, Milwaukee, USA). For the DIAG-MRI acquisitions, the standard coil system (32 channels head-and-neck units) was used. For the RT-MRI acquisitions, the specific coil system (16 channels GE MR Radiation-Oncology suite) with RT equipment (five points head-and-shoulder mask, radiotherapy flat couch and head board) was used. Figure 1 shows the DIAG and RT coil systems. A RT protocol was designed with isotropic 3D T1 gradient echo and 3D T2 fast spin echo (CUBE) sequences. The T1 and T2 sequences were applied with three and four distinct parameter sets (Table 1). The acquisitions were performed in sagittal orientation and reconstructed in axial orientation to minimize acquisition time and avoid aliasing, except for the RT T2 sequences. To allow reliable estimation of noise and contrast ratio with parallel imaging acceleration 17 , all images were acquired twice. This double acquisition was repeated five times on manufacturer phantom and each volunteer.

Pseudo-CT generation

Eight patients treated for head-and-neck cancer were considered. These patients received a CT scan and MRI in treatment position. CT scans were acquired with a Philips large-bore scanner (120 Kv, 2 mm slice thickness). For MRI, 3D T2-weighted sequences were performed using our optimized protocol (TE = 100.3 ms, TR = 3000 ms, flip angle = 90°, FOV = 410 mm, voxel sizes = 1.6 mm 3 ).

To correct MRI non-uniformity, the images were preprocessed by using N4 bias-field correction and histogram matching as in Dowling et al. [START_REF] Dowling | Automatic Substitute Computed Tomography Generation and Contouring for Magnetic Resonance Imaging (MRI)-Alone External Beam Radiation Therapy From Standard MRI Sequences[END_REF] . Each CT was registered to its corresponding MRI with a rigid registration, followed by a non-rigid registration.

Image quality metrics

To evaluate the MR image quality Signal-to-Noise Ratio (SNR), Contrast-to-Noise Ratio (CNR), Percentage Image Uniformity (PIU) and Mean Opinion Score (MOS) were considered. These metrics were described in the paragraphs below.

Signal-to-Noise Ratio

The Signal-to-Noise Ratio (SNR) is a metric used to estimate the quality of the acquired signal related to the noise. A simple method to determine SNR is to compute, on a single image, the ratio between the mean of the signal in the region of interest (ROI) and the standard deviation in the background. However, new MRI reconstruction methods modify the noise distribution in the images, and the standard computation method of the SNR is not reliable. In this study, SNR was computed with a more appropriate method using double acquisition [START_REF] Firbank | A comparison of two methods for measuring the signal to noise ratio on MR images[END_REF][START_REF] Murphy | Signal-to-noise measures for magnetic resonance imagers[END_REF] . The signal was estimated by computing the mean image of the double acquisition. The noise was estimated by computing the difference image of the double acquisition. ROIs were placed inside the resulting images and the SNR was computed with the following formula:

= μ √2 (1)
where μ is the signal measured on the average image of the double acquisition, σ is the standard deviation measured on the difference image, √2 is the correction factor taking into account the double acquisition.

Contrast-to-Noise Ratio

CNR aims to quantify the contrast between two given tissues. As for SNR, a double acquisition was performed for CNR computation. This metric was computed, inside two ROIs ( , ) placed in two distinct tissues, with the following formula:

= | μ )) - μ ))| 1 2 )) + )) √2 (2)
where is the mean image of the double acquisition and the difference image.

Percentage Image Uniformity

PIU was used to evaluate this image uniformity [START_REF] Sled | Understanding intensity non-uniformity in MRI[END_REF] . PIU was computed with the following formula:

= 1 - ! " ) -! ) ! " ) + ! ) # (3)
where " ( ) is the region with the highest signal (the region with the lowest signal) inside the image. In our study, a 3D extension of this metric was implemented by using the Insight-Toolkit library [START_REF] Ibanez | The ITK software guide[END_REF] (ITK).

Mean Opinion Score

MOS is a measure used in psychometry, which aims to quantify the opinion of a group of individuals. This metric consists of averaging the scores (appreciations) given by several experts.

Comparison of the DIAG and RT coil systems

To compare the image quality achieved by the DIAG and RT systems, SNR (Eq. 1) and PIU (Eq. 3)

were estimated on 3D T1-weighted images of phantoms and volunteers (TE: 4.2 ms; TR: 7.0 ms; Flip angle: 15°; bandwidth: 195.3 Hz/px, FOV: 380 x 380 x 414 mm 3 ). Moreover, PIU was used to compare PURE and N4 21 non-uniformity correction algorithms.

Signal-to-Noise ratio study

For the manufacturer phantom images, five circular ROIs were placed in the central slice. For the volunteer images, five circular ROIs were placed in the organs-at-risk (brainstem, up to corpus callosum white matter, cerebellum, tongue and scalene muscle). SNR measurements were performed inside the ROIs and averaged for each coil system. The SNR ratios between the DIAG and RT coil systems were computed to compare image quality.

The SNR (Eq. 1) is impacted by different noise sources in the MR scanning process. These sources are mainly the receiving coil resistance and losses originating from human body tissues. These losses are proportional to the conductivity of tissues [START_REF] Giovannetti | An efficient method for electrical conductivity measurement in the RF range[END_REF] . To simulate the noise source, independently from the coil resistance, a home-made uniform phantom mimicking the head-and-neck anatomy and containing a saline solution was imaged. Three salt concentrations were used (0, 3.2g/L and 5 g/L).

For the unsalted solution, the noise originating from the coil is evaluated, at 3.2 g/L an average conductivity of human body tissues is considered [START_REF] Giovannetti | An efficient method for electrical conductivity measurement in the RF range[END_REF] , and 5g/L corresponds to a maximum barely present in human body tissues.

Percentage Image Uniformity study

Manual delineations of the manufacturer phantom were performed, separating the VOI from the background. Then, PIU (Eq. 3) was computed inside the VOI before and after application of the PURE (phased-array uniformity enhancement) [START_REF] Ogasawara | Image Non-Uniformity Correction for 3-T Gd-EOB-DTPA-Enhanced MR Imaging of the Liver[END_REF] and N4 image non-uniformity correction algorithms [START_REF] Tustison | N4ITK: improved N3 bias correction[END_REF][START_REF] Boyes | Intensity non-uniformity correction using N3 on 3-T scanners with multichannel phased array coils[END_REF] . PURE is a tool developed by an MRI manufacturer used in clinical imaging and N4 is an algorithm originated from computer vision used in research. The efficiency of both nonuniformity correction algorithms is compared.

Optimization of the T1 and T2 sequences for the RT coil system

To obtain reliable 3D T1 and T2 MR images for head-and-neck radiotherapy planning, an experimental design was performed. Three T1 and four T2 parameter sets were defined, and SNR (Eq. 1), CNR (Eq. 2) and MOS were used as criteria to select the optimal parameter set. During the experiment, clinical constraints were also considered (patient and RT equipment set-up time).

Signal-to-Noise ratio study

Circular ROIs were placed in the organs-at-risk (brainstem, up to corpus callosum white matter, cerebellum, tongue and scalene muscle) of the volunteers. SNR (Eq. 1) were computed inside the ROIs, and averaged for all T1 and T2 images. The SNR was used to select the optimal parameter set.

Contrast-to-Noise ratio study

Circular ROIs were placed in the scalene muscle and the brainstem of the volunteers. CNR (Eq. 2)

were computed inside the ROIs, and averaged for each T1 and T2 images. The CNR was used to select the optimal parameter set.

Mean Opinion Score study

The MOS was obtained from eight radiotherapists and five physicists. They ranked the volunteer images for each T1 and T2 parameter sets.

Pseudo-CT generation

To evaluate performances of our optimized protocol, pseudo-CTs were generated from the patient MRIs. Each pseudo-CT was obtained by using a GAN with perceptual loss [START_REF] Largent | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning[END_REF][START_REF] Largent | Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods[END_REF] . This DLM was composed of two networks: a generator (G) and a discriminator (D), which were trained in competition. Training and validation of this method was performed by using a leave-one-out scheme.

Generator network

The generator network aimed to provide pseudo-CTs from MRIs. In this study, the generator was a U-Net [START_REF] Han | MR-based synthetic CT generation using a deep convolutional neural network method[END_REF] . Its architecture was composed of two networks called encoding and decoding. The encoding aimed to extract multi-scale features from the input MRI. This network was composed of 12 convolutional layers, followed by batch normalization and ReLu activation functions [START_REF] Hahnloser | Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit[END_REF] .

The decoding part aimed to gradually reconstruct the pseudo-CT using the features computed during the encoding. This network was a mirror version of the encoding network.

Design of the encoding and decoding networks were detailed in supplementary materials 1.

To train the generator, a perceptual loss functions was implemented [START_REF] Yang | Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss[END_REF] . This loss mimics the human visual system to compare CT and pseudo-CT using similar features as opposed to only the intensities [START_REF] Nie | Medical Image Synthesis with Context-Aware Generative Adversarial Networks[END_REF][START_REF] Yang | Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss[END_REF] . The VGG16 network [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] was pretrained from the ImageNet data set 28 , and used to compute the features inside the CT and pseudo-CT. The perceptual loss (LG) function was defined as:

$ % , ) = '()) ) -()) ) ) ' (4)
where is the MRI, is the corresponding CT, ) ) is the pseudo-CT provided by the generator, ‖ ‖ is the L2 norm, and ()) is the output of the 7 th VGG16 convolutional layer.

Discriminator network

The discriminator network [START_REF] Wang | Perceptual Adversarial Networks for Image-to-Image Transformation[END_REF] aimed to classify the generated pseudo-CT as real or fake CT. Thus, the output of this network is a probability value ranging between 0 (fake) and 1 (real). The discriminator architecture was composed of six convolutional layers and one fully connected layer.

Each convolutional layer was followed by batch normalization and Leaky-ReLu activation functions. The fully connected layer was followed by a sigmoid activation function. The loss function of the discriminator (LD) was a L2 loss.

Design of the discriminator networks was detailed in supplementary materials 1.

The generator (LG) and discriminator (LD) losses were combined to form the following adversarial loss: $ + ,-, . , ) = / $ 0 , ) + / $ % , ), where is the MRI, is the corresponding CT, $ 0 , ) is the discriminator loss, $ % , ) is the generator loss, and / and / are the weights for the discriminator and generator losses (Eq. 4).

Hyper-parameter setting of the GAN [START_REF] Largent | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning[END_REF][START_REF] Largent | Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods[END_REF] was detailed in supplementary materials 1.

Pseudo-CT evaluation

To evaluate the accuracy of the DLM [START_REF] Largent | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning[END_REF][START_REF] Largent | Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods[END_REF] , a voxel-wise comparison of the HU from CT and pseudo-CT was performed. For this aim, the mean absolute error (MAE) and the mean error (ME)

were calculated for the whole body. These endpoints were defined as:

Results

Comparison of the DIAG and RT setups

Table 2 gives the SNR value and ratio between the manufacturer phantom and volunteer images.

The SNR was always higher for the DIAG coil system than for the RT coil system. The SNR ratios of both coil systems were 1.3 for phantom and volunteers. As the SNR increases with the square root of the acquisition time, it will require an increase of 70% of the acquisition time ( SNR ratio) = 1.69) to reach the diagnostic image quality with the RT coil system. Table 3 shows the SNR values obtained on the home-made phantom filled with different concentrations of NaCl.

The SNR ratios were 1.6, 1.3 and 1.1 with respectively 0, 3.2 and 5 g/L of NaCl. For this phantom, the SNR was also higher for the DIAG coil system compared to that of the RT coil system. Figure 2 illustrates the image non-uniformity provided by both coil systems before and after PURE and N4

corrections. The PIU on raw 3D T1-weighted images is higher for the DIAG coil system compared to that of the RT coil system (38.8 % against 33.5 %). The N4 algorithm provided higher values compared to the PURE (DIAG: 86.7 % against 41.7%, RT: 76.6 % against 49.0 %). 4 gives the SNR values from the volunteer images acquired with the distinct T1 and T2 parameter sets. Higher SNR values were obtained for the v3 T1 and v1 T2-weighted images. Table 4 gives also the CNR values for the volunteer images. CNR values were higher for the v3 T1 and v4 T2-weighted images compared to other parameter sets. The lowest values were found for the v2 T2-weighted images. Additional Table 1 shows the MOS values given by 8 radiotherapists and 5 physicists. The maximum MOS values were found for the v3 T1 and v4 T2-weighted images. These values correspond to the appreciation "good". The scores range from 2.64 to 4.0.

Optimization of the T1 and T2 sequences for the RT setup

Pseudo-CT evaluation

Discussion

In a context of head-and-neck MRI-only radiotherapy, we quantify the loss of image quality caused by using a RT setup for acquisition in treatment position. The 30% image quality decrease found would require a 70% increase in acquisition time to be fully compensated (as in diagnostic). To compensate this decrease, an experimental design was proposed to optimize a clinical MRI protocol. The obtained MR images are suitable for dose planning (acquisition in treatment position and dose calculation).

To compare the performances of the DIAG and RT coil systems, 3D isotropic T1 images were acquired on phantoms and healthy volunteers, covering all the head-and-neck anatomy.

SNR ratios of 1.3 between the DIAG and RT coil systems were found for the manufacturer phantom and volunteers. In another study, Liney et al. [START_REF] Liney | Commissioning of a new wide-bore MRI scanner for radiotherapy planning of head and neck cancer[END_REF] reported ratios equal to 1.09, 1.55 and 1.9

(respectively in axial, coronal and sagittal planes) between the DIAG and RT coil systems. These values are close to our SNR ratio. However, they are hardly comparable because the authors performed the imaging in 2D with a different RT setup. Moreover, the coverage of these RT coil (head only) was too small to image correctly the lower neck region, limiting its use to some tumor sites.

The comparison between different saline solutions showed the importance of the conductivity losses for head-and-neck imaging at 1.5 T. As expected, the DIAG coil system is by design far more optimized for head-and-neck imaging than the flexible RT coil system. This is demonstrated by the 1.6 ratio of SNR between the two systems for the pure water phantom. When the salt concentration increases, losses in the phantom increase, reducing the impact of the coil design. In our case, the SNR of the two coil systems are equivalent for a 5 g/L salt concentration. At 3.2 g/L, the observed ratio of 1.3 is similar to the ratio observed on volunteer, supporting the use of this salt concentration in phantoms mimicking the human body [START_REF] Giovannetti | An efficient method for electrical conductivity measurement in the RF range[END_REF] .

The DIAG coil system is less affected by image non-uniformity compared to the RT coil system.

This system is designed with coil elements surrounding all the patient head, thus providing a very uniform signal. The flexible RT coil system is designed to accommodate different patient anatomies, this explains its non-uniformity. 3D PIU values computed on the 3D T1 image were 38.8% and 33.5 % for the DIAG and RT systems. In another study, Wong et al. [START_REF] Wong | Image quality assessment of a 1.5T dedicated magnetic resonance-simulator for radiotherapy with a flexible radio frequency coil setting using the standard American College of Radiology magnetic resonance imaging phantom test[END_REF] reported 2D PIU values equal to 92.7% and 93.4% for the DIAG and RT setups. These values are hardly comparable with our PIU results because they were only computed using one axial slice of 2D head images. The N4 non-uniformity correction algorithm provided better results compared to the manufacturer method PURE. Nevertheless, the N4 method has a lot of tuning parameters requiring strong knowledge in computer vision making it unappealing for clinical routine. The PURE method doesn't need tuning parameters. Indeed, the PURE method appears more appropriate for clinical routine while the N4 method can be used for pseudo-CT generation [START_REF] Johnstone | Systematic Review of Synthetic Computed Tomography Generation Methodologies for Use in Magnetic Resonance Imaging-Only Radiation Therapy[END_REF] .

A design experiment was performed on the 3D T1 and T2 volunteer images, to select the best sequence parameters for delineation and MRI dose calculation (pseudo-CT generation) during the RT planning.

On the T1-weighted MR images, the whole quantitative and subjective criteria provided the same results. The v3 T1 parameter set gave the best results with mean SNR, mean CNR and MOS respectively equal respectively to 20.6, 6.16 and 3.91. Conversely, for the T2-weighted images these criteria do not converge to the same result. As tissue contrast is more important than noise for delineation tasks during the RT planning, CNR and MOS were mostly considered to select the optimal T2 parameter set. The v4 T2 parameter set provided the best results with mean CNR and MOS equal to 44.46 and 4.0.

During our experiment, clinical constraints as patient and RT equipment set-up time, and patient discomfort (caused by the head and shoulder mask), were also considered. The patient RT set-up time was around 15 minutes compared to 5 minutes for the DIAG set-up time. The acquisition time of the whole protocol was 15 minutes which is shorter than a standard radiotherapy treatment session (~ 30 min).

Pseudo-CT generation was investigated in a limited number of studies for head-and-neck site.

Guerreiro et al. [START_REF] Guerreiro | Evaluation of a multi-atlas CT synthesis approach for MRI-only radiotherapy treatment planning[END_REF] used an ABM to generate pseudo-CTs in the head-and-neck area and reported a MAE = 90.7 HU. Johansson 32 performed a Gaussian mixture and obtained a MAE = 137 HU. Our GAN method with perceptual loss [START_REF] Largent | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning[END_REF][START_REF] Largent | Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods[END_REF] compared favorably with these studies. Our optimized MRI allow us to obtained accurate pseudo-CTs.

Our study presents some limitations. Firstly, MRI geometrical distortions were not evaluated.

However, for head-and-neck site geometrical distortions are generally low and inferior to 2 mm, which is acceptable for RT planning [START_REF] Paulson | Consensus opinion on MRI simulation for external beam radiation treatment planning[END_REF][START_REF] Torfeh | Characterization of 3D geometric distortion of magnetic resonance imaging scanners commissioned for radiation therapy planning[END_REF][START_REF] Walker | MRI distortion: considerations for MRI based radiotherapy treatment planning[END_REF] . Secondly, the efficient and low-SAR (Specific-Absorption-Rate) 3D gradient echo T1-weighted sequence used in this study could be sensible to dental artifacts. A fast spin echo alternative was added as an option to address this issue. Thirdly, we do not quantify the impact of using an RT-setup on tumor and organs-at-risk delineations. This quantification could be very challenging to perform because of bias from IRM-DIAG IRM-RT deformable registrations required to compare these delineations. Moreover, only three volunteers were scanned for practical reasons (fabrication of personalized head and shoulder mask, double acquisitions performed five times for each phantom and healthy volunteer, availability of the clinical MRI scanner). Finally, a dosimetric evaluation of the obtained pseudo-CTs was not performed, it is part of future works.

Conclusion

This study aims to improve patient positioning and dose targeting in head-and-neck MRI-only radiotherapy by using MRI acquired in treatment position (with RT-setup). The ratios between the SNR of the both coil systems were computed to quantify their differences in term of image quality. Each acquisition was performed twice and repeated five times. Additional Table 1. The Mean Opinion Score (MOS) given by 8 radiotherapists and 5 physicists.

The score label is defined by bad = 1, poor = 2, fair = 3, good = 4, excellent = 5.

Additional Table 1. La note d'opinion moyenne (MOS) donnée par huit radiothérapeutes et cinq physiciens. Le label associé à chaque note est : très mauvais = 1, mauvais = 2, acceptable = 3, bien = 4, excellent = 5.

Fig 3

 3 Fig 3 illustrates the axial reconstructed T2-weighted MR images (parameter sets v1, v2, v3) and the native T2-weighted MR image (parameter set v4), for one volunteer.Table 4 gives the SNR values

  Fig 4 illustrates the MR images obtained with the v3 T1 and v4 T2 sequence parameters, for one volunteer wearing a head and shoulder mask within the RT setup. These sequence parameter sets were retained for the clinical protocol. The patient set-up time with radiotherapy device was around 15 minutes (compared to 5 minutes for diagnostic set-up). The acquisition times were 3.21 and 6.22 minutes and the full protocol time was 15 minutes.

Fig 5 .

 5 Fig 5. illustrates for one patient the MRI, the real CT and the pseudo-CT generated by the GAN using the perceptual loss. The MAE and ME (Eq. 5 and Eq. 6) obtained by using the GAN DLM on our optimized patient MRI were 82.8 (± 48.6) HU and -3.9 (± 12.8) HU. The computation time to generate one pseudo-CT was approximately 35 seconds.

  T1 and T2-weighted MR images of the three volunteers (with RT equipment for RT acquisitions). Three T1 and four T2-weighted sequence parameters were tested for the comparison of the coil systems and the design optimization. Each acquisition was performed twice and repeated five time Table 4. SNR et CNR provenant d'images IRM 3D pondérées en T1 et T2 de trois volontaires. Trois paramètres de séquences pondérées en T1 et quatre paramètres de séquences pondérées en T2 ont été testées pour la comparaison des systèmes d'acquisition et la réalisation du plan d'expérience. Chaque séquence a été acquise deux fois avec cinq répétitions.

Fig 1 .Fig 1 .Fig 2 .Fig 2 .Fig 3 .Fig 4 .Fig 4 .Fig 5 .

 11223445 Fig 1. DIAG (a) and RT (b) coil systems, manufacturer phantom (c), and one volunteer wearing a five points head and shoulder mask (d)

Table 1 .

 1 To compensate loss of MR image quality generated by RT-setup, a design experiment was proposed to optimize a headand-neck MRI protocol. The obtained optimized MR images allow accurate and fast pseudo-CT generation by a generative adversarial network using perceptual loss. These images could be therefore used for dose planning in clinical practice. 3D T1 and T2-weighted sequence parameters. Three T1 and four T2-weighted sequence parameters were tested for the comparison of the coil systems and the design optimization. Each acquisition was performed twice and repeated five times.

		V1 T1	V2 T1	V3 T1	V1 T2	V2 T2	V3 T2	V4 T2
	FOV (mm)	380	380	380	380	380	380	410
	Matrix (px)	260x260 260x260 260x260 192x190 192x190 192x190 256x256
	Voxel size (mm 3 )	1.5 3	3	1.5 3	2.0 3	2.0 3	2.0 3	1.6 3
	Number of slices	176	176	124	224	224	224	242
	TE (ms)	4.2	1.12	4.2	74.6	131.9	95.5	100.3
	TR (ms)	7.0	3.5	7.0	2000	2000	2000	3000
	Flip angle (°)	15	10	15	90	90	90	90
	Bandwidth (Hz/px)	195.3	325.5	195.3	325.5	244.1	244.1	122.1
	Acceleration factor	2	2	1	2	2	1	1.5
	Acquisition time (min:s)	2:22	1:11	3:21	3:17	2:13	3:39	6:31
	Acquisition orientation	Sagittal Sagittal Sagittal Sagittal Sagittal Sagittal	Axial

Table 1 .

 1 Paramètres des séquences 3D pondérées en T1 et T2. Trois paramètres de séquences pondérées en T1 et quatre paramètres de séquences pondérées en T2 ont été testées pour la comparaison des systèmes d'acquisition et la réalisation du plan d'expérience. Chaque séquence a été acquise deux fois avec cinq répétitions.

Table 2 .

 2 SNR from 3D T1-weighted MR images of the manufacturer phantom and the volunteers.

Table 2 .

 2 SNR provenant d'images IRM 3D pondérées en T1 d'un fantôme et de volontaires. Le ratio entre les SNR des deux systèmes d'acquisition a été calculé pour quantifier leurs différences en termes de qualité d'images. Chaque séquence a été acquise deux fois avec cinq répétitions.

		Manufacturer phantom	Volunteer
	Coil systems	DIAG	RT	DIAG	RT
	SNR	173.8 (± 10.0)	133.8 (± 12.3)	21.2 (± 5.2)	16.3 (± 3.3)
	Ratio	1.3		1.3

Table 3 .

 3 Influence of the conductivity on the SNR measurements from DIAG and RT coil systems. SNR from 3D T1-weighted MR images of a home-made phantom filled with different concentrations of NaCl. Each acquisition was performed twice.

Table 3 .

 3 Influence de la conductivité sur les SNR provenant des systèmes d'acquisition de diagnostic et de radiothérapie. Ces SNR proviennent d'images IRM 3D pondérées en T1 de fantômes remplis avec différentes concentrations de NaCl. Chaque acquisition a été réalisée deux

	fois.

Table 4 .

 4 SNR and CNR from 3D

Acknowledgement

We would like to thank the MRI radiographers of our clinical center for assistance during the data acquisition. The volunteers were included in the OSS-IRM study, supported by the University Hospital of Rennes and the University of Rennes. This work was financed, in part, by the French Institut National du Cancer (INCa) in the context of a PhysiCancer project.

Supplementary materials 1: Pseudo-CT generation by a GAN with perceptual loss

Each pseudo-CT were obtained by using a GAN [START_REF] Largent | Comparison of deep learning-based and patch-based methods for pseudo-CT generation in MRI-based prostate dose planning[END_REF][START_REF] Largent | Pseudo-CT Generation for Mri-only Radiotherapy: Comparative Study Between A Generative Adversarial Network, A U-Net Network, A Patch-Based, and an Atlas Based Methods[END_REF] on the patient MRIs. This DLM was composed of two networks: a generator (G) and a discriminator (D), which were trained in competition.

Generator network

The generator network aimed to provide pseudo-CTs from MRIs. In this study, the generator was a U-Net [START_REF] Han | MR-based synthetic CT generation using a deep convolutional neural network method[END_REF] . Its architecture was composed of two networks called encoding and decoding. The encoding aimed to extract multi-scale features from the input MRI. This network was composed of 12 convolutional layers, followed by batch normalization and ReLu activation functions [START_REF] Hahnloser | Digital selection and analogue amplification coexist in a cortex-inspired silicon circuit[END_REF] . The filter numbers of these layers were 64, 64, 128, 128, 256, 256, 256, 512, 512, 512, 512, and 512, and their filter size was 3 × 3 (stride = 1). For down-sampling, convolutional layers with a filter size of 2 × 2 (stride = 2) were used.

The decoding part aimed to gradually reconstruct the pseudo-CT using the features computed during the encoding. This network was a mirror version of the encoding network. For feature upsampling, transposed 2D convolutional layers were used with a filter size of 2 × 2 (stride = 2). To obtain the pseudo-CT, the last layer of the decoding network was a convolution layer with one filter (size = 1×1).

To train the generator a perceptual loss functions was implemented [START_REF] Yang | Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss[END_REF] . This loss mimics the human visual system to compare CT and pseudo-CT using similar features as opposed to only the intensities [START_REF] Nie | Medical Image Synthesis with Context-Aware Generative Adversarial Networks[END_REF][START_REF] Yang | Low-Dose CT Image Denoising Using a Generative Adversarial Network With Wasserstein Distance and Perceptual Loss[END_REF] . The VGG16 network [START_REF] Simonyan | Very Deep Convolutional Networks for Large-Scale Image Recognition[END_REF] was pretrained from the ImageNet data set, and used to compute the features inside the CT and pseudo-CT. The perceptual loss function ($ % ) was defined as:

where is the MRI, is the corresponding CT, ) ) is the pseudo-CT provided by the generator, ‖ ‖ is the L2 norm, and ()) is the output of the 7 th VGG16 convolutional layer.

Discriminator network

The discriminator network aimed to classify the generated pseudo-CT as real or fake CT. Thus, the output of this network is a probability value ranging between 0 (fake) and 1 (real). The discriminator architecture was composed of six convolutional layers and one fully connected layer.

Each convolutional layer was followed by batch normalization and Leaky-ReLu activation functions. The filter numbers of these layers were 8, 16, 32, 64, 64 and 64. The filter size was 3 × 3 (stride = 2) for the first four layers and 1 × 1 (stride = 1) for the remaining layers. The fully connected layer was followed by a sigmoid activation function. The loss function of the discriminator $ 0 was a L2 loss.

The generator and discriminator losses were combined to form the following adversarial loss: $ + ,-, . , ) = / $ 0 , ) + / $ % , ), where is the MRI, is the corresponding CT, $ 0 , ) is the discriminator loss, $ % , ) is the generator loss, and / and / are the weights for the discriminator and generator losses.

Training of the GAN with perceptual loss

The GAN DLM was trained using anatomically paired data: axial 2D slices of the training CT and MR images. Data augmentation was performed to increase the size of the training cohort. It was conducted by randomly applying affine registrations on the slices (translated by -5% to 5% per axis, rotated by -10° to +10°, sheared by -10° to 10°). A mini-batch size of 10 slices and 200 epochs was considered. The network parameters were optimized using the Adam algorithm [START_REF] Kingma | Adam: A Method for Stochastic Optimization[END_REF] . The parameters of this algorithm parameters were: D = 1 × 10 GH , I = 0.9, and I = 0.9. The weights of the discriminator and generator loss functions were: / = 100 and / = 1, respectively. The