Yolanda Sánchez-Dehesa

Loïc Cerf

José-María Peña

Jean-François Boulicaut

Guillaume Beslon

Artificial Regulatory Networks Evolution

Genetic network inference is one of the main challenges for computer scientists in cellular biology. We propose to use in silico experimental evolution to guide the development of inference algorithm by (i) developing general knowledge about genetic networks structure (and use this knowledge to develop inference heuristics), and (ii) generate large realistic benchmarks to support validation of inference algorithms. For this purpose, we develop the RAevol model which aims at simulating the evolution of regulatory networks.

Introducing the challenge of network inference

Gene network dynamics is supposed to explain most of the cellular behaviors such as cell differentiation, cell response to stress or cell proliferation in cancer. Regulation activity is the core of cell behavior and it could become a key element in many research lines, either in specific cells/organisms or in a more general way.

A plethora of algorithms and tools have been proposed to reconstruct the actual cell regulatory network from transcriptomics data (see, e.g., [START_REF] Buc | Modeling and indentification of biological networks[END_REF][START_REF] Filkov | Handbook of Computational Molecular Biology, chapter Identifying Gene Regulatory Networks from Gene Expression Data[END_REF]) or, less ambitiously, to classify or cluster transcriptomics data. However, all these algorithms have to deal with a least two main difficulties.

The first difficulty is the under-determination of regulatory networks: Any transcription data set can be produced by an infinity of different genetic networks. Thus, when inferring the networks, the algorithms must use a priori knowledge to choose between networks that would be equivalent on a sole data basis. Most of the time, a parsimony criterion is used. This criterion, coming from epistemology (Occam's razor) and machine learning, is ubiquitous in science. However, genetic networks have not been constructed by a rational engineering process. They have been slowly and incrementally built by a blind tinkerer: Evolution. Yet, there is no obvious reason for evolution to choose the parsimonious way.

The second difficulty is the algorithm validation. Lots of inference algorithms are based on heuristics (e.g., parsimony), over-simplistic assumptions (interactions between genes are generally additive), or on a priori biological hypothesis that are still under debate (e.g., the assumption that network dynamics is visible in data gathered from thousands of unsynchronized cells). To validate these algorithms, one can compare their results with previously established knowledge or use artificially generated data sets to compare the inferred network with the one that has been used to generate the data [START_REF] Medina | BRITE: Universal topology generation from a user's perspective[END_REF][START_REF] Mendes | Artificial gene networks for objective comparison of analysis algorithms[END_REF]. However, this approach is entangled with our lack of knowledge about the hidden network structure. Both challenges thus meet on a same question: How are regulatory networks organized? Lots of studies have shown that they own a very specific topology [START_REF] Barabasi | Network biology: Understanding the cell's functional organization[END_REF] and that specific motifs are over-represented in such networks [START_REF] Alon | Network motifs: theory and experimental approaches[END_REF].

Artificial evolution as a benchmark generator

As Dobzhansky said 30 years ago: "Nothing in biology makes sense except in the light of evolution". Following this idea, we argue that taking evolution into account would help us to better understand the iterative process by which these networks are constructed. Yet, it is well known that networks generative process has a strong influence on the final network structure and dynamics [START_REF] Barabasi | Network biology: Understanding the cell's functional organization[END_REF]. However, genetic networks evolution cannot be easily studied in vivo due to their kinetic aspects. So, many authors have estimated the evolutionary mechanism by using bio-informatics tools and models, i.e., by inferring networks history from today structures [START_REF] Teichmann | Gene regulatory network growth by duplication[END_REF]. We propose the reversed approach: By modeling in silico the evolutionary dynamics of genomes and regulatory networks, we try to estimate the most probable structure. This knowledge will then be used as a priori knowledge in inference tools.

Moreover, such a model could provide plausible networks to lately generate large data sets which can be used as benchmarks for data mining and inference algorithms. Yet, evaluating data mining algorithms aiming at describing dynamic properties of regulatory networks (e.g., [START_REF] Jiang | Mining coherent gene clusters from gene-sample-time microarray data[END_REF]) is difficult today. Dynamic real-life data remain rare compared to what will be possible to collect within a few years. Thus, researchers tend to evaluate their algorithms at best on data issued from other research fields (e.g., ad-hoc patches on synthetic data generators like the popular basket data generator from IBM), at worst from randomly generated data. In both cases, looking for relevant patterns or models within the generated data is somehow useless. Moreover, it remains unclear whether such an experimental validation methodology (e.g., measures w.r.t. scaling issues) is fair. Our thesis is that studying realistic simulated networks is obviously a better alternative.

RAevol: In silico evolution of regulatory networks

There is a large amount of models dealing with evolution of regulatory networks [START_REF] Ciliberti | Robustness can evolve gradually in complex regulatory gene networks with varying topology[END_REF][START_REF] Aldana | Robustness and evolvability in genetic regulatory networks[END_REF][START_REF] Soyer | Evolution of complexity in signaling pathways[END_REF][START_REF] Banzhaf | On evolutionary design, embodiment and artificial regulatory networks[END_REF][START_REF] Francois | Design of genetic networks with specified functions by evolution in silico[END_REF]. However, all these models make the unrealistic hypothesis of a direct evolution of networks: The network is either directly altered by mutations or it is directly selected (e.g., on the basis of its topology). To study evolution in biological systems "in silico", we need to build models gathering the main basis of evolution: Genetic mutations, genotype-phenotype mapping, and phenotypic selection. Moreover to study evolution of regulatory networks, we should take into account that mutations do not affect directly the network. Obviously, mutation occurs on the genome, thus modifying indirectly the genetic network. Similarly the genetic network is not submitted to any direct selection pressure. The network contributes to the cell phenotype. So it is indirectly selected if and only if it produces selectable phenotypic traits. Yet, while many evolutionary studies directly select the networks for their topological (e.g., small world or scale free connectivity) or kinetic properties (e.g., stability or robustness), it is not clear whether such properties have a real impact on the phenotype.

Thus, we need to create an integrated model including evolution of genotypephenotype mapping and enabling the evolution of a genetic network inside such a system. This biological model should also include the main biological facts about regulation. It should be stratified on different organization levels from genome to organism phenotype and be compliant with some minimal known characteristics of real regulatory networks: Different activation levels, auto-regulation, different degrees of protein production, different binding levels, and all these characteristics must be evolvable by mutation of the "primary" genetic sequence. Finally, the organisms should be selected at the phenotypic level.

To build such a model we started from the integrative evolutionary model Aevol [START_REF] Knibbe | Structuration des génomes par sélection indirecte de la variabilité mutationnelle, une approche de modélisation et de simulation[END_REF][START_REF] Knibbe | Evolutionary coupling between the deleteriousness of gene mutations and the amount of noncoding sequences[END_REF]. Aevol integrates a genotype-phenotype mapping, with a transcription/translation process to compute proteins from the genes. Each organism in Aevol owns its own genome which is translated into a set of proteins. These proteins form a simple metabolic network giving the organism phenotype. Aevol can manage large population of such individuals: They are selected on the basis of the set of metabolic processes they are able to achieve. Aevol has been initially developed to study robustness and evolvability in artificial organisms. For the sake of simplicity, it does not include a regulatory process. Each protein has its own production rate that gives its basal concentration level. The phenotype results from the interactions of basic functional elements encoded by genes, considering the concentration of these elements (i.e., their basal level). For studying evolution of regulatory networks a regulatory system has been included in Aevol to build the Regulatory-Aevol Model (RAevol). In RAevol, protein production level is calculated from the matching level between the protein promoter and all other proteins sequences (see below). The main element of the model is the protein. It can behave in three different ways giving three artificial codes: Genetic code used to find transcribed regions along the binary genome sequence and to translate them into a sequence of amino-acids (there are six amino-acids in the Aevol model). Metabolic code. Given the protein sequence, it enables us to compute its function (functions are described as fuzzy sets in an arbitrary functional space). All protein functions are summed-up to compute the organism metabolism. Regulatory code used to compute the affinity of any protein with any genetic sequence and its influence on the transcription process (activation or inhibition). When a protein matches the regulatory region of a gene, it regulates its transcription, thus modifying its concentration in the metabolic network.

Details about the RAevol platform can be found in [START_REF] Sanchez-Dehesa | Modeling evolution of regulatory networks in artificial organisms[END_REF].

First results and open issues

We have used the RAevol platform to simulate long term evolution of an artificial organism. The organism must perform three different metabolic functions but, depending on the incoming of an external signal (i.e., a protein added manually), it must stop performing one of these process. Table 1 shows the main genetic and metabolic parameters at five stages of the evolution as well as the global network characteristics. During the 5000 last generations, the network size increases. It is due to the appearance of transcription factors (i.e., proteins taking part to the regulatory network but with no effect on the metabolism). This shows that the regulatory network has developed its own structure in parallel with the metabolic network. The emergence of the regulatory network is illustrated on Fig. 1: During the 5000 last generations, the network acquires a lot of new links (due to the addition of new nodes). Moreover these links are not randomly distributed: Highvalued (mainly negative) links have appeared and the distribution of the links is multi-modal. This distribution is clearly different from the one that would have been obtained by a random generation. We now plan to develop inference algorithms that exploit such properties learned from the model. The first simulations show that our model can generate large data sets. The next step will be to complexify the metabolism dynamics in the model by increasing the number of environmental conditions. Since we can control the complexity of the evolved network (data not shown), RAevol can generate a collection of benchmarks of different sizes (either in terms of network size or in terms of dynamics complexity). In parallel, we plan to extract knowledge directly from the data generated by RAevol to improve mining and inference algorithms (e.g., by choosing realistic heuristic criterion). Both directions are to help practitioners to better take-up the challenge of network inference from data.

Fig. 1 .

 1 Fig. 1. Histogram of links in the regulatory network at three evolutionary stages. They show the progressive partitioning of the links in two normal distributions and the emergence of high-valued links in the network.

Table 1 .

 1 Structure of the organisms at five evolutionary stages. The metabolic error quantifies the difference between the organism metabolism and the ideal metabolism given the environment; The fitness of an organism is inversely proportional to its metabolic error. Transcription factors are nodes connected to the regulation network but not to the metabolic network.

	Generation	0	1000	5000	10000 15000
	Metabolic error	0.1377 0.0616 0.0206 0.0171 0.0161
	Genome size (in kilobase pairs)	5.0	21.3	15.3	12.3	12.8
	Nb. of nodes in the genetic network	3	24	35	37	45
	Nb. of transcription factors	2	0	1	1	8
	Nb. of nodes in the metabolic network	1	24	34	36	37
	Nb. of links	3	576	1223	1332	2601
	positive links	2	508	771	827	1276
	negative links	1	68	452	505	1325

Proc. 1st Int. Workshop Machine Learning for Systems Biology MLSB'07, Evry (F), 2007, pp. 47-52.

Acknowledgments. This work is partly funded by EU contract IST-FET IQ FP6-516169.


Marco Sensale

Jérôme Chambert

Franz Chouly

Arnaud Lejeune

El Sylvain Joly

Khansa Rekik

Gwenaël Rolin

Thomas Lihoreau

Brice Chatelain

Patrick Sandoz

Experimental and numerical assessment of the mechanics of keloid-skin composites undergoing large deformations

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

Experimental and numerical assessment of the mechanics of keloid-skin composites undergoing large deformations M. Sensale 1,2,3 , J. Chambert 1,2 , F. Chouly 1,4 , A. Lejeune 1,2 , S. Joly 1,2 , K. Rekik 1,2 , G. Rolin 5,6 , T. Lihoreau 6,7 , B. Chatelain 8 , P. Sandoz 1,2 , S. Bordas 9 , E. Jacquet 1,2 (1) Univ. Bourgogne Franche-Comté, (2) FEMTO-ST Institute, UFC/CNRS/ENSMM/UTBM, Department of Applied Mechanics, Besançon, France, The aim of this paper is to evaluate the stress fields in cutaneous tissues with the long-term goal to prevent keloid development in patients. Keloids are non cancerous tumors [START_REF] Ogawa | Mechanobiology of Cutaneous Wound Healing and Scarring[END_REF] that expand continously on the skin for a number of reasons. These specific tumors affect 11 million more patients of all age, every year [START_REF] Shih | Bayat A[END_REF] and are particularly frequent in the asian and african populations. The evolution of keloids is related to genetic, biological, biophysical and biomechanical factors [3] and are considered "complex biological systems" [4]. Keloids develop due to the excessive proliferation of fibroblasts on several specific anatomical sites on the skin, that can be identified from the value of the retractability of the tissue [5].

Keloids grow like expansive scars on the skin surface. The relation between their development and the mechanical stress fields in and around the Keloid were studied in some papers [6,7] but to our knowledge one of the few attempts at formulating a growth criterion is due to [8] who studied the influence of the local state of stress on the keloid propagation direction.

The paper presents a patient-specific methodology to identify the stress field. A keloid was observed on a patient using several imaging modalities, namely ultrasound and optical microscopy. A 3D image of the surface of the keloid was used to develop a 3D geometrical model of the keloid under consideration. The hyperelastic properties of the keloid and of the healthy skin were obtained through independent testing, on the same patient using an custom-made extensometer [START_REF] Jacquet | Skin Res Techno in press[END_REF]. A three-dimensional numerical model of the keloid-skin composite was built in order to investigate the nature of the stress field in the vicinity of the keloid-skin interface. The validity of the simulation is determined by complementary data that have been recorded during the mechanical tests.

The mechanical results obtained from the numerical model are compared to displacement fields identified by Digital Image Correlation.

The paper highlights the key steps of the study:

• The development of a realistic 3D model of the keloid.

corresponding author: corr.author@science.world • The Finite Element simulation of a uniaxial non invasive traction test.

• The comparison between numerical results and experimental data to validate the numerical model.

The knowledge of the stress field in and around the keloid has the potential to improve the design of a specific device able to contain the growth of the keloid which is the topic of further investigations.

1

  EUROMECH Colloquium 595: Biomechanics and computer assisted surgery meets medical reality 29-31 August 2017, Centrale Lille, Villeneuve d'Ascq, France


Renata Zocatelli
email: renata.zoccatelli1@univ-orleans.fr

Marlène Lavrieux

Typhaine Guillemot

Léo Chassiot

Claude Le Milbeau

Jérémy Jacob

Fecal biomarker imprints as indicators of past human land uses: source distinction and preservation potential in archaeological and natural archives

Keywords: past agropastoral dynamics, fecal sterols, bile acids, archaeological and natural archives

This paper presents the potential of 5β-stanols and bile acids to act as fecal source biomarkers in order to identify and characterize past agropastoral activities in archaeological and natural archives. First of all, a molecular inventory of 5β-stanols and bile acids was made on fresh fecal human and domestic animal samples, using the same methodology to define the specificity of these molecular biomarkers. The selected species were cow, horse, pig and sheep as they are the major domestic species present in European archaeological sites. To our knowledge, our work constitutes the first report on fecal biomarkers in sheep feces. Bile acids can distinguish diet regime and species having the same diet with greater specificity than 5β-stanols. Fresh human fecal material and historical / archaeological fecal material were analyzed to assess their stability through time by calculating the coprostanol/epi-coprostanol (Cp/epi-Cp) and deoxycholic acid/cholic acid (DOC/C) ratios. Results show that bile acids are more resistant to diagenesis than 5β-stanols, at least on a 700-year time scale. Human and domestic animal fecal molecular imprints were then compared to the molecular content of 65 samples retrieved from archaeological sites, soils and lacustrine sediments to test their ability to trace past land-use dynamics. This study is the first to combine bile acids and 5β-stanols to identify a source of fecal material indifferent types of location (archaeological site, catchments and lacustrine sediments) and on different time scales. The combination of sterols and bile acids can be used in a variety of natural archives and archaeological contexts to define the origin of fecal material, to specify land-use, and to reconstruct past pastoral practices at various spatio-temporal scales. .

Introduction

Land use change is known to be one of the main factors in global change, as it affects, inter alia, biodiversity [START_REF] Smith | The onset of the Anthropocene[END_REF][START_REF] Braje | Human acceleration of animal and plant extinctions: A Late Pleistocene, Holocene and Anthropocene continuum[END_REF], biogeochemical cycles [START_REF] Foley | Global consequences of land use[END_REF], geomorphological processes [START_REF] Syvitski | Predicting the flux of sediment to the coastal zone: application to the Lanyang watershed, northern Taiwan[END_REF][START_REF] Syvitski | Sediment flux and the Anthropocene[END_REF] and climate (IPCC, 2013;[START_REF] Ruddiman | The Anthropocene[END_REF][START_REF] Ellis | Dating the Anthropocene: Towards an empirical global history of human transformation of the terrestrial biosphere[END_REF]. The development of agricultural practices, especially through animal domestication and land clearing, induced a fundamental shift in the interactions between humans and environments [START_REF] Redman | Human Impact on Ancient Environments[END_REF]Zeder et al., 2006), from local to regional and continental scales [START_REF] Braje | Human acceleration of animal and plant extinctions: A Late Pleistocene, Holocene and Anthropocene continuum[END_REF]. To predict ecosystem responses to global changes, the characterization of past land-uses and their impacts on environment/climate systems are relevant [START_REF] Ruddiman | The Anthropogenic greenhouse era began thousands of years ago[END_REF][START_REF] Dearing | Humanenvironment interactions: learning from the past[END_REF][START_REF] Hurtt | The underpinnings of land-use history: three centuries of global gridded land-use transitions, wood harvest activity, and resulting secondary lands[END_REF][START_REF] Jacob | Impacts of new agricultural practices on soil erosion during the Bronze Age in the French Prealps[END_REF].

Fossil organic molecules or molecular biomarkers are compounds preserved in archaeological and natural (soil, sediment) archives, having an unequivocal biological source (e.g. [START_REF] Van Bergen | Organic geochemical studies of soils from the Rothamsted Classical Experimentsl. Total lipid extracts, solvent insoluble residues and humic acids from Broadbalk Wilderness[END_REF]Simoneit, 2002, Killops andKillops, 2005). Biomarker analysis can provide information on environmental conditions either in predominantly natural or in more anthropized contexts such as archaeological sites and urban areas, at local or catchment scale (e.g. [START_REF] Bull | Organic geochemical studies of soil from the Rothamsted Classical Experiments II, soils from Hoosfield Spring Barley experiment treated with different quantities of manure[END_REF][START_REF] Oldfield | The late-Holocene history of Gormire Lake (NE England) and its catchment: a multiproxy reconstruction of past human impact[END_REF]Zolitschka and Ariztegui, 2007;[START_REF] Jacob | Millet cultivation history in the French Alps as evidenced by a sedimentary molecule[END_REF][START_REF] Battarbee | Palaeolimnology and its developing role in assessing the history and extent of human impact on lake ecosystems[END_REF][START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF]Lavrieux et al., , 2013a;;Zocatelli et al., 2010[START_REF] Zocatelli | Free fatty acids in Lake Aydat catchment soils (French Massif Central): sources, distributions and potential use as sediment biomarkers[END_REF][START_REF] Zocatelli | Sedimentary evidence of soil organic matter input to the Curuai Amazonian floodplain[END_REF].

Fecal biomarkers (C27, C28 and C295β-stanols and bile acids), produced in the digestive tracts of certain large mammals [START_REF] Elhmmali | Combined analysis of bile acids and sterols/stanols from riverine particulates to assess sewage discharges and other fecal sources[END_REF][START_REF] Bull | The origin of faeces by means of biomarker detection[END_REF][START_REF] Tyagi | Use of Sterol and Bile Acid Biomarkers to Identify Domesticated Animal Sources of Fecal Pollution[END_REF] are commonly used to trace human or animal fecal inputs in current environments [START_REF] Nes | Lipids in evolution[END_REF][START_REF] Elhmmali | Bile acid as a new class of sewage pollution indicator[END_REF][START_REF] Evershed | 5β-stigmastanol and related 5β-stanols as biomarkers of manuring: analysis of modern experimental material and assessment of the archaeological potential[END_REF][START_REF] Bull | Organic geochemical studies of soil from the Rothamsted Classical Experiments II, soils from Hoosfield Spring Barley experiment treated with different quantities of manure[END_REF]Bull et al., , 1999b[START_REF] Bull | An organic geochemical investigation of the practice of manuring ata Minoan site on Pseira Island, Crete[END_REF][START_REF] Bull | The origin of faeces by means of biomarker detection[END_REF]Jardé et al., 2007a;[START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF]D'Anjouet al., 2012;[START_REF] Guillemot | Evolution of pastoralism in Southern Greenland during the last two millennia reconstructed from bile acids and coprophilous fungal spores in lacustrine sediments[END_REF][START_REF] Guillemot | Environmental responses of past and recent agropastoral activities on south Greenlandic ecosystems through molecular biomarkers[END_REF]. However, to date, only a few studies have precisely distinguished human and animal sources in archaeological settlements [START_REF] Bull | The application of steroidal biomarkers to track the abandonment of a Roman waste water course at the agora (Athens, Greece)[END_REF] or in the surrounding area (Shillito et al., 2011a).

Moreover, the fecal imprint of some large mammal species often encountered in archaeological contexts, such as sheep, has never been investigated. Moreover, the published data were acquired using different protocols, which makes the comparison between the fecal imprints difficult.

To fill these gaps, this study proposes an integrated ("source to sink") approach to fecal biomarkers (5β-stanols and bile acids), from the source organisms to the archives, at different spatial and temporal scales, based on a large set of new data. First, a complete inventory of fecal biomarkers was made in current human and domestic animal feces. Cow, horse, pig and, for the first time, sheep, were selected because they represent the most abundant animals described in European archaeological sites. To identify their fecal molecular distribution, the same protocol was used, allowing a comparison between them and a characterization of the potential specificity of these biomarkers. The early and selective degradation of fecal biomarkers was then studied in order to ascertain the stability of established relationships through time. This was achieved by analyzing the molecular content of fresh human feces, recent sediments in septic tanks and archaeological middens. Finally, the fecal molecular imprints previously proposed were tested in different archives. This includes septic tanks and soils in archaeological settlements (Orléans, France), soils (Lake Aydat catchment, Massif Central, France) and lake sediments (retrieved from Lake Montcineyre, Massif Central, France and Lake Igaliku, South Greenland). These archives enable past agropastoral dynamics to be assessed at different spatio-temporal scales.

Materials and methods

Sampling

Reference samples

Fresh feces of humans, cows, horses, pigs and sheep constituted the reference samples.

Fresh animal feces were collected at the Institut National de la Recherche Agronomique (INRA, Tours, France), whereas the human fecal material was obtained from an anonymous donor.

Samples were placed in thermo coolers immediately after collection (-20 °C).

Collection samples

An ancient septic tank, archaeological middens, soils and lacustrine sediments were sampled. First, they were used to assess the early diagenesis and preservation of the fecal biomarkers' distribution through time and then for the reconstruction of past agropastoral dynamics on various space and time scales.

A total of 60 samples were selected (Appendix 1):

-Four samples were taken to assess the possible early and preferential degradation of fecal biomarkers over the first centuries after deposition: a19thcentury septic tank (St19) and three archaeological middens dated from the 14th, 15th and 16th centuries (St14, St15, St16, respectively;[START_REF] Joyeux | Mémoire de fouilles : Une nouvelle Orléans, Des secrets bien enfouis sous la 2de ligne de tramway[END_REF] collected from undisturbed settlement layers in an excavated housing located in the medieval city of Orléans (Loire Valley, France).

-10 soils (PS1-PS10), from fields pastured by bovines for at least 60 years, were sampled in the Lake Aydat catchment (Massif Central, France) in order to describe the fecal molecular distribution in grazing areas and their transfer to soils.

-A total of 46 samples were retrieved from two lacustrine sediment cores to reconstruct past and recent land uses at a catchment scale. Two catchments with distinct anthropogenic contexts were selected:

(i) Lake Montcineyre catchment (Massif Central, France) located twenty kilometers away from Aydat (Fig. 1). Archaeological and historical data attest toagropastoral activities since the Middle Ages [START_REF] Charbonnier | Histoire de l'Auvergne des origines à nos jours: Haute et Basse-Auvergne[END_REF]Bayard, 2010 and references therein).

However, despite the presence of archaeological settlements in the catchment since the Neolithic, agropastoral activities and their impact on the local ecosystems remain poorly understood at catchment scale [START_REF] Chassiot | Forçages naturels et anthropiques sur la sédimentation holocène en domaine lacustre. Application aux lacs naturels d'Auvergne et aux réservoirs des bassins versants de la Loire et d[END_REF][START_REF] Chassiot | Historical seismicity of the Mont Dore volcanic province (Auvergne, France) unraveled by a regional lacustrine investigation: New insights about lake sensitivity to earthquakes[END_REF]. 13 samples were retrieved from a sedimentary core from Lake Montcineyre (MO1-MO13), spanning the last 800 years [START_REF] Chassiot | Historical seismicity of the Mont Dore volcanic province (Auvergne, France) unraveled by a regional lacustrine investigation: New insights about lake sensitivity to earthquakes[END_REF].

(ii) Lake Igaliku catchment, located in South Greenland. Two distinct agropastoral periods are attested regionally: the Norse settlement, between AD 986 and ca. AD 1450 and the modern period, since the 1920s [START_REF] Gauthier | Pollen and nonpollen palynomorph evidence of medieval farming activities in southwestern Greenland[END_REF][START_REF] Massa | A 2500 year record of natural and anthropogenic soil erosion in South Greenland[END_REF]. Located one and a half kilometers away from the lake, the village of Igaliku was the Norse episcopal capital of Greenland and one of the most important agropastoral centers, judging from archaeological structures (Arnebord et al., 2007). Nowadays, agropastoral activities are characterized by predominant sheep breeding [START_REF] Austrheim | Sheep grazing in the North-Atlantic regiona longterm perspectiveon management, resource economy and ecology[END_REF]. Two farms composed of stables, barns, and hay fields are located on the southern shore of Lake Igaliku. 33 sediment samples (Iga1-Iga33) spanning the last 2000 years were retrieved from Lake Igaliku [START_REF] Guillemot | Environmental responses of past and recent agropastoral activities on south Greenlandic ecosystems through molecular biomarkers[END_REF].

Fecalbiomarkeranalyses

Samples were dried at 30 °C during 48h and then crushed. Ca. two grams of dried samples were extracted using an accelerated solvent extractor (Dionex® ASE 200, 100 °C and 1000 psi) with a mixture of CH2Cl2:MeOH (9:1 v/v). Total lipid extract was fractionated into neutral and acidic compounds using solid phase extraction on Supelclean LC-NH2 SPE bulk packing [START_REF] Zocatelli | Free fatty acids in Lake Aydat catchment soils (French Massif Central): sources, distributions and potential use as sediment biomarkers[END_REF]. Neutral compounds were eluted with CH2Cl2:isopropanol (2:1) and acidic compounds with ether:formic acid (9:1) after acidification. Total neutral and acid fractions were then dried under N2. As described in [START_REF] Lavrieux | Occurrence of triterpenyl acetates in soil and their potential as chemotaxonomical biomarkers of Asteraceae[END_REF], the sterols were isolated from the total neutral fraction by flash chromatography on a Pasteur pipette filled with silica (activated 24h at 120°C, then deactivated with H2O, 5%), using a sequence of solvents of increasing polarity (heptane, heptane:toluene and heptane:ethyl acetate). The acidic fraction was methylated by adding a mixture of anhydrous MeOH and acetyl chloride (55°C for one hour). It was then separated into fatty acid methyl esters (FAMEs) and hydroxy acid methyl esters by flash chromatography with a Pasteur pipette filled with silica, using CH2Cl2, CH2Cl2:isopropanol and MeOH [START_REF] Guillemot | Evolution of pastoralism in Southern Greenland during the last two millennia reconstructed from bile acids and coprophilous fungal spores in lacustrine sediments[END_REF].Finally, sterol and bile acid fractions were silylated using BSTFA and pyridine (60°C for one hour). Prior to gas-chromatography and mass-spectrometry (GC-MS) analyses, 5α-cholestaneand nor-deoxycholic acid standards were added to all fractions for quantitation purposes.

Fractions were analyzed by gas chromatography-mass spectrometry (GC-MS) with a Polaris TRACE-GCQ. The chromatograph was fitted with an Rtx-5MS column (30 m, 0.25 mm i.d., 0.25 μm film thickness). The GC operating conditions were: 40°C (hold 1 min) ramping from 40°C to 120°C at 30°C.min -1 , then from 120°C to 300°C at 5°C.min -1 , hold 30 min. The samples were injected automatically in splitless mode, with the injector temperature set at 280°C. Helium was the carrier gas (1 ml.min -1 ).The MS was operated in the electron ionization (EI) mode at 70 eV and scanned from m/z50-650. Identification of fecal biomarkers was based on published retention times and mass spectra [START_REF] Elhmmali | Bile acid as a new class of sewage pollution indicator[END_REF][START_REF] Bull | An organic geochemical investigation of the practice of manuring ata Minoan site on Pseira Island, Crete[END_REF] and also on authentic standards of sterols and bile acids, obtained from Sigma Aldrich Chemical Co. and Alfa Aesar. Because of possible coelution with other components, compound concentrations were estimated by measuring peak areas on ion specific chromatograms. Fecal biomarker concentrations were estimated after calculation of a correction factor between the peak area on the ion specific chromatogram and the peak area on the total ion current (TIC) chromatogram for authentic standards. The minimum level of detection using GC-MS was about 10 ng.

Results and Discussion

Fecal biomarker inventory

Nine sterols (sensulato): five fecal stanols [coprostanols or 5α-stanols, coprostanol (Cp), epi-coprostanol (epi-Cp), methylcoprostanol (mCp), epi-ethylcoprostanol (epi-ethCp) and ethylcoprostanol (ethCp)]; two 5α-stanols [(cholestanol (Chln) and ethylcholestanol (ethChln)] and two sterols [sensustricto; cholesterol (Chl) and sitosterol (Sit)] were detected in thereference samples and alsoin the samples. In addition, six bile acidswere identified: lithocholic acid (LC), deoxycholic acid (DOC), chenodeoxycholic acid (CDOC), cholic acid (C), hyodeoxycholic acid (HDOC) and ursodeoxycholic acid (UDOC; Table 1 and2). 3.2. Biomarker distribution in fresh fecal material

Sterols

The distribution of sterols varied widely among the considered species (Table 1; Fig. 2a ande). In the herbivorous group, cow feces showed the highest values of total coprostanols (TC, 1817 µg /g of dried fecal material; Table 1), followed by sheep (1302 µg/g) and horse (336µ/g). In herbivorous fecal samples ethCp was the main compound followed by mCp (Table 1). In the omnivorous group, TC concentrations were one order of magnitude greater than in the herbivorous group, with a predominance of Cp. Human and pig feces had rather similar fecal sterol profiles. Indeed, Cp and ethCp were the major compounds in pig (Cp = 3522 µg/g, 48% of TC; ethCp = 3093µg/g; 41% of TC) and human feces (Cp = 12251µg/g, 71% of TC; ethCp = 3929µg/g, 23% of TC). However, the proportion of TC was more than twice as high in human asin pig feces (Table 1). More especially, Cp contents in human feces were 3.5 times higher than in pig feces, which is consistent with previously published data [START_REF] Leeming | Using fecal sterols from humans and animals to distinguish fecal pollution in receiving waters[END_REF].

In the light of these results, the differences in fecal sterol distributions cannot distinguish between mammals having the same diet. Previous studies using ratios of fecal and non-fecal sterols barely succeeded in discriminating between mammals (e.g. [START_REF] Grimalt | Assessment of fecal sterols and ketones as indicators of urban sewage inputs to coastal waters[END_REF]Bull et al., 1999a[START_REF] Bull | The origin of faeces by means of biomarker detection[END_REF][START_REF] Bull | The application of steroidal biomarkers to track the abandonment of a Roman waste water course at the agora (Athens, Greece)[END_REF]Jardé et al., 2007a,b;Shillito et al., 2011a;[START_REF] Baeten | Fecal biomarker and archaeobotanical analyses of sediments from a public latrine shed new light on ruralisation in Sagalassos, Turkey[END_REF], assuming that the variations in coprostanol concentrations are not controlled by selective degradation.

These ratios were either developed to attest to pollution (Sr1> 0.7; [START_REF] Grimalt | Assessment of fecal sterols and ketones as indicators of urban sewage inputs to coastal waters[END_REF] or to identifyanimal sources (Sr2; Bull et al., 1999a):

Cp/(Cp + epi-Cp + Chln) (Sr1) (ethCp +epi-ethCp)/(Cp +epi-Cp) (Sr2)
We tested these two ratios in the reference samples. For all of them, Sr1 values were greater than 0.7 with Sr1human>Sr1pig>Sr1cow>Sr1horseand Sr1sheep (Table 1) indicating that omnivorous animals produce more Cp than herbivorous animals. The Sr2 ratio, used to distinguish ruminant from pig/human material sources, was below 1.0 for human and pig feces (0.32 and 0.76, respectively), and above 1.0 for herbivorous feces (horse = 2.29, sheep = 2.67 and cow = 3.05).

As expected, Sr1 and Sr2 ratios measured in the reference samples corroborated previous studies conducted by [START_REF] Grimalt | Assessment of fecal sterols and ketones as indicators of urban sewage inputs to coastal waters[END_REF] and Bull et al. (1999aBull et al. ( ,b,c, 2002) ) and can be considered reliable as long as there is no selective degradation and/or mixing of different animal imprints.

Bile acids

The distribution of bile acids also varied widely among the different animal species (Table 1; Figure 2b andf). In the herbivorous group, all species produced DOC, whereas LC was only produced by cows and horses (Fig. 2b). It is therefore possible to distinguish them from sheep, whose feces only contained DOC (3.21 µg/g of dried fecal material). Cows and horses can be distinguished by the total bile acid content, which was twice as high in cow (0.56 µg/g) as in horse feces (1.17 µg/g).

In the omnivorous group, human and pig fecal material can be discriminated by the bile acid composition. In human fecal samples, DOC was the major bile acid detected, followed by LC (Fig. 2f; 35.56 and 45.44µg/g respectively; Table 1). Other bile acids such as CDOC (2.15 µg/g), C (1.79 µg/g) and UDOC (1.24 µg/g) were detected in lower contents. While DOC was the most abundant bile acid in human fecal material, it was not detected in pig fecal samples.

In the latter, the major bile acids found were HDOC (32.01 µg/g) followed by LC (21.07 µg/g) and UDOC in lower contents (2.31 µg/g). Thus, bile acid distributions allow a clear distinction not only between herbivorous and omnivorous mammals, but also between animals having the same diet.The fecal molecular inventory of a large range of animal feces presented in this study not only confirms, but also completes previous studies [START_REF] Bethell | The study of molecular markers of human activity: the use of coprostanol in the soil as an indicator of human fecal material[END_REF][START_REF] Elhmmali | Bile acid as a new class of sewage pollution indicator[END_REF][START_REF] Bull | The origin of faeces by means of biomarker detection[END_REF][START_REF] Bull | The application of steroidal biomarkers to track the abandonment of a Roman waste water course at the agora (Athens, Greece)[END_REF][START_REF] Tyagi | Use of Sterol and Bile Acid Biomarkers to Identify Domesticated Animal Sources of Fecal Pollution[END_REF]. Indeed, to our knowledge, our work constitutes the first report onfecal biomarkers in sheep feces.

Fecal signal preservation

Some studies attest to a rapid degradation of coprostanols by anaerobic bacteria present in sewage and natural waters [START_REF] Gaskell | Short-term diagenesis of sterols[END_REF][START_REF] Grimalt | Assessment of fecal sterols and ketones as indicators of urban sewage inputs to coastal waters[END_REF], potentially masking their original sources [START_REF] Mermoud | Steroidal ketones in the early diagenetic transformations of ∆ 5 sterols in different types of sediments[END_REF][START_REF] Rushdi | Compositions and sources of extractable organic matter in Mesopotamian marshland surface sediments of Iraq: II. Polar compounds[END_REF]. Nevertheless,in soils, other studies showed that these biomarkers are able to survive and highlight human activities during several millennia [START_REF] Pepe | Étude d'une fosse du 13 ème siècle par les marqueurs biogéochimiques: chantier archéologique du Louvre (Paris)[END_REF][START_REF] Bethell | The study of molecular markers of human activity: the use of coprostanol in the soil as an indicator of human fecal material[END_REF]Shillito et al., 2011a, b), assuming good preservation of these biomarkers. Few studies have attempted to assess such discrepancies. [START_REF] Elhmmali | Bile acid as a new class of sewage pollution indicator[END_REF] showed that DOC was better preserved than Cp in sewage particulate organic matter before and after microbiological treatments, suggesting that bile acids are probably better preserved than sterols.

To better assess the respective preservation of fecal biomarkers, we compared the fecal imprint of the human reference sample to samples from the septic tank and from archaeological middens, covering the 19th to the 14th centuries (St19, St16, St15 and St14). All these samples were characterized by a major/exclusive human fecal input. InSt19, St16, St15 and St14

samples, Sr1 values ranged between 0.87 and 0.92 (Table 2) and Sr2 exhibited low values (0.33-0.84), all attesting toan omnivorous fecal imprint. In addition,Cp was the major sterol observed (between 0.58 and 10.54 mg/g), followed by ethCp (Fig. 2; Table 1). These indices,associated to the presence of CDOC and C, and the absence of HDOC, attest to a human fecal input.

Fecal sterols and bile acid contents decreased with the age ofthe samples (Table 2, Fig. 3).

This trend could be attributedto a degradation of fecal biomarkers, but could also result from a decreasing occupation of the site. Preservation of fecal biomarkers in human feces over time was assessed through the calculation of theCp/epi-Cpand DOC/C ratios.The Cp/epi-Cp ratio is already used to overcome possible diagenetic biases related to the degradation of Cp into epi-Cp [START_REF] Simpson | Early anthropogenic soil formation at Tofts Ness, Sanday, Orkney[END_REF]Bull et al., 1999a).In addition to this ratio, we used the DOC/C ratio because DOC is a secondary by-product of C formed in anaerobic environments via enzymatic reactions [START_REF] Bull | The application of steroidal biomarkers to track the abandonment of a Roman waste water course at the agora (Athens, Greece)[END_REF]. A relatively low DOC/C ratio variability over time indicates good preservation. The DOC/C ratio can be used if it is assumed that the chemical difference between DOC and C is minor, implying that the polarity difference between the two compounds is negligible and thus would not affect the solubility and adsorption to soil/sediment particulates.

From today to the 14th century, the DOC/C ratio remainedconstant in our samples (DOC/C ~20, Fig. 3e), whereas the Cp/epi-Cp ratiodecreasedduring the first century (from ~70 to ~10).Assuming that this decrease in the Cp/epi-Cp ratio is not related to diet changes over centuries, it would suggest a greater degradation of fecal sterols than of bile acids. This better preservation of bile acids compared to 5β-stanols supports previous observations made by [START_REF] Elhmmali | Bile acid as a new class of sewage pollution indicator[END_REF]. Regarding these results, it appears that bile acids are better preserved, at least at the multi-centennial scale.

Applications of fecal biomarkers to reconstruct agropastoral activities

To trace and characterize past agropastoral activities in and outside archaeological sites, we applied the fecal biomarker distribution defined from our reference samples to several natural archives over various temporal scales.

Aydat catchment soils

In soil samples retrieved from lake Aydat catchment (Massif Central, France), previous studies based on fecal biomarkers evidenced recent husbandry practices [START_REF] Lavrieux | Preservation of an ancient grassland biomarker signature in a forest soil from the French Massif Central[END_REF][START_REF] Zocatelli | Free fatty acids in Lake Aydat catchment soils (French Massif Central): sources, distributions and potential use as sediment biomarkers[END_REF]. Here, this was achieved by re-exploiting the dataset of fecal biomarker distribution and comparing it to the reference samples. The distribution of sterols and bile acids was quite similar over soil samples (Table 2, Fig. 4), with TC varying between 0.56 and 26.9mg/g soil, and with 8-37% of coprostanols (ethCp and Cp; Table 2). Samples also contained significant amounts of LC and DOC (0.08-0.36 and 0.84-4.25 µg/g). No HDOC, which is specific of pig feces, was detected. Associated to the distribution of sterols, the identification of DOC and the absence of C and CDOC argue for a fecal input from cows to the soil, consistent with current husbandry in the catchment. In any case, this study shows that the recent fecalcompoundsdistribution istransferred to soils andconsistent with current agropastoral practices. For paleosoils located in archaeological sites fecal biomarkers are a promising tool inarchaeological research regarding the use of space and human activities.

Lake Montcineyre sediments

A high diversity of fecal biomarkers was inventoried in the 800-yr-long lacustrine core retrieved from Lake Montcineyre. Fecal biomarkers such as Cp, epi-Cp, mCp, ethCp, epi-ethCp, LC and DOC were detected all along the core. Fig. 5b shows the evolution of total coprostanols, Sr2 ratio, LC and DOC. Although a small amount of Cp was detected, the absence of CDOC, C and UDOC indicates that there was no significant fecal human input (Table 2, Fig. 5). Sr2 values above 1 together with DOC concentrations seven times higher than LC concentration over the whole archive are indicative of the presence of livestock composed mainly of cows over the last 800 years (Fig. 5b). Intervals with higher DOC and LC concentrations could indicate periods of increased agro-pastoral activities. Conversely, because TC did not exhibit the same trends, it probably illustratesdifferential degradation between bile acidsand fecal sterols. In view ofthe better preservation of bile acids compared to that of 5βstanols confirmed and described in section 3.3., it isthe evolution of LC and DOC biomarkersthat describes agropastoral activities during the last 800 years in the Montcineyre catchment.

3.4.3. Lake Igaliku sediments

The regional historical context of SouthGreenland is very different from that of the Massif Central. In the lake Aydat and Montcineyre catchments, agropastoral activities occurred without interruption since the Neolithic. In South Greenland only two agropastoral periods (the Norse settlement between AD 986 to ~ AD 1450 and the last century) are attested. Figure 6 shows the evolution of TC, Sr2 ratio and DOC contents during the last two millennia. The Sr2 ratio and DOC contents evidence the two phases synchronous with these two periods of agropastoralism. As here, [START_REF] Guillemot | Evolution of pastoralism in Southern Greenland during the last two millennia reconstructed from bile acids and coprophilous fungal spores in lacustrine sediments[END_REF] identified two main peaks of DOC Igaliku lacustrine sediments. During the last century, Sr2 values above 10 attest to the presence of herbivorous livestock grazing in the catchment. High contents of ethCp associated to the presence of DOC as the only bile acid suggest a predominant sheep fecal input. This is confirmed by current agropastoral activities which are mainly characterized by sheep breeding [START_REF] Austrheim | Sheep grazing in the North-Atlantic regiona longterm perspectiveon management, resource economy and ecology[END_REF]. In the Norse period, less information concerning agropastoral activities are available. In south Greenland, three archeological studies showed a predominance of ovine bones with, among others, cow and pig bones [START_REF] Mcgovern | The zooarcheology of Ø17a[END_REF][START_REF] Edvardsson | Archaeological excavations at Qassiarsuk 2005 -2006[END_REF][START_REF] Madsen | Pastoral settlement, farming and hierarchy in Norse Vatnahverfi, South Greenland[END_REF]. In the sediments of Lake Igaliku, a maximum of Sr2 at 26 is recorded and evidences herbivorous livestock grazing around Lake Igaliku; low Cp contents and the absence of CDOC, C and UDOC indicate no significant human fecal contribution while a similar fecal biomarker imprint was identified during the last century. High contents of ethCp and the presence of DOC alone suggest that Norse livestock was mainly composed of sheep, as confirmed by archaeozoological data [START_REF] Mcgovern | The zooarcheology of Ø17a[END_REF][START_REF] Edvardsson | Archaeological excavations at Qassiarsuk 2005 -2006[END_REF][START_REF] Madsen | Pastoral settlement, farming and hierarchy in Norse Vatnahverfi, South Greenland[END_REF]. Considering the climatic and environmental conditions of south Greenland (positive temperatures only seven months a year and difficulties to cultivate fodders; [START_REF] Austrheim | Sheep grazing in the North-Atlantic regiona longterm perspectiveon management, resource economy and ecology[END_REF][START_REF] Cappelen | Weather observations from Greenland 1958-2013[END_REF], as sheep are more resistant to cold conditions and needed less fodder than cows, it would be logical that the Norse Greenlandic livestocks were mainly constituted of sheep, as nowadays. Between the two agropastoral phases of sheep breeding, TC and DOC contents drop, attesting to a decrease of pastoral activities around Igaliku.

This study has provided new insight in to the determination of fecal material sources from biomarkers in natural archives. A further step will be to interpret the varying concentrations of biomarkers in terms of livestock population changes. This should be achieved through a calibration aiming at establishing quantitative relationships and taking into account production, transport and sedimentationprocesses (Eglinton and Eglinton, 2008).

Conclusions

Fecal molecular imprints of the most abundant big mammals encountered in archaeological sites in Europe were defined, using a unique extraction protocol on fresh cow, horse, sheep, pig and human feces. This study determined the fecal molecular distribution of sheep faeces. The sterol distribution in mammal feces clearly discriminates omnivorous (human and pig) from herbivorous (cow, horse and sheep) animals, but does not allow any differentiation at the species level. However, the bile acid patterns offer an unequivocal imprint for most of the species considered. Comparing fecal biomarkers in fresh human feces with an ancient septic tank and archaeological middens with known human fecal pollution showed that bile acids are better preserved over time than sterols, as suggested by previous studies. In the light of all these results, it is now possible to discriminate human and domestic animal species, using fecal molecular biomarkers and especially bile acids, showing a better specificity and preservation.

Thanks to the good preservation and specificity of fecal biomarkers, it was possible to test their ability to trace past land-use dynamics in various anthropication contexts, in natural soils and lacustrine sediments, aged from the last decennia to millennia. A complete fecal molecular inventory in the Lake Aydat catchment soils, Lake Montcineyre and Lake Igaliku sediments is made and brings information about past land uses, allowing a reconstruction of local past pastoral practices and enlightening us on past interactions between human and environments. Values of Sr1 > 0.7 indicate fecal material detection [START_REF] Grimalt | Assessment of fecal sterols and ketones as indicators of urban sewage inputs to coastal waters[END_REF]. Values of Sr2 > 1 indicate the predominance of herbivorous feces [START_REF] Bull | The origin of faeces by means of biomarker detection[END_REF]. Cp: coprostanol, epi-Cp:

epi-coprostanol, Chl: cholesterol, Chln: cholestanol, mCp: methyl-coprostanol, ethCp: ethylcoprostanol, Sit: sitosterol, ethChln: ethyl-cholestanol, LC: lithocholic acid, DOC: deoxycholic acid, CDOC: chenodeoxycholic acid, C: cholic acid, HDOC: hyodeoxycholic acid and UDOC: ursodeoxycholic acid. [START_REF] Bull | The origin of faeces by means of biomarker detection[END_REF]. Cp: coprostanol, epi-Cp: epicoprostanol, Chl: cholesterol, Chln: cholestanol, mCp: methyl-coprostanol, ethCp: ethylcoprostanol, Sit: sitosterol, ethChln: ethyl-cholestanol, LC: lithocholic acid, DOC: deoxycholic acid, CDOC: chenodeoxycholic acid, C: cholic acid, HDOC: hyodeoxycholic acid and UDOC: ursodeoxycholic acid.

FIGURE CAPTIONS

Fig. 1 :

 1 Fig. 1: Location of study sites: In Greenland (a), Igaliku catchment (b; dotted line, LI -Lake Igaliku), Orléans archaeological site and Massif Central(c) with, for the latter the Aydat catchment (dotted line, d; LA -Lake Aydat) and theMontcineyre catchment (dotted line, e; LM -Lake Montcineyre).

Fig. 2 :Fig. 3 :

 23 Fig.2: Distribution of sterols and bile acids in animal fecal materials in reference samples (a, b, e and f). Note the different scales used between sterols and bile acids, and between herbivorous and omnivorous fecal biomarkers. Example of total ion current (TIC) chromatogram of sheep (c and d) and human (g and h) feces. For the nomenclature of compounds refer to Section 3.1.

Fig. 4 :

 4 Fig. 4: Relative average concentrations of sterols and bile acids in soil lipid extracts (a,b). GC-MS chromatograms (TIC) of sample PS8 for alcohol and acidic fractions (c,d). Nomenclature of compounds is presented in section 3.1. 24α:C24 α-hydroxycarboxylic acid; 22ω:C22 ωhydroxycarboxylic acid; 24ω:C24 ω-hydroxycarboxylic acid; 26ω:C26 ω-hydroxycarboxylic acid; C30-OH:C30 n-alkanol; C32-OH:C32n-alkanol.

Fig. 5 :

 5 Fig.5: Evolution of Total Coprostanols (Cp + epi-Cp + mCp + epi-ethCp +ethCp), Sr2 ratio, LC and DOC contents in lacustrine sediments (a). Specific ion mass (m/Z) chromatograms of alcohol fraction (m/Z = 215+255+213+368+383+398+484) and acidic fraction (m/Z = 208+215+253+255+368) of sampleMO-1are shown (b,c). Nomenclature of compounds is presented in section 3.1.

Fig. 6 :

 6 Fig.6: Evolution of Total Coprostanols (Cp + epi-Cp + mCp + epi-ethCp +ethCp), Sr2 ratio and DOC contents over the last two millennia in lacustrine sediments (a). Specific ion mass (m/Z) chromatograms of alcohol fraction (m/z = 215+255+213+368+383+398+484) and acidic fraction (m/Z = 208+215+253+255+368) of sample IGA-82 are shown (b,c). Nomenclatures of compounds are given in section 3.1.

  Fig.1 626

  Zocatelli, R.,Jacob, J., Turcq, B., Boussafir, M., Sifeddine, A., Bernardes, M.C., 2010. Molecular evidence for recent turf cultivation in Northeast Brazil (Lagoa do Boqueirão, RN State). Organic Geochemistry 41, 427-430.Zolitschka, B. and Ariztegui, D., 2007. Lacustrine and marine archives of environmental variability across South America. Quaternary International 161, 1-3.

TABLE CAPTIONS Table 1 :

 CAPTIONS1 Sterol and bile acids contents (µg/g) and ratios of fecal sterols for reference samples.

Table 2 :

 2 Sterol and bile acids contents (mg/g and µg/g, respectively) and ratios of fecal sterols for the samples. Values of Sr1 > 0.7 indicate feces samples. Values of Sr2 > 1 indicate the predominance of herbivorous feces

Table 1 :

 1

		Horse	Cow	Sheep	Human	Pig
	Sterols
	Cp	40	275	203	12251	3552
	epi-Cp	29	49	36	0	0
	Chl	36	230	164	866	376
	Chln	17	90	87	857	941
	mCp	110	506	425	980	888
	ethCp	109	859	456	3929	3093
	epi-ethCp	48	128	182	0	0
	Sit	124	395	392	707	1102
	ethChln	44	40	72	0	80
	Total Coprostanols	336	1817	1302	17160	7533
	% coprostanols	60.33	70.64	64.55	87.70	76.29
	Sr1	0.70	0.75	0.70	0.93	0.79
	Sr2	2.29	3.05	2.67	0.32	0.76
	Bile Acids
	LC	0.07	0.07	0.00	35.56	21.07
	DOC	0.48	1.10	3.21	45.44	nd
	CDOC	nd	nd	nd	2.15	nd
	C	nd	nd	nd	1.79	nd
	HDOC	nd	nd	nd	nd	32.01
	UDOC	nd	nd	nd	1.24	2.31
	Total Bile Acids	0.56	1.17	3.21	86.18	55.38

Acknowledgments

The work received financial support from the OTARIE Project (Région Centre, European Council) and the ERODE Project (EC2CO, CNRS/INSU).We thank theInstitut National de la Recherche Agronomique (INRA) for the reference samples, Pascal Joyeux (INRAP), and Emmanuel Chapron (GEODE UMR 5602 CNRS,University of Toulouse).We also thank the French Polar Institute (IPEV) and the ANR CEP&S "Green Greenland" project (ANR-10-CELP-0008) and particularly Vincent Bichet, Emilie Gauthier, Hervé Richard (Chronoenvironnement UMR 6249,University Franche-Comté)and anonymous donors for sampling authorizations and/or providing some of the collection and reference samples.We would also like to thank the two anonymous reviewers for suggestions and comments which have helped to improve the quality of this manuscript.


Claudia Weidensteiner

PhD Noureddine Kerioui

MD Bruno Quesson

Baudouin Denis De Senneville

Hervé Trillaud

MD Chrit T W Moonen
email: moonen@imf.u-bordeaux2.fr

PhD Baudoin Denis De Senneville

Stability of real-time MR temperature mapping in healthy and diseased human liver

Keywords: MRI, temperature mapping, liver, Sensitivity Encoding, Proton Resonance Frequency

Introduction

Minimally invasive, percutaneous ablative therapies under image guidance with thermal energy sources such as radio frequency (RF), and laser are increasingly used for tumor ablation in the clinic because of their efficacy and low cost. Ultrasound (US) or CT imaging is used for positioning the RF needle or laser fiber. However, a precise and quantitative on-line control of the ablation is not possible with these imaging modalities. The imaging method used for clinical guidance should ideally have a good spatial, temporal, and thermal resolution and should be easily implemented. In addition, the thermal and spatial resolution should be sufficient to detect temperature rise in the tissue around the lesion in order to 1) enable real-time control of the ablation of the complete tumor and of a safety margin of 5 -10 mm around the tumor and to 2) avoid unwanted thermal damage in healthy tissue. MRI can monitor temperature changes quantitatively using the temperature-dependent shifts of the proton resonance frequency (PRF) ( 1 ) or the temperature dependency of T1 ( 2 ).

Therefore temperature mapping with MRI can be used to predict the treatment outcome during the heating procedure. T1-weighted sequences have been used in clinical trials of thermal ablation in the liver ( 3 , 4 , 5 ). However, calibration of the method is difficult due to non-linear effects when coagulation occurs. Therefore, quantitative temperature mapping for ablation procedures is difficult with T1-methods. The PRF-method has proven to be precise at a field strength ≥ 1 T ( 6 ) when based on RF-spoiled, gradient echo imaging. The PRF change is linear with temperature and independent of tissue type when fat suppression is applied. Its disadvantage is the sensitivity to motion artifacts, since the method is based on the phase difference between two complex MR images. For mobile organs, such as the liver, motion may induce erroneous temperature mapping and therefore, motion correction has to be applied. Preliminary accounts of PRF-based temperature mapping in the human liver has recently been reported based on the following motion correction methods: 1) respiratory triggering or gating ( 7 ); 2) the use of motion insensitive sequence like radial scanning ( 8 ). In addition, suspension of respiration has been applied under general anesthesia to improve temperature mapping ( 9). The latter approach may not be feasible due to the patient's condition.

Fast imaging sequences have to be applied for real-time control because they are less sensitive to respiratory motion artifacts and allow gated acquisition in the expiration phase.

However, the PRF-method is most sensitive if TE is in the same range as T2 * ( 10 ) which is rather short in the healthy liver (20 -30 ms). Additionally, T2 * of the liver may vary depending on the pathology. Therefore, the duration of the EPI echo-train in fast gradient-echo sequences has to be limited to obtain an accurate temperature estimate. A possibility to shorten acquisition time is the use of partial parallel acquisition techniques like SENSE ( 11) with the use of a phased array coil. The SENSE technique has previously been used for imaging of moving objects like the heart ( 12). Phase maps acquired with SENSE techniques have not yet been reported for temperature mapping.

In a recent paper ( 13 ) we have shown that PRF temperature mapping can be successfully performed in healthy rabbit liver in vivo during laser ablation. A segmented gradient echo EPI sequence with fat suppression, respiratory gating, and flow suppression was performed. Based on those results, the method was adapted for human liver. In order to prepare clinical trials on image-guided ablation of human liver tumors, the quality of temperature mapping should be tested under clinical conditions. The aim of this work was to investigate the stability of PRF temperature mapping on a clinical scanner in the liver of volunteers and liver tumor patients. The stability was assessed by measuring the standard deviation of a series of temperature maps at baseline temperature, i.e. the tissue was not heated and not ablated. For a given TE, the phase stability can be directly correlated with temperature stability. Respiratory gating was combined with the rapid acquisition scheme of a SENSE segmented EPI-sequence to improve motion correction. The key questions of this study were the following: 1) Is the stability of the method sufficient to obtain reliable temperature information with measurement uncertainty of a few °C (small compared to the temperature increase needed for ablation studies)? 2) Can the method be performed in liver tumor patients suffering from cirrhosis or steatosis? 3) Is the performance different in patients with cirrhotic livers (having primary tumors) than in patients with healthy livers (having secondary tumors)?

Materials and Methods

Patient and Volunteer Handling

Five healthy volunteers and thirty patients were recruited for this temperature mapping study under an approved study protocol. The patients were divided in 2 groups: 1) cirrhotic livers (21 patients with hepatocellular carcinoma (HCC)) and 2) non-cirrhotic livers (7 patients with liver metastases: 4 from colorectal tumor, 2 from neuroendocrine tumor, 1 from gastric tumor; 2 patients with angioma). One patient of the first and four patients of the second group had liver steatosis. Four patients of the first group had advanced cirrhosis. A total of 35 imaging sessions was performed in the first group, since many patients were scanned more than once. Twenty-six of all patients underwent ultrasound guided RF ablation of liver tumors several weeks before this study. Their written consent was received before the start of the protocol. The volunteers and patients were lying supine with a flexible 4-element phased array body coil (standard equipment; Philips Medical Systems, Best, The Netherlands) wrapped around their chest. A respiratory sensor (standard equipment; Philips Medical Systems, Best, The Netherlands) was positioned between their chest and the coil for continuous monitoring of breathing and respiratory gating of the sequence. The volunteers and patients were allowed to breathe freely (respiratory cycle duration approx. 4 sec) during temperature mapping. Hepatic T2 * was determined in 3 healthy volunteers and in 11 patients (9 patients with HCC due to cirrhosis; 1 patient with HCC due to hemachromatosis; 1 with a healthy liver and a benign tumor) with the same phased array coil as described above. One volunteer and one patient with cirrhosis were scanned twice. The measurement was performed in one breath hold of 15 sec duration.

MR Protocol for Temperature Mapping

MR experiments were performed on two 1.5 T Philips Intera clinical scanners (Philips Medical Systems, Best, The Netherlands) using the built-in quadrature body coil for excitation and a 4-element phased array flexible body coil for signal detection. For comparison, additional experiments were performed in 10 patients for which the quadrature body coil was used for both excitation and detection.

In the volunteer experiments without SENSE acceleration, a segmented EPI sequence with 9 gradient echoes (EPI-factor 9) and TE/TR of 16 ms/119 ms was used (TE range 6.1 ms -23.7 ms). The FOV was 300 mm x 300 mm to 350 mm x 350 mm (depending on the size of the abdomen); and the matrix was 112 x 90 (zerofilled to 128 x 128), yielding an in-plane resolution of 2.7 mm x 3.3 mm to 3.1 mm x 3.9 mm. Three parallel slices (thickness 5 mm, gap 6 mm) were acquired in an interleaved mode within one respiratory cycle gated at the end of expiration (acquisition time 1.2 sec). The phase encoding direction was the left-right direction. Since fat does not show a temperature dependent frequency shift fat signal must be suppressed to avoid an erroneous temperature map. This is especially important in the liver. Thus, a water-selective binomial spectral-spatial excitation pulse (25° flip angle) was applied for slice selection, as was described previously ( 14 ). Two parallel saturation slabs (thickness 60 mm, gap 8 mm) were used to suppress flow artifacts. For comparison, protocols without saturation slabs with the same spatial resolution were acquired (TR shortened to 100 ms). Data were collected with just one coil element of the array (the one closest to the liver), and also with all 4 elements.

A SENSE acceleration factor of 1.5 and 2 was performed using acquisition parameters described above. The acquisition time was reduced to 0.79 sec for SENSE factor 1.5 and to 0.60 sec for SENSE factor 2, due to partial acquisition of k-space.Before the start of the temperature mapping protocol a sensitivity map of the phased array coil was acquired to allow SENSE reconstruction and reconstruction of the phase map from the phased array data.

For the patient studies, a segmented EPI sequence as described above was used, but with only 7 gradient echoes and TE/TR of 12 ms/119 ms (TE range 5.9 ms -16.4 ms). The FOV was 300 mm x 300 mm to 400 mm x 400 mm (depending on the size of the abdomen); and the matrix was 112 x 89 (zerofilled to 128 x 128), yielding an in-plane resolution of 2.7 mm x 3.3 mm to 3.6 mm x 4.5 mm. The slices were placed at the original tumor position in each patient. Data were collected with all 4 receiver coil elements. A SENSE acceleration factor of 1.5 was used. In addition, images with an EPI-factor of 9, and with TE=16 ms, were acquired for the patient group with healthy liver. A reference map of the coil sensitivity was acquired before the start of the temperature mapping protocol. A FOV of 500 mm x 500 mm and a matrix size of 144 x 112 (zerofilled to 256 x 256) were employed for the experiments using the built-in quadrature body coil for excitation and signal detection, yielding a nominal in-plane resolution of 3.5 mm x 4.5 mm. TE/TR was 16 ms/119 ms; EPI-factor was 7. The gating width was 1200 ms, resulting in a time resolution of 3 slices in 2 respiratory cycles.

Data Processing

Reconstruction of magnitude and phase MR images was done on-line using the standard Philips hard-and software available on clinical scanners. Analysis of stability and performance of the method was performed off-line in the IDL program environment (Research Systems Inc., Boulder, CO, USA). To obtain a temperature map, the phase map was subtracted from a reference phase map (which was checked to be free of severe motion artifacts), yielding the phase difference ΔΦ in each point r  . After phase unwrapping, the temperature difference ΔT was calculated according to

0 ) ( ) ( B TE r r T           , (1)
where

C ppm     / 0004 . 0 0101 . 0  ( 15 )
is the temperature dependant water chemical shift, γ is the gyromagnetic ratio, and B0 is the field strength.

In order to identify motion-corrupted images, the cross-correlation was calculated between the magnitude reference image and each of the magnitude images in a time series of 20 images based on the following formula

                          1 0 2 1 0 2 1 0 ) ( ) ( ) )( ( N k k N k k K N k k XY Y Y X X Y Y X X P (2)
where PXY is the cross-correlation coefficient of 2 images X and Y with k is the number of the pixel for a total of N pixels (=128x128 pixels). The calculation was performed for the complete image, not just for a part of it. When the cross-correlation coefficient was lower than 0.95 (experimentally determined threshold), the image was considered as corrupted and therefore discarded.

To determine the noise level of the temperature maps, the standard deviation of the phase in each pixel was calculated for the series of 20 phase maps (of 1 slice) in volunteers and patients. The standard deviation was only calculated for those time series where 0 or 1 image was discarded because of motion problems. A temperature standard deviation map was calculated from the resulting phase standard deviation map with equation 1. The temperature standard deviation was averaged in 3 regions of interest (ROI): the complete liver, left liver, and in the right liver. The signal-to-noise-ratio SNR was determined in the magnitude images with following formula ( 16)

53 . 1   N S SNR  . ( 3
)
The signal amplitude S was averaged in a region without vessels in the liver in one slice and the standard deviation of the noise σN was determined in a region outside the body.

MR Protocol and Post Processing for T2 * Measurement

MR experiments were performed with the standard body coil for excitation and the 4 elements phased array flexible body coil for signal detection as described above. A set of 9 gradient echo images was acquired in one breath-hold to observe T2 * decay in the liver using a segmented EPI sequence, with TR=150 ms and TE ranging from 9.2 ms to 50.8 ms. Two adjacent, transversal slices of 6.5 mm thickness with a FOV of 420 mm x 223 mm and a matrix size of 256 x 99 were acquired, yielding an in-plane resolution of 1.6 mm x 2.3 mm.

Phase encoding was performed in the anterior-posterior direction. A water-selective binomial spectral-spatial excitation pulse was applied for slice selection, as was described previously (Erreur : source de la référence non trouvée). A ROI was chosen in the liver on the image series and the signal intensity was averaged in the ROI for each TE. An exponential decay was fitted to the time evolution of signal intensity, yielding T2 * .

Results

T2 * Determination T2 * in livers with early cirrhosis was in the range of 17 -31 ms (mean ± SD = 23.8 ± 5.3 ms; n=9) and not significantly different from healthy livers (patient with benign tumor and volunteers) where the range was 23 -33 ms (mean ± SD = 27.1 ± 3.9 ms; n=5). In advanced cirrhosis and in hemochromatosis, T2 * is significantly shorter, with values as low as <10 ms.

Its precise value could not be determined with the used sequence, since TE of the used sequence is too long for such short T2 * values. As a consequence, we generally reduced TE and echo train duration for temperature mapping in patients compared to volunteers to improve temperature stability and SNR.

Stability of the Method in Volunteers

Both phase and magnitude images acquired without saturation slabs show flow artifacts in the phase direction around the big vessels. With saturation slabs, the flow artifact vanishes and the vessels appear dark in the magnitude images. Note that the gall bladder still appears bright. The MR temperature mapping shows higher precision when using the saturation slabs:

the temperature standard deviation map has lower values and is more homogeneous throughout the liver. The temperature standard deviation maps show a standard deviation of 1 -3 ˚C in most parts of the liver. Areas with standard deviation > 5˚C are restricted to regions with residual movement artifacts, like regions close to the stomach and the chest wall. As expected, more severe motion artifacts were observed when the phase encoding direction was changed to anterior-posterior, the main direction of breathing motion

The average value for the standard deviation for all volunteers was lower for EPI without SENSE, than for EPI with SENSE ( Additionally, experiments with the same sequence, the same coil and identical parameters as for human liver in vivo were performed on a heated beef liver sample to verify the accuracy of the SENSE method for temperature mapping in comparison with absolute temperature measurement with a thermocouple. The MR thermometry corresponded well with classical thermometry. The standard deviation of MR thermometry was less than 0.5°C for beef liver ex vivo.

Stability of the Method in Patients

Figure 1 shows the magnitude image, the phase image, and the color-coded map of the temperature standard deviation for 3 patients with cirrhosis, severe cirrhosis, and metastasis of colorectal cancer, respectively. The image quality in all 3 patients is sufficient to obtain phase information over the complete abdomen, despite the significantly lower signal intensity in severe cirrhotic liver than in non-cirrhotic or slightly cirrhotic livers. Residual motion artifacts can be seen in a large number of patients for liver tissue adjacent to the stomach showing up as red area in the standard deviation map (Fig 1, bottom row middle). Consequently the temperature standard deviation in the left liver was higher than in the right liver (Table 2).

The SNR in the liver was in the range of 60 -100. In livers with severe cirrhosis (n=4), the SNR in the liver was approximately 40% lower than the SNR in the spleen. SNR was in the same range for non-cirrhotic (healthy or steatose) or slightly cirrhotic livers.

There was no difference in temperature standard deviation between livers with and without steatosis in the group of the non-cirrhotic livers (Table 2). In the other group, the standard deviation of patients with advanced cirrhosis was only calculated in 2 of the 4 patients (mean of standard deviation in total liver 3.03°C and 1.65°C), since too many images were corrupted for two of these patients. The patient with cirrhosis and steatosis did not show a significantly different standard deviation compared to the mean value in this group.

The stability depends also on the position of the slice. A slice in the dome of the liver shows more artifacts than a centered slice, because of the stronger influence of the respiratory motion or even by heart motion. A slice in the base of the liver can be corrupted by stomach or bowel motion.

The images obtained with the built-in quadrature body coil showed, as expected, a decrease in SNR of 68 % (n=8) due to the lower sensitivity of this coil (Fig. 3). Nevertheless, the image quality was sufficient to obtain stable phase maps. The stability was lower compared to the measurements with the 4-element body coil in the same patients: the mean temperature standard deviation was 0.49 °C higher with the quadrature body coil (n=8). The spatial resolution is decreased since a larger FOV is needed to avoid back folding of the images of arms. In addition, the temporal resolution was lower (2 respiratory cycles) using the same gating width, since no SENSE acceleration can be performed A reduction of the gating window from 1200 ms to 600 ms (consequently causing a reduction of the temporal resolution from 1 to 2 respiratory cycles) did not improve the results for patients with short, irregular breathing cycles. Another option to reduce the gating width without a reduction in time resolution was the use of SENSE factor 2. Under these experimental conditions, the gating width was 900 ms and the stability of the method was slightly improved. The SNR decreased in accordance with the volunteer experiments.

An increase of echo time from 12 ms to 16 ms (with an increase of the echo train length from 7 to 9) in the patient group with non-cirrhotic livers (where slightly longer T2 * values were found) resulted in a decrease of the temperature standard deviation by 0.32°C (n=6), whereas a decrease of the mean SNR with echo time could not be observed in this subgroup.

Identification of motion-corrupted images

Irregular breathing caused failure of the gating method leading to 2 kinds of artifacts: 3). In 83 % of the patients with cirrhosis no or one image was corrupted, compared to 89 % of the patients without cirrhosis patients and 100 % of the volunteers.

However, in the patients with advanced cirrhosis, this number drops to 50%., In total, only 4% of temperature maps were found to be corrupted of all images acquired with SENSE factor 1.5 in patients and volunteers.

A change in echo time did not change significantly the number of corrupted images in patients with healthy livers (Table 4). However, the number increased when using the built-in body coil for signal acquisition. A reduction of the acquisition window by decreasing the time resolution increased the number of corrupted images, whereas use of SENSE factor 2 led to a reduction of the number of motion-corrupted images.

Discussion

T2 * Values

It has been previously shown that the sensitivity of the PRF method has a maximum for TE=T2 * . However, hepatic T2 * is rather short (compared to e.g. kidney or brain tissue), leading to the following consequences for temperature mapping sequences: 1) The echo train duration in the segmented EPI-sequence must be limited, since TE should not exceed T2 * . The shorter the echo train length, the more TR periods are needed to complete an image and the longer is the total acquisition time.

2) The spatial resolution is hampered since a rapid T2 * decay broadens the point spread function which determines the actual voxel size. Therefore, quantitative temperature mapping using PRF is more challenging in the liver than in other organs.

The T2 * value depends on the local field homogeneity and it can therefore differ for the same patient/volunteer from scanner to scanner or from session to session. Thus, tissue of the same type can have a wide range of T2 * values as is also shown in our measured values. We did not determine accurate T2 * calculations n a patient with liver metastasis, since in general signal intensity (and hence T2 * ) is not different from healthy tissue of healthy volunteers.

However, this is not the case for a liver with steatosis, which may lead to different T2 * . The tissue changes in patients with early cirrhosis did not result in a significant change in T2 * as compared to healthy livers. However, in livers with advanced cirrhosis T2 * dropped under 10 ms. In the case of hemachromatosis, the hepatic T2 * below 10 ms can be explained by the iron deposits resulting from this pathology. To cover a broad range of T2 * values with the same sequence (without having to measure T2 * in every patient), we reduced TE and echo train length as compared to the initial volunteer experiments. This reduction does not lead to a significant reduction in quality of MR temperature mapping in patients with non-cirrhotic livers (like in the volunteer experiments) since an increase in TE and echo train length did not significantly improve the temperature stability.

Patient Study

The stability of the method is sufficient to obtain reliable temperature information in patients with a measurement standard deviation of 3°C or less. The precision of temperature estimate is highly dependent on both the SNR and the influence of motion on acquired

images. An estimate of the influence of the SNR can be easily obtained by looking at the quality of the magnitude images. In case of an insufficient SNR, acquisition parameters have to be changed. By analyzing the correlation factor it is possible to evaluate the performance of the respiratory gating. When the percentage of corrupted images is too high, the gating parameters have to be adapted.

The motion correction method using simple respiratory gating was sufficient, and the SENSE acceleration can be very useful in patients who exhibit a short and irregular breathing cycle. The method can be easily implemented on a clinical scanner since only standard equipment is used. The post processing is not time-consuming, so that temperature difference maps can be obtained in real time using the previously described software interface ( 17 , [START_REF] Salomir | Hyperthermia by MR-guided focused ultrasound: accurate temperature control based on fast MRI and a physical model of local energy deposition and heat conduction[END_REF] ). The obtained temperature difference maps are quantitative and thus allow immediate control and adjustment of the laser or RF energy deposition offering the possibility of an automatic feedback control loop (Erreur : source de la référence non trouvée). The accuracy of the respiratory-gated PRF-method for monitoring laser ablation was shown previously in rabbit liver in vivo (Erreur : source de la référence non trouvée).

The main contribution to the residual error of the method is the motion of the liver in vivo that, apparently, cannot be prevented by the respiratory gating. In comparison, a much smaller standard deviation (approximately 0.5°C) can be achieved with this method on a static beef liver sample ex vivo.

In many patients, the temperature standard deviation was higher than 10°C in liver tissue close to the stomach. The reason for this is slight motion of that liver segment caused by digestive movements of the stomach. A possible solution for temperature monitoring in this region would be the use of medication reducing digestive motion during the procedure.

Images corrupted by irregular breathing motion could be successfully identified and discarded on the basis of cross-correlation of the magnitude images with a threshold of 0.95. The number of discarded images is a measure for the performance of the respiratory gating method. In 84% of the patients the method performed well (only 5% or less of the images were corrupted), in only 7% of the patients more than 25% of the images were corrupted. In cases of very short breathing cycles, the number of corrupted images could be lowered and the stability could be improved by the use of a smaller gating window width.

This was either achieved by using a higher SENSE factor (however, SENSE factors > 2 caused artifacts); or by keeping the SENSE factor at 1.5, leading to a decreased time resolution (2 instead of 1 breathing cycles). In our study, the first method lead to better results.

At the present status of the visualization algorithms, the corrupted images were simply discarded and no post-processing for motion artifact correction was employed. If needed, a suitable correction method can be developed to enable on-line correction of in-plane motion thus conserving the temperature information.

The images acquired with the built-in body coil showed a large decrease in SNR (due to the coil geometry and the loading factor), which lead to significantly lower temperature stability. In addition, SENSE acceleration cannot be used with this coil. However, the stability remains sufficient for temperature monitoring. The advantage is the better accessibility to the patient. With the wrap-around phased-array coil the catheter with the laser fiber or the RF-needle needs to be placed through the openings of this coil thus limiting access.

When comparing the two patient groups, the temperature stability was slightly higher in the patients without cirrhosis. In the case of the advanced cirrhotic livers, this can be explained by the lower SNR compared to healthy livers, since the SNR is affecting the phase standard deviation and therefore the stability of the method ( 19). In our study, a higher number of cirrhotic than non-cirrhotic patients had an irregular breathing cycle causing possible failure of the respiratory gating.

General anesthesia is often applied during an ablation procedure leading to a more regular and slowerbreathing, possibly resulting in an improved performance of the gating method. In case of a conscious sedation of the patient, the respiration is also slightly more regular than in the freely breathing patients of this study without any sedation. Nevertheless, the current study performed with patients and volunteers breathing freely -which can be considered as more challenging in this respect -the stability of real time temperature mapping was judged to be sufficient for guidance of ablation procedures since the temperature standard deviation is much lower than the absolute temperature difference during the procedure while maintaining a temporal resolution much higher than the total heating duration.

Conclusion

The proposed method using a respiratory gated segmented EPI sequence with SENSE has a high temperature stability, a high temporal and a sufficient spatial resolution for accurate monitoring of temperature during thermal ablation. The SENSE method is particularly suited for short respiratory cycles due to the reduced acquisition time. The results of this study indicate that quantitatively monitoring ablation procedures in the liver using real-time temperature mapping is feasible using conventional clinical MR instrumentation. Monitoring of temperature for both healthy and cirrhotic livers of all stages should be possible with a temporal resolution of a single respiratory cycle and a spatial resolution of 3-4 mm both under general anesthesia and conscious sedation. The images were acquired with the 4-channel coil and SENSE factor 1.5. An image was defined as corrupted when its cross-correlation coefficient > 0.95. The images were acquired with the built-in body coil (without SENSE), with a shortened acquisition window and a temporal resolution of 2 respiratory cycles (SENSE 1.5), with a shortened acquisition window and SENSE 2, and with TE=16ms in patients with non-cirrhotic livers (SENSE 1.5). In brackets are the number of corrupted images acquired with the 4-channel coil, TE=16 ms, and SENSE factor 1.5 in the same patients. An image was defined as corrupted when its cross-correlation coefficient > 0.95.

  image signal intensities.

1 )

 1 Breathing during the acquisition in the gating window time span lead to motion-corrupted images showing the typical ghosting and blurring in the phase encode direction (intra-images artifacts). 2) Irregular breathing depths changed the position of the image plane by through-plane motion (Fig. 2 bottom). Motion artifacts caused peaks in the range of 8 -20 °C in the time evolution of the temperature difference in one voxel. Those motion-corrupted images were automatically identified with the cross-correlation algorithm. The images with a cross-correlation coefficient < 0.95 corresponded to the images with artifact peaks in the temperature difference curve (Fig. 2, top). They were discarded from the time series of images. After the identification step the temperature difference curve displays no data at the specific time point. The number of discarded images was determined in each image time series (Table

  Due to the high temporal resolution of the method of a single breathing cycle the loss of temperature information at a single time point does not significantly hamper the quality of the monitoring of a typical ablation procedure with a duration of 15 -30 min. A fixed cross-correlation threshold value, emerically determined, appeared a simple and effective solution. Other thresholds are also possible, e.g. based on the standard deviation of the cross-correlation coefficient. The calculation of the cross-correlation is fast and easy, and thus can be implemented in a real-time software interface. It should be noted that the image intensity may change in the heated zone due to T1 changes in an ablation experiment. As a consequence, it is recommended to exclude this area in cross-correlation calculations in the case of high local change of the contrast.

Fig. 1 :

 1 Fig. 1: Transversal images in 3 liver tumor patients acquired with the respiratory-gated SENSE-EPI sequence, together with corresponding color-coded map of the temperature standard deviation of a time series of temperature maps.

Fig. 2 :

 2 Fig. 2: Time course of measured temperature difference in a single voxel for a patient with a healthy liver with metastasis (top left) together with cross-correlation coefficient for this series of images (top right). The horizontal dashed line represents the threshold value for the cross-correlation. The peak in the temperature difference curve corresponds to an image with cross-correlation coefficient < 0.95 (arrows). As can be seen on the 3 successive images of the time series (bottom), the temperature jump was caused by through-plane motion (arrow).

Fig. 3 :

 3 Fig. 3: Comparison between images (top row) and map of temperature standard deviation (bottom row) acquired with the built-in body coil (left column) and with the 4-channel coil (right column) in the same patient with a cirrhotic liver.

Table

  is expected for an acceleration factor of 1.5 and 2, respectively (Erreur : source de la référence non trouvée). The experimental SNR values reported in Table1were found to be in good agreement with theory. Temperature stability was not determined for SENSE factors > 2, since reconstruction artifacts hampered the image quality. The breathing cycle length in the healthy volunteers is sufficient to acquire 3 slices even without SENSE within one cycle. However, this is not possible with a shorter breathing cycle length. Then SENSE acceleration is recommended to acquire all 3 slices within one breathing cycle.

	1	2		% 71
				1	5 . 1		82	%	and

). The SENSE magnitude images show a lower SNR for higher SENSE factors. Theoretically a decrease to

Table 1

 1

			without	SENSE 1.5	SENSE 2
			SENSE
	temperature standard	1.38 ± 0.33	2.03 ± 0.55	1.93 ± 0.63
	deviation in	°C
	relative SNR	1	0.87 ± 0.32	0.74 ± 0.25

Table 1 : Temperature stability and SNR in volunteers (n=5).

 1 The values of the temperature standard deviation maps were averaged over the total liver, the mean value in °C is shown for the respiratory gated segmented EPI sequence without SENSE, with SENSE factor 1.5, and with SENSE factor 2. The SNR values are calculated relative to the value obtained without SENSE.

Table 2

 2

		total liver	left liver	right liver
	cirrhotic livers	2.34 ± 1.13	3.14 ± 2.93	2.07 ± 0.87
	(n=29)
	non-cirrhotic livers	2.18 ± 0.92	2.80 ± 1.38	1.97 ± 0.84
	(n=8)
	steatosis (n=4)	2.15 ± 1.26	2.18 ± 1.59	2.11 ± 1.11
	healthy (n=4)	2.21 ± 0.63	3.42 ± 0.94	1.82 ± 0.61

Table 2 :

 2 Temperature stability in patients.The values of the temperature standard deviation maps were averaged in the total liver, the left, and the right liver. The mean values for all images series with less than 2 corrupted images are shown. All values are mean ± standard deviation in °C.

Table 3

 3

	Number of corrupted	0-1	2-5	≥ 6
	images
	volunteers	5	0	0
	(n=5)
	cirrhotic livers	29	4	2
	(n=35)
	non-cirrhotic livers	8	0	1
	(n=9)

Table 3 : Number of motion-corrupted images in a time-series of 20.

 3

Table 4

 4

	Number of corrupted	0-1	2-5	≥ 6
	images
	built-in body coil	8 (10)	1 (0)	1 (0)
	(n=10)
	2 respiratory cycles	5 (4)	0 (2)	1 (0)
	(n=6)
	SENSE 2	7 (6)	1 (1)	0 (1)
	(n=8)
	TE=16ms,	6 (7)	1 (0)	0 (0)
	non-cirrhotic livers
	(n=7)

Table 4 : Number of motion-corrupted images in a time-series of 20 for different acquisition schemes.

 4

Acknowledgements

The authors thank Bénédicte Caire-Gana for her help with the volunteer experiments. This work was presented at the 11 th annual meeting of the ISMRM in Toronto

Marie Curie stipend of the European Union for C.W.