Antoine Lhuillier
email: antoine.lhuillier@enac.fr

Christophe Hurter
email: christophe.hurter@enac.fr

Christophe Jouffrais
email: jouffrais@irit.fr

Emmanuel Barbeau
email: barbeau@cerco.ups-tlse.fr

Hélène Amieva
email: helene.amieva@isped.u-bordeaux2.fr

Visual Analytics for the Interpretation of Fluency Tests during Alzheimer Evaluation

Keywords: Applied computing~Document capture Alzheimer Disease, 3C Dataset, Electronic Health Records, Information Visualization, Data cleaning

. In this activity, the patients are asked to cite the largest possible number of city names within a minute. Because the names are handwritten very quickly by a medical practitioner some cities are abbreviated or poorly written. In order to analyze such data, medical practitioners needs to digitize it first and clean the dataset. Because these tasks are intricate and error prone we propose a novel set of tools, involving interactive visualization techniques, to help medical practitioners in the digitization and data-cleaning process. This system will be tested as part of an ongoing longitudinal study involving 9500 patients.

CCS Concepts

• Human-centered computing~User centered design • Human-centered computing~Visual analytics • Human-centered computing~Visualization techniques • Social and professional topics~Personal health records • Information systems~Data cleaning •

INTRODUCTION

According to a ministerial review from 2004, approximately 860,000 people are affected by Alzheimer's disease in France. This figure is estimated to reach 1.3 million in 2020 and 2.1 million in 2040. This is why the study of Alzheimer's disease has been identified as a major challenge for society. In the 3C cohort study [START_REF] Alperovitch | Epidemiological studies on aging in France: from the PAQUID study to the Three-City study[END_REF], 9500 elderly people performed a set of tasks (lexical, cognition evaluation, sensory and motor assessments). They were longitudinally followed during a 22 year period and some of them developed Alzheimer's disease (AD). Hence, the 3C project contains Electronic Health Record (EHR) that has been used to extract relevant information [START_REF] Auriacombe | Validity of the Free and Cued Selective Reminding Test in predicting dementia The 3C Study[END_REF][START_REF] Catheline | P2a-2 Variabilité interindividuelle des performances cognitives mnésiques et leurs corrélats neuroanatomiques au cours du vieillissement: Etude sur une cohorte en population (3C)[END_REF] regarding personal health.

Our study focuses on one of the tasks performed by the patients in the 3C project: the lexical evocation task (called a fluency task). This fluency task is a part of the Isaac test set [START_REF] Isaacs | The set test: a rapid test of mental function in old people[END_REF][START_REF] Isaacs | The Set test as an aid to the detection of dementia in old people[END_REF]. In this task, the patient is asked to cite the largest possible number of city names within a minute.

The lexical evocation task is directly connected to the concept of cognitive maps introduced by Tolmann [START_REF] Tolman | Cognitive maps in rats and men[END_REF] and echoed by O'Keefe and Nadel [START_REF] O'keefe | The hippocampus as a cognitive map[END_REF]. A cognitive map can be considered as a conceptual matrix in which episodes of life are recorded to be later accessed by user mental processes (i.e. fluency test). Moreover, according to O'Keefe and Nadel, hippocampal lesions distort spatial "imaging". Since AD impacts the hippocampal region, we are strongly convinced that the evolution of the patient cognitive map will lead us to detect at an early stage the beginning of AD.

We hypothesize that being able to spatially and temporally map cited cities during the test will help neuropsychologists to analyze the evolution of the patient cognitive map. Therefore performing the digitization and cleaning of the fluency task is a great opportunity to study the effect of aging on the human brain.

The digitization process is not an easy task and will require special care. Because notes are taken by hand by a medical practitioner some cities are abbreviated or poorly handwritten (a sample is shown in figure 1). The first step toward analyzing this data is the digitization. This process involves first to transform the handwriting information into a list of cities with temporal timestamps. Secondly, one needs to clean the dataset from digitization errors as well as remove ambiguities between the misspelled cities or any other kind of confusion of data. Due to the complexity of these two tasks, no automatic method can be used. Furthermore some errors in the list of cities can also contain relevant information regarding the cognitive state of the patient Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. VAHC ' (e.g. some of them are called intrusion and will be detailed in the section 5 of this paper).

We used a User Centered Design (UCD) [START_REF] Schuler | Participatory design: Principles and practices[END_REF] process and developed a set of tools (like an auto-completion algorithm, a confidence-based algorithm to support the data cleaning process and finally visualization tools to help knowledge extraction) to help medical practitioners during the digitization and cleaning process.

Our contribution relies on our interactive visualization system to help practitioners clean the patient records, trace modifications and remove ambiguities between digitization error and actual insights.

In the following section we present relevant the related work. Then we list the design requirements to fulfill the digitization and cleaning tasks. Next, we describe our software features and justify our implementation choices. Finally, we outline the strength of our design with specific cleaning scenarios.

RELATED WORK

Data cleansing, also called data cleaning, or data scrubbing is the process of correcting data by removing errors. The quality of the resulting cleansing is highly dependent of the intervention by a human domain expert to perform accurate cleaning [START_REF] Damerau | A technique for computer detection and correction of spelling errors[END_REF]. This is also the case of the dataset we will have to clean where only an expert can perform the cleaning process.

Many tools also focus on the data cleaning process, such as TimeCleanser [START_REF] Gschwandtner | TimeCleanser: A visual analytics approach for data cleansing of time-oriented data[END_REF] which is specialized in time oriented data. Our data set is also time oriented since city citations are time ordered, but the majority of the cleaning process will operate on the city names and not on the temporality of the city citations. In our case, the cleaning process will operated mostly on the geographical aspect of the dataset.

Different taxonomy of the data quality problems exist [START_REF] Barateiro | A Survey of Data Quality Tools[END_REF][START_REF] Bose | Wanna improve process mining results[END_REF][START_REF] Kim | A taxonomy of dirty data[END_REF][START_REF] Müller | Problems, methods, and challenges in comprehensive data cleansing[END_REF][START_REF] Oliveira | A Formal Definition of Data Quality Problems[END_REF][START_REF] Rahm | Data cleaning: Problems and current approaches[END_REF]. However, none of them propose a solution in which inconsistent spatial data can be automatically cleaned.

Among the available data cleaning tool, we note that AJAX [START_REF] Galhardas | AJAX: an extensible data cleaning tool[END_REF] provides a valuable framework including expressive and declarative language specifications to perform the data clean process. For instance, it supports mapping, matching, clustering and merging transformation. However in our study, very few errors are generic and only the individual investigation of the records can help to determine if they contain errors or ambiguities.

We investigated text recognition algorithms like A2iA [START_REF] Menasri | The A2iA French handwriting recognition system at the Rimes-ICDAR2011 competition[END_REF], but the handwriting process with time restriction produced too many abbreviations and poor quality trails which hinder and prohibit the usage of automatic text recognition.

Bensalem et al. [START_REF] Bensalem | Toponym disambiguation by arborescent relationships[END_REF] and Buscaldi et al. [START_REF] Buscaldi | A conceptual density-based approach for the disambiguation of toponyms[END_REF] in their toponym disambiguation bases their analysis on the geographical as well as hierarchical relations (e.g. Paris is the capital of France and France is a country of Europe). In their work, the hierarchical relations is based upon the WordNet ontology [START_REF] Miller | WordNet: a lexical database for English[END_REF]. However, the WordNet ontology was not precise enough to help us disambiguate toponyms for small villages of France.

DEFINITIONS

In this section we define important medical and technical terms.

Fluency Test: This is a task part of the Isaac test set [START_REF] Isaacs | The set test: a rapid test of mental function in old people[END_REF][START_REF] Isaacs | The Set test as an aid to the detection of dementia in old people[END_REF]. In the fluency test, patients are asked to cite the largest possible number of city names within a minute. As the subject lists city names, the practitioner writes the cited cities and adds a marker at 15, 30 and finally 60 second intervals (the end of the test).

Citation: We make a distinction between two types of citations: Verbal and Digitized citations. Verbal citation is cited by the subject and then laid down on paper. Digitized citation is the verbal citation that has been digitized.

Intrusion:

A word that is definitely not a city (e.g. Banana). This is one of the errors that can be present in the verbal and/or digitized citation. More details regarding errors and ambiguities are provided in section 5.

Paraphasia: A speech defect characterized by incoherence in the arrangement of a syllable or word. There are different types of paraphrases depending on the type of incoherence. (e.g. semantic, verbal or phonemic). For example saying "tiger" instead of "lion" is a semantic paraphasia.

Patient:

The subject who performed the fluency test. The patient can be diagnosed as having dementia or not as a result of medical testing process which are outside of the scope of this paper.

Medical Practitioner:

The person who supervised the fluency tests and annotated the cited cities on paper. He is a medical expert.

Digitizer:

The person who will take the paper based record of the fluency test and enter the list of city citations in the software. This software contains many features to help the digitizer during the digitization process (these features will be explained in section 7.1).

Cleaner:

The user who will employ our tools to perform data cleaning. The cleaner can also be a practitioner but he or she has specific skills regarding patient cognitive processes. He must be an expert to determine whether the investigated record contains errors or ambiguities and finally perform possible corrections.

Inspector:

The user in charge of checking the correctness of the cleaned data. The inspector is involved in our process after the cleaner. He must be a medical expert regarding patient cognitive processes (e.g. a neuropsychologist).

A USER CENTERED DESIGN PROCESS

To accelerate the development process of our digitization and cleaning tools, we applied a user centered design process (UCD) [START_REF] Schuler | Participatory design: Principles and practices[END_REF]. In this design process, we involved users as much as possible in the tool developments.

Different kinds of users were involved in this project: the practitioners who ran the tests, the patients who took the tests, the digitizer who will carry out the digitization, the cleaner who cleans the data, the inspector who validates the cleaned data and finally the neuropsychologists who analyzes the results. All of these categories of users were involved in the design process during the development of the tools. We recorded interviews with practitioners and digitizers and we conducted numerous call conferences with one specific inspector and one neuropsychologist.

In total we spent one day with the practitioners located in Bordeaux, and we conducted many discussions with three practitioners, two cleaners, and one inspector. We spent up to twenty hours of discussion time on the phone to assess our tools. We conducted one brainstorming with two HCI experts, one cognitive science expert also author of this paper, a practitioner and a cleaner. We conducted one design walkthrough (human computer interface validation) with one cleaner. Finally we spent three hours installing and detailing our tools for one cleaner.

These sets of interviews and brainstormings helped us to analyze errors and to refine the design requirements of our solution.

ERRORS AND UNCERTAINTIES

Exploring, building statistics on a large cohort of patients requires taking into account errors during the digitization and cleaning process. Therefore we need to quantify, analyze and justify each error that may occur during the processes from the digitization to the analysis phase. This led us to define ways to compute a confidence value for a digitized cited city names with regards to possible errors.

Types of Error

Thanks to the UCD design process and the numerous discussions with the practitioners, we have identified three different types of error detailed in table 1 Digitization errors -Wrong city (copy error) -Unknown city (city not in our database)

In addition to correcting the digitization and uninterpretable error types, the data cleaning step can also be used to identify patient specific errors. For example, in our particular case, some digitization errors (such as a wrong city) can in fact be a patient error (for example, Toulouse and Toujouse are two existing cities but can also be a verbal paraphasia error).

Confidence Values

The confidence value can be computed using multiple criteria. Thus, in our study we have identified three types of confidence: spelling, phonetic and Euclidian based distance.

Spelling-based

The first confidence value is spelling. Multiple cities have the same name, for example there are more than 10 different cities whose name start by "Chalons" and are orally referred as "Chalons" without the rest of their proper name. There is a large possibility of typing errors (e.g. misspelling). Thus, it is probable that the digitizer made a mistake. From these two errors, it is necessary to remove any ambiguity. Our solution is to define a string-distancebased confidence. This confidence is based upon the Damereau-Levenstein [START_REF] Damerau | A technique for computer detection and correction of spelling errors[END_REF][START_REF] Levenshtein | Binary codes capable of correcting deletions, insertions, and reversals[END_REF] distance and allows us to get the list of city with similar spellings. We define the confidence as such:

𝐶𝑎𝑟𝑑({𝑐𝑖𝑡𝑦 | 𝑑(𝑐𝑖𝑡𝑒𝑑𝐶𝑖𝑡𝑦, 𝑐𝑖𝑡𝑦) < 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑𝑉𝑎𝑙𝑢𝑒})
With the threshold being the minimum allowed string distance between the cited city and another.

Phonetic-based

Relatively similar to the spelling-based confidence, the phoneticbased confidence uses a phonetic comparison between two words. We choose to implement the metaphone algorithm [START_REF] Philips | Hanging on the metaphone[END_REF][START_REF] Philips | The double metaphone search algorithm[END_REF] suitable for the French language. In this algorithm the listed city names are translated into a key. Thus, we can find phonetically similar cities by comparing their keys. We define our confidence as the string distance between two city keys.

𝐶𝑎𝑟𝑑({𝐶𝑖𝑡𝑦𝐾𝑒𝑦 |𝑑(𝑐𝑖𝑡𝑒𝑑𝐶𝑖𝑡𝑦𝐾𝑒𝑦, 𝑐𝑖𝑡𝑦𝐾𝑒𝑦) < 𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑})

With the threshold being the minimum allowed phonetic distance

Euclidian-distance-based

From our experience, only lexically based confidences may not be sufficient to detect potential errors. Thus, we have defined a third confidence level based on the Euclidian distance of the city from the preceding and the following one. During the task of verbal fluency, people tend to list cities near to each one's following routes itinerary as theorized in vista space concepts [START_REF] Meilinger | The Network of Reference Frames Theory: A Synthesis of Graphs and Cognitive Maps[END_REF] and revealed during our interviews with practitioners. Thus, if a cited city is far from the other, it may highlight a potential digitization error.

Let Center be the geographical position between the B, preceding city and F, the following one of our cited city and G, the geographical position of this city. We define our Euclidian based confidence as: 𝑑(𝐺, 𝐶𝑒𝑛𝑡𝑒𝑟)

‖𝐵𝐹 ⃗⃗⃗⃗⃗ ‖

Normalizing our Euclidian based confidence, allows us to spot a divergent city from the preceding and following ones.

DESIGN REQUIREMENTS

This section presents the design requirements to achieve the digitization and the cleaning tasks. First, we analyze the digitization requirements and then the cleaning one's. Requirements associated with the digitization process are abbreviated "Rd" and then those with the cleaning process "Rc". All the requirements associated with the two tasks were collected during the UCD process

Digitization Process Requirements

During the digitization process, the digitizer needs to retrieve the list of digitized cited cities.

Rd1: Multiple solutions were available to us such as; character recognition, verbal recognition or typing. Due to the poor writing quality of the tests (figure 1), we were not able to use the character recognition. Concerning verbal recognition, as the tests are in French, we needed a French-based grammar recognition. However, we were unable to find an efficient and open source French-based grammar. Thus, finally we choose to digitize the fluency test by manual typing.

Rd2: Manual typing does not solve by itself the need to digitize a correct dataset. Some cities might have the same name (e.g. London, UK and London, CA). To avoid such indetermination, the user must be able to digitize a city to a unique name.

Rd3: The digitizer must specify the separators between cities listed during a period of 0 and 15 seconds, 15 and 30 seconds and 30 and 60 seconds.

Rd4: The digitizer must be able to specify an intrusion.

Cleaning Process Requirements

Rc1: Each digitized and cleaned fluency test must be validated by an inspector. To enhance his validation work, we want him to be able to look through any modification that the cleaner might have made and to validate or reject each test. Thus, we need to provide him with the list of each modification as well as allowing him to display the original digitized dataset.

following requirements concern the cleaner tasks.

Rc2: The cleaner must be able to correct a digitized city and specify the error type. Thus, he must have interaction means to achieve his task. In the continuity of our digitization process, the cleaner must be able to solve any city-name indetermination problem (i.e. the system must allow the addressing of a unique city name).

Rc3: However, allowing him to fill-in a unique city name, means using a database of existing cities. If a city is missing from such a database, the cleaner need to be able to one directly.

Rc4: Finally, as some errors are not known in advance, the system must give the cleaner the opportunity to find new errors. Thus, he must be able to explore the dataset in order to study prospective analysis methods.

Fluency Test Dataset

The fluency test dataset contains the recording of the cities listed by a patient during a test (e.g. "Paris, Bordeaux, Toulouse, Lyon, etc..."). This list of cited cities is separated into three sub-lists which consist of the cited cities during three different periods (see table 2). Tests are linked through the patient id. The cited cities between 0 and 15 seconds

City30

The cited cities between 15 and 30 seconds

City60

The cited cities between 30 and 60 seconds

The cited city field includes the cited city name, the real name and the geographical position (latitude and longitude).

TOOLS

This section details the basic features of the two tools we developed in order to fulfill the previously identifier requirements; the digitization and data cleaning tools.

Digitization Tool

The digitization tool has two panels. The main panel shows the list of the fluency test records with values (figure 2 -top). The digitizer can import and export the digitized data at any time via two buttons.

To add test results, the digitizer uses a form and manually enters the names [Rd1] and fills in the data of the fluency test (see table 2). To allow the digitizer to bind a city name with a unique city, we have implemented an auto-completion algorithm based on a city database [Rd2]. The city database is composed of the major international cities [START_REF]GeoNames world cities over 15000 citizens database[END_REF] and all French towns [START_REF]GeoNames French cities database[END_REF]. When a town is missing, the digitizer can still override the auto-completion. In that case, the digitizer is warned with an orange feedback. Moreover, he can specify an intrusion with a right click on the text box [Rd4].

While entering the listed cities, the digitizer can see the geographical polyline formed by the fluency test (at the bottomright of figure 2). This tapered polyline shows the location of each city (known in the database) and the direction of the polyline [START_REF] Holten | An extended evaluation of the readability of tapered, animated, and textured directed-edge representations in node-link graphs[END_REF]. With this visualization, he is able to see whether a city seems erroneous or not (i.e. a city too far from the previously listed ones). Finally, by typing an existing test ID, the digitizer is able to load an existing test and modify it using the top-right box.

Cleaning Tool

Our cleaning tool (figure 3) is composed of three main panels; the histoSelector panel, the citation panel and the detail panel. In the following subsections, we detail each component.

The HistoSelector Component

To filter and explore the dataset during the cleaning process [Rc4], we display histograms showing the distribution of values of each dataset attribute (figure 4). The cleaner can select the dataset attribute to be displayed by right clicking on the histogram name.

To find new insights or errors in the dataset, we have chosen a selection paradigm based on a brushing interaction [START_REF] Li | Empirical comparison of dynamic query sliders and brushing histograms[END_REF]. Users draw a bounding box (a one dimension brushing technique) and can modified it by sliding the bounds of the selection. Multiple boxes can be specified, and they can be removed with a brush stroke with the right button of the mouse pressed (i.e. the erase mode). We maintained the classical reading pattern to right) in such a way that the selections or modifications of one histograms directly modify its neighbor's values. Nesting multiple histogram allows the user to refine a query depending on multiple dataset's attributes.

Thus, the fluency tests selected through the histograms are displayed in the citation panel.

Citation Panel

The citation panel is the representation of each fluency test in our cleaning tool (figure 5) [Rc1].

One row corresponds to one fluency test. The row is composed of coloured squares which correspond to each cited city with its confidence value. Two parameters are displayed in thee squares.

First, the saturation of each square is bound to the state of validation of the city. If the city has been validated then it has a low saturation value. In the other hand, if it is not validated, the saturation is at its full value. As saturation is a dissociative visual parameter [START_REF] Bertin | Graphics and graphic information processing[END_REF], encoding the validation state of a city through saturation allows the visual emergence of the not validated cities.

Secondly, the color of each square is bound to the confidence value of the city. The associativity of the color improves pre-attentive selection [START_REF] Bertin | Graphics and graphic information processing[END_REF] allowing users to instantly visualize a group of low confidence cities that need to be reviewed. Our color range is defined by five levels of confidence with a color generator [31]. The colors are green for confidence values over 80%, then lightgreen (60-80%), yellow (40-60%), orange (20-40%), and red for confidence values lower than 20%.

Finally, clicking on a colored square opens up the detail panel.

Detail Panel

The detail panel allows the user to correct a potential error in a digitized city. This panel is composed of four main parts.

The first part, at the top of the form, displays the values associated with the city (PatientId, BirthDate, TestDate, Confidence value and the actual digitized cited city). In this first part, the user is able to correct the city name (via the Correct City textbox) and fill-in the error type [START_REF] Auriacombe | Validity of the Free and Cued Selective Reminding Test in predicting dementia The 3C Study[END_REF]. Each textboxes has personalized auto-completion algorithm. The cleaner is also able to add a city to the database via the "City not in DB" button [Rc3]. The second part displays the list of cities lexically similar to the cited one.

The third part shows the cited cities preceding and following the selected city and displays the distance between them. This geographical distance is enhanced by displaying the polyline formed by the fluency test associated with the selected city centered on the selected one. With this visualization, the user is able to geographically place and check the location of the city and to compare it with the following and preceding one as well as with the whole fluency test.

The last part of the detail panel (at the bottom) is an image display of the original paper-based test. This allows the cleaner to check if the city was really written or not, especially in the case of a city with a low confidence value but in which the digitized city was indeed cited by the patient (refer to figure 7 for an example).

Once the correction of the city has been performed, the user can validate or cancel his changes.

SCENARIO

This section describes four scenarios of use. The first two describe a true-positive and a false-positive detection. The third one shows how the cleaner can explore the dataset and find unrevealed erroneous data. Finally the last scenario illustrates the validation process performed by one inspector. The typical meta-scenario is as follows:

While a patient 'V' executes the task of verbal, a practitioner 'W' writes down each cited city on a paper. At 15", 30" and 60" the practitioner writes down a slash to timestamp the cited cities. the test, a digitizer 'X' manually types each cited city and marks slashes with our digitization tool as explained in 7.1.

Once the data is loaded into our numeric database, a cleaner 'Y' uses our cleaning tool to correct the database. The cleaning tool help the cleaner by showing the most error-prone cited cities according to the selected type of confidence. The cleaner then has to check whether the detection of an erroneous value by the system was justified (scenario 8.1) or not (scenario 8.2).

Finally, an inspector 'Z' checks each correction made by the cleaner in order to validate or invalidate the changes made to the database. The tracking and validation of these modifications are detailed in 8.4 track modifications use case.

True-Positive Scenario

The visualization shown in figure 3 displays the digitized part of our dataset. The cleaner 'Y' wants to validate each digitized fluency test. To do so, 'Y' follows a procedure composed of three steps.

He initially decides to correct the tests with a low spelling-based confidence value. To do so, the user selects the lower values in the confidence histogram by brushing them (cursor on figure 3). By selecting those values, only the tests with a low confidence value are displayed in the citation panel. The selected fluency tests are ordered by decreasing level of confidence in the citation panel.

For the second step, 'Y' validates the first row of fluency tests. He starts by clicking on the first red rectangle in the fluency test with the lowest confidence (first row of the Citation Panel). This action opens the detail panel to correct this test citation (figure 6).

In the detail panel, the cleaner sees that the digitized city name is Valence. This city is located near Lyon in France. However, the user sees that the preceding city is Alicante and the following one is Sevilla, two Spanish cities. The distance between those cities is thus too large. Moreover, in the similar cities (cities at a small distance from the original one), he sees a Spanish city named Valencia. Thanks to these two insights, the cleaner can infer that the real cited city is not Valence in France but Valencia in Spain. 'Y' enters the corrected city name in the "Corrected City" textbox. Then, specifies the type of error, in this case a digitization error. This example illustrates how the cleaner can correct the tests that are likely to be more erroneous according to our spelling-based confidence algorithm. Moreover, it shows how one can help the cleaner to solve a decision problem and gain more insights to our database by the types of errors. By selecting the right criteria, he able to display the tests to be cleaned, thus allowing him to focus on those tests. Then, the cleaner selects the first red square showing a very low confidence value on a citation.

False Positive Scenario

In the detail panel, he sees the information concerning the citation (shown in figure 7). The digitized citation shows the city of Algiers in Algeria. The previous cited city is Pau and the following is Bougue. Thus, the Euclidian distance is relatively high.

To confirm the inaccuracy of the digitized citation, the cleaner takes a look at the original citation at the bottom of the detail panel in figure 7. However, 'Y' sees that the city that was written down on paper during the test is Algiers. He concludes the low confidence level was a false-positive one. Finally, he validates the correctness of the digital citation by clicking the "validation" button.

Since the confidence based criteria cannot always be true, this usecase highlights the way a cleaner can detect and resolve a falsepositive result thanks to the confidence algorithm.

Detect Outliner Subsets Scenario

In this use-case the cleaner 'Y' wants to detect a subset of outliners in the data to be cleaned. In this particular use-case, he wants to investigate the relationship between the length of the fluency tests and the year of the test. This use-case illustrates how the cleaner can detect outliner subsets through exploration of data properties. Exploring and interacting with the dataset helped him find unpredicted errors.

Track Modifications Scenario

We detail a scenario where an inspector 'Z' wants to validate corrections made by the cleaner 'Y' during the cleaning process.

Once the cleaner has finished the cleaning process, the inspector can validate or invalidate these modifications. To achieve this objective, he starts binding the first histoSelector to the modified attribute and the second to the validated attributes. Then by selecting the modified but invalidated fluency tests (via the two configured histograms) 'Z' displays the list of modifications requiring validation in the citation panel.

Within these tests, the inspector can explore and click on each ones. He chooses to click on the first test and check the digitized cited city. Thus, 'Z' opens the detail panel. In the detail panel he sees that the corrected city name is Damascus. By clicking on the button next to the versioning number (left arrow "<" in figure 8), he sees that the initial value of the digitized cited city was a city unknown in the database named "Damas" (the French name for Damascus).

Via the original test view in the bottom of the detail panel, "Z" checks that Damascus is indeed written in the original test. However the expert does not read "Damas" but "Douai" a French city near the preceding and following cities. Thus, he corrects the cited city a second time to the known city of Douai. Finally, he corrects and validates his change with the "Validate" button.

Through this use-case, we see how the validation of the inspector can spot errors made during the cleaning process and double check the corrections. This enables the correctness of the clean dataset to be improved.

CONCLUSION & FUTURE WORK

The study of Alzheimer's disease is a unique opportunity to work on an unexploited handwritten dataset. In the digitization and cleaning process the main difficulty is to reduce ambiguities between errors induced by our own cleaning/digitization process and errors made by the patient during the verbal fluency test. Processing and identifying these errors cannot be achieved with an automatic algorithm due to its variability. Thus it must be carried out by users. solve this problem we developed two tools that help users during the digitization as well as the cleaning steps. As an example, these tools have helped the people in charge of the digitization process by allowing them to directly input known city names into an existing database. In the cleaning process, our tool helps the user to solve the ambiguity problems inherent to city names (having multiple cities with the same name) as well as solving problems from our digitization process (missing city names).

Our work can be further improved and extended in many aspects. For instance, other confidence tests could be investigated: the homonic test or the toponym test are good candidate to extend the available tools to detect issues in our database. We believe that the tools described in this paper can be useful to provide cleaner data and facilitate the study of Alzheimer disease progression.

15 ,

 15 October 25, 2015, Chicago, IL, USA. Copyright 2015 ACM 978-1-4503-3671-0/15/10 …$15.00. DOI: http://dx.doi.org/10.1145/12345.67890

Figure 1 .

 1 Figure 1. Sample of a fluency test result. The slashes represents timestamp separators (15, 30 & 60 seconds)

  The timestamp of the fluency test (each 15, 30 & 60 seconds) is displayed in the interface with slash buttons allowing the digitizer to put the three slashes initially in the paper-based test [Rd3].

Figure 3 .

 3 Figure 3. The cleaning tool. On the left the HistoSelector panel, and on the right the Citation Panel. The cleaner can change the confidence with combo box above the citation panel.

Figure 2 .

 2 Figure 2. The digitization tool. On top is the list of tests, below is form to add a fluency test. Each city is typed in comboboxes. Unknown cities are orange, and intrusions words red.

Figure 4 .

 4 Figure 4. Histogram Panel. By drawing boxes on the histograms, a cleaner has selected all patients who revealed only a low number of towns during the 2002-2004 tests. A selection in one histogram updates all histograms to its right.

Figure 5 .

 5 Figure 5. Citation panel. Displays the confidence of each digitized city encoded in five levels of color (red to green). A validated city is de-saturated (top of the figure).

  In a similar manner to the previous use-case, the cleaner 'Y' can use distance-based confidence to enhance his task of data cleaning. In this use-case, he wants to validate each digitized fluency test. 'Y' follows the same steps as in the spelling distance scenario.

Figure 7 .

 7 Figure 7. Detail panel showing a false positive case. Although the cited city (Algiers) is far from its neighbors (Pau and Bougue), we see on the picture of the original test that the patient did cite it (in the orange box).

Figure 6 .

 6 Figure 6. Detail panel. On the top are the test information, then the phonetically similar cities, and the geographical position within the fluency test. Finally, we display the original fluency test.

  'Y' initially decides to bind a first histogram to the fluency test length (the number of cited cities). By clicking on the tag name of the histoSelector component, he is able to change its binding. Then, he repeats the operation on the following histoSelector component (directly on the right of the previous one) binding it to the year of the test. With this configuration the cleaner is able to visualize the distribution of the test years depending on the selected fluency test lengths (figure4). Then he selects the fluency test with a low number of listed cities by brushing the histoSelector component. During the brushing sequences, the cleaner can directly see the repartition of test dates. 'Y' discovers that the majority of tests with a low number of listed cities were performed in 2003-2004. Moreover, with the citation panel, the cleaner finds that all these low numbered tests are missing the 30 to 60 second parts. Thus, the cleaner can conclude the presence of an error in the test protocol during the tests in the period from 2003 to 2004.

Figure 8 .

 8 Figure 8. Validation of a cleaned test. In this case, the cleaned city is Damascus but the original cited city was Douai. The expert can invalidate the modification and make a new correction to the data.

Table 1 . Error types identified in our study Type Sub-type (with details)

 1 .

	Patient errors	-Semantic paraphasia (lexical substitution)
		-Verbal paraphasia (phonetic substitution)
		-Intrusion
		-Repetition
		-Undetermined others…
	Uninterpretable
	errors

-Unreadable city name (on paper) -Incomplete test (e.g. a whole part of the test is missing)

Table 2 . Fluency test field names and semantics Field name details testId

 2

The unique id of the test

Id

The unique id of a patient

BirthDate

The birthdate of a patient

TestDate

The date of the test City15


Junshi Xia

Lionel Bombrun

Tülay Adali

Yannick Berthoumieu

Christian Germain

CLASSIFICATION OF HYPERSPECTRAL DATA WITH ENSEMBLE OF SUBSPACE ICA AND EDGE-PRESERVING FILTERING

Keywords: Classification, hyperspectral data, independent component analysis, edge preserving filter

Conventional feature extraction methods cannot fully exploit both the spectral and spatial information of hyperspectral imagery. In this paper, we propose an ensemble method of subspace independent component analysis (ICA) and edgepreserving filtering (EPF) for the classification of hyperspectral data to achieve this task. First, several subsets are randomly selected from the original feature space. Second, ICA is used to extract spectral independent components followed by a recent and effective EPF method, rolling guidance filter (RGF), to produce spatial features. The spatial features are treated as the input of a random forest (RF) classifier. Finally, the classification results from each subset are integrated together to produce the final map. Experimental results on real hyperspectral data demonstrate the effectiveness of the proposed method. A sensitivity analysis of this new classifier is also performed.

INTRODUCTION

Due to its ability to provide detailed spectral information of different materials present in a given scene, hyperspectral data has increasingly become preferred for remote sensing applications [START_REF] Chang | Hyperspectral Imaging: Techniques for Spectral Detection and Classification[END_REF][START_REF] Chang | Hyperspectral Data Exploitation: Theory and Applications[END_REF]. However, classification of hyperspectral data is a challenging problem largely due to the curse of dimensionality (Hughes phenomenon) [START_REF] Hughes | On the mean accuracy of statistical pattern recognizers[END_REF]. Random Forest (RF) is one of the most widely used pixel-wise classifier for hyperspectral data, which is insensitive to the dimensionality of data and also has provided good performance in terms of classification accuracy. In addition, the RF classifier offers two main advantages, i.e. very low computational complexity and having only few parameters to tune [START_REF] Breiman | Random forests[END_REF][START_REF] Chan | Evaluation of Random Forest and AdaBoost tree-based ensemble classification and spectral band selection for ecotope mapping using airborne hyperspectral imagery[END_REF]. Another common strategy to overcome the Hughes phenomenon is to reduce the dimensionality via feature extraction or feature selection techniques [START_REF] Guyon | Feature Extraction: Foundations and Applications[END_REF]. In recent years, independent component analysis (ICA) has received attention for feature extraction of hyperspectral remote sensing images [START_REF] Comon | Handbook of Blind Source Separation: Independent Component Analysis and Applications[END_REF][START_REF] Wang | Independent component analysis-based dimensionality reduction with applications in hyperspectral image analysis[END_REF][START_REF] Dalla Mura | Classification of hyperspectral images by using extended morphological attribute profiles and independent component analysis[END_REF][START_REF] Falco | A study on the effectiveness of different independent component analysis algorithms for hyperspectral image classification[END_REF]. In particular, ICA extracts source components that generate the mixed signal measured by the sensor and the independent components refer to the different classes presented in the scene [START_REF] Comon | Handbook of Blind Source Separation: Independent Component Analysis and Applications[END_REF][START_REF] Falco | A study on the effectiveness of different independent component analysis algorithms for hyperspectral image classification[END_REF]. Classically, when we apply ICA to hyperspectral data, principal component analysis (PCA) is performed first, and then, ICA is performed on a percentage of top, most important principal components, while the remaining components are discarded [START_REF] Falco | A study on the effectiveness of different independent component analysis algorithms for hyperspectral image classification[END_REF]. Although this method is effective for extracting features of hyperspectral data, it might lead to a loss of useful information in the discarded components. In order to alleviate this problem, we propose a random subspace ensemble approach, in which several subsets are randomly selected from the original spectral bands and then ICA is applied on each subset.

For high spatial resolution hyperspectral data, the classification performance is generally very low when only the spectral information is considered [START_REF] Fauvel | Spectral and spatial classification of hyperspectral data using svms and morphological profiles[END_REF][START_REF] Fauvel | Advances in spectral-spatial classification of hyperspectral images[END_REF]. In order to improve classification performance, spatial contextual information (relationship between neighboring pixels) should be included in the analysis [START_REF] Fauvel | Spectral and spatial classification of hyperspectral data using svms and morphological profiles[END_REF][START_REF] Fauvel | Advances in spectral-spatial classification of hyperspectral images[END_REF]. Previous approaches for including such information include mathematical morphology (MM) and Markov random fields (MRF). Li et al. [START_REF] Li | Generalized composite kernel framework for hyperspectral image classification[END_REF] developed a generalized composite kernel (GCK) framework by combining spectral information and the most effective MM features: extend multi-attribute profiles (EMAPs). Recently, Xia et al. proposed two powerful classifiers, namely RoF-MRF [START_REF] Xia | Spectralspatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields[END_REF] and RS-EMAPs [START_REF] Xia | Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles[END_REF]. The former combines the class posterior probabilities produced by Rotation Forest (RoF) and the spatial information represented by MRF-based multilevel logistic (MLL) prior [START_REF] Xia | Spectralspatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields[END_REF]. The latter uses random subspace (RS) ensembles to classify EMAPs features [START_REF] Xia | Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles[END_REF].

Recently, edge-preserving filtering (EPF) has been successfully applied in many fields such as denoising [START_REF] He | Guided image filtering[END_REF]. It is used to remove noise, weak edges, and small details whereas the overall structure of the image is preserved. In this paper, the spatial contextual information is exploited by means of an EPF. Hence, it helps reduce the intraclass uncertainty, leading to high classification performance. Among the recent effective EPF solutions, we promote the rolling guidance filter (RGF) for extraction of spatial features from hyperspectral data.

The main contribution of the paper is to propose a spectral-spatial classification method based on ensemble of subspace ICA and EPF. This framework offers many advantages.

1. ICA extracts discriminant features of each class in the image;

2. RGF provides complementary spatial information of the structures present in the scene;

3. Ensemble scheme can enhance the classification performance when compared to the use of a single technique.

The rest of the paper is organized as follows. In Section 2, ICA and EPF are introduced. The proposed method is described in Section 3 and the experimental results are shown in Section 4. The conclusion is given in Section 5.

BACKGROUND

Next, we briefly describe the ICA and RGF that play a critical role in the performance of the proposed method.

Independent component analysis (ICA)

ICA is an attractive solution to the blind source separation (BSS) problem, which decomposes an observed set of mixtures into a set of statistically independent components [START_REF] Comon | Handbook of Blind Source Separation: Independent Component Analysis and Applications[END_REF]. We consider the observed mixture x = [x 1 , .. T are estimated by forming y = Wx, where W is the estimated demixing matrix that makes use some form of diversity. The most widely used type of diversity is non-Gaussianity, i.e. higher-order-statistics (HOS). Two popular ICA approaches used in the remote sensing community, FastICA [START_REF] Hyvarinen | Fast and robust fixed-point algorithms for independent component analysis[END_REF] uses a fixed nonlinearity to maximize non-Gaussianity and Joint Approximate Diagonalization of Eigenmatrices (JADE) [START_REF] Cardoso | Blind beamforming for non-gaussian signals[END_REF] extracts the demixing matrix W by joint a diagonalization of the fourth-order cumulant matrices.

A more attractive approach to making use of HOS is to use a dynamic nonlinearity that is matched to each of the estimated source densities, y d , for d = 1, . . . , D separately. Entropy bound minimization (EBM) utilizes an efficient entropy estimator to approximate the density of the sources by maximizing the entropy bound and using a finite number of prespecified measuring functions [START_REF] Li | Independent component analysis by entropy bound minimization[END_REF]. It provides robust performance using the four measuring functions proposed in [START_REF] Li | Independent component analysis by entropy bound minimization[END_REF] but also allows for selection of nonlinearities using prior information about the sources. Another important type of diversity, which is of particular interest here, is sample depen-dence. The adjacent pixels in an image are highly correlated and making use of this additional statistical property when achieving ICA promises to further improve the performance of ICA [START_REF] Adali | Diversity in independent component and vector analyses: Identifiability, algorithms, and applications in medical imaging[END_REF]. Entropy rate bound minimization (ERBM) [START_REF] Li | Blind spatiotemporal separation of second and/or higher-order correlated sources by entropy rate minimization[END_REF] effectively combines the dynamic nonlinearity selection of EBM with an invertible filter model and hence achieves better performance in terms of minimization of the entropy rate

I r (y 1 ; ...; y D ) = D i=1 H r (y i ) -log |det(W)| -H r (x) (1)
where, H r (y i ) is the entropy rate of the process y i and H r (x) is a constant with respect to W.

Rolling Guidance Filter (RGF)

Rolling guidance filter, which has been proposed for nonlinear image decomposition based on a modification of the bilateral filter, effectively removes noise and small details while preserving large-scale structures automatically, which the standard bilateral filter often fails to do [START_REF] Zhang | Rolling guidance filter[END_REF].

Rolling guidance filter is composed of two steps, i.e. small structure removal and edge recovery. Small structure removal can be done by Gaussian filtering. Then, a joint bilateral filter is used to recover the edge iteratively. If we let J 1 initially set as the output of the Gaussian filtering stage and then J t+1 is the output of the tth iteration of joint bilateral filtering with the input image I and J t , and is written as

J t+1 (i) = 1 Q i j∈Np exp - i -j 2 2σ 2 s - J t (i) -J t (j) 2 2σ 2 r I(j) (2)
where,

Q i = j∈Np exp -i-j 2 2σ 2 s - J t (i)-J t (j) 2 2σ 2 r . i
and j are the coordinates of pixels in the image, N p denotes the set of neighbor pixels of i, σ s and σ r control the spatial and range weights respectively. In order to make RGF more effective, two main steps can be combined into one by starting rolling guidance simply from a constant-value (C) image. In this case, the initial step can be saved by starting rolling guidance from J 0 , where ∀i, J 0 (i) = C. Algorithm 1 depicts the final rolling guidance filter implementation.

Algorithm 1 Rolling Guidance Filter

Input: I, σ s , σ r and n iter 1: Initialize J 0 as a constant image 2: For i = 1 : n iter 3: J t ← JointBilateral(I, J t-1 , σ s , σ r ) using (2) 4: Endfor Output: The output image G ← J n iter

ENSEMBLE OF SUBSPACE ICA WITH RGF FOR CLASSIFICATION

As shown in Fig. 1, the proposed classification method consists of five steps: 1) randomly select M features from the original feature set of hyperspectral data; 2) perform ICA on the M selected features; 3) apply RGF on each extracted components; 4) perform classification on the filtered images in each subset; 5) combine the results together to generate the final classification map by a majority vote rule. Next, we describe the implementation for each step. 1) Random subspace: K subsets of spectral bands in hyperspectral data are randomly selected. Each subset contains M features. In this case, there are different features in each subset, resulting in generating different input features of RF classifier by using ICA and RGF in the next two steps, which could be beneficial for the decision fusion in the last step.

2) ICA: The aim of this step is to extract discriminant ICs for the classes in each subset, to be used for classification. Here, we propose to use ICA-EBM due to its superior separation performance and moderate computational complexity [START_REF] Li | Independent component analysis by entropy bound minimization[END_REF].

3) RGF: RGF is performed on each extracted component to obtain the mth feature in ith subset,

G m k ← Algorithm 1 IC m k , σ s , σ r , n iter (3)
where, σ s and σ r are the spatial and range standard deviations of the filter. IC m k is the mth (m = 1, ..., M ) component derived from ICA in the kth (k = 1, ..., K) subset. G m k is the resulting feature obtained by RGF.

4) Classification: RF is used for the classification of the filtered features [START_REF] Breiman | Random forests[END_REF]. In RF, two parameters, number of trees and number of selected features are empirically selected. In this paper, the number of trees is set to be 100 and the number of selected features is set to be √ M . 5) Decision fusion: Different classification results are combined for the final class decision by a majority vote rule.

EXPERIMENTAL RESULTS AND ANALYSIS

In this section, the proposed approach is evaluated using real hyperspectral data, which is recorded by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor over the Indian Pines in Northwestern Indiana, USA. This scene, which comprises 220 spectral bands in the wavelength range from 0.4 to 2.5 µm with spectral resolution 10 nm, is composed of 145 × 145 pixels, and the spatial resolution is 20 m/pixel. This dataset has 16 classes of interest. We randomly select 30 samples per class to form the training set and the rest of the pixels are used for testing (for the minor classes Grass/pasture-mowed and Oats, the training samples remain constant). K and M are set to be 10 and 16, respectively. σ s and σ r in RGF are set to be 7 and 0.1, respectively. The results are obtained after 10 Monte Carlo runs.

In the first experiment, the performance of the proposed method is compared with the following algorithms: 1) original spectral information; 2) random subspace (RS): proposed method without ICA and RGF; 3) random subspace with ICA (RS-ICA): proposed method without RGF and 4) random subspace with RGF (RS-RGF): proposed method without ICA. The classification results are shown in Table . 1. It can be seen that RS method does not improve the performance. The main reason is that we select a small number of features (M = 16) in each subset. RS-ICA and RS-RGF produced the better results than RS and original, indicating the effectiveness of ICA and RGF techniques. The proposed method yields the best performance, with 31.6, 31.6, 24.3 and 20.4 percentage points over original, RS, RS-ICA and RS-RGF, respectively. The proposed method achieves to the best classification results for all classes.

In the second experiment, we present comparisons with the proposed approach with different ICA algorithms (e.g. JADE and FastICA) (Table 2). For the ERBM, the filter length is set to be 3. Increasing this value does not significantly influence the result. For the RS-ICA, ERBM gives the best result. This is due to the fact that ERBM not only matches a wide range of distributions but also considers sample dependence. EBM achieves the second best classification performance due to the use of an efficient entropy estimator and prior information. When we apply RGF on the features extracted from ICA techniques, the difference among the ICA techniques is not significantly, indicating that RGF plays an important role to improve the performance. Although ERBM is superior to the other ICA techniques using both spectral and spatial information as input, it requires more computation time than others. In this case, EBM is a good tradeoff between performance and computational complexity.

In the third experiment, we present comparisons of the proposed method against the aforementioned state-of-the-art spectral-spatial classifiers, such as GCK [START_REF] Li | Generalized composite kernel framework for hyperspectral image classification[END_REF], RoF-MRF [START_REF] Xia | Spectralspatial classification for hyperspectral data using rotation forests with local feature extraction and markov random fields[END_REF] and RS-EMAPs [START_REF] Xia | Random subspace ensembles for hyperspectral image classification with extended morphological attribute profiles[END_REF]. From Table 3, we can conclude that the proposed method outperforms those methods in this dataset.

In the fourth experiment, we investigate the parameter sensitivity of the proposed method to parameter choice. From Fig. 2, we observe that: 1) There is no pattern of dependence between K and the accuracy; 2) when M becomes larger, the proposed method tends to give better performance at the expense of increased computational complexity; 3) The proposed method achieves the best classification performance when σ s = 7 but the performance is satisfactory over a wide range of values as well. As observed in Fig. 2(d), the optimal range of σ r for this dataset is between 0.1 to 0.3.

Hence, selection of parameters is not very critical for the proposed methodology, which is an important added advantage. The only one that seems to provide better performance in a small range is σ r . In practice, the users might select a small value of σ r as in our case to better preserve the edges of hyperspectral data in order to increase the discrimination between the classes.

CONCLUSION

In this work, we developed a novel ensemble approach that combines random subspace, ICA, and rolling guidance filter for the classification of hyperspectral data. Experimental results confirmed improvements of the new approach over RS, RS-ICA and RS-RGF. The proposed method is superior to the recently proposed state-of-the-art spectral-spatial classifiers as well.

  ., x D ] T = As, which can be viewed as a linear combination of D random variables-or random processes when sample dependence is taken into account-s = [s 1 , ..., s n ] T through a D × D nonsingular mixing matrix A. The statistically independent components (ICs) y = [y 1 , ..., y D ]

Fig. 1 .

 1 Fig. 1. Schematic of the proposed ensemble classification method

Fig. 2 .

 2 Fig. 2. Sensitivity of the proposed method. (a) influence of K (M =16, σs = 7 and σr = 0.1), (b) influence of M (K = 10, σs = 7 and σr = 0.1), (c) influence of σs (K=10, M = 16 and σr = 0.1) and (d) influence of σr (K=10, M = 16 and σs = 7)

Table 1 .

 1 Overall, average and class-specific accuracies achieved by the classifiers.

	Class	Original	RS	RS-ICA	RS-RGF	Proposed
	Alfalfa	81.25	85.00	90.00	96.67	98.75
	Corn-no till	40.16	41.84	66.80	68.75	90.70
	Corn-min till	48.54	50.19	58.69	64.83	89.47
	Bldg-Grass-Tree-Drives	67.60	70.69	81.13	95.25	99.75
	Grass/pasture	83.49	82.68	82.46	78.61	93.53
	Grass/trees	79.61	80.85	95.54	83.53	98.84
	Grass/pasture-mowed	87.69	89.23	86.92	94.62	99.23
	Corn	86.54	84.47	96.56	96.12	99.80
	Oats	77.00	76.00	81.00	83.00	100.00
	Soybeans-no till	64.37	65.82	72.75	66.52	91.86
	Soybeans-min till	52.25	50.96	40.14	59.62	88.24
	Soybeans-clean till	48.99	48.46	72.24	69.14	94.67
	Wheat	94.62	94.34	99.51	94.45	99.34
	Woods	83.31	83.41	92.99	82.48	98.27
	Hay-windrowed	48.83	41.31	56.54	93.57	98.23
	Stone-steel towers	98.00	98.00	96.00	98.92	99.08
	OA	61.60	61.53	68.82	72.75	93.15
	AA	71.39	71.45	79.33	82.88	96.23

Table 2 .

 2 Classification accuracies obtained from the proposed methods using different ICA methods.

	Classifier	EBM	RS-ICA ERBM JADE	FastICA
	OA	68.82	72.61	65.36	65.29
	AA	79.33	83.55	71.67	72.27
	Classifier	EBM	Proposed method ERBM JADE	FastICA
	OA	93.15	93.58	92.75	93.16
	AA	96.23	96.63	96.14	95.82

Table 3 .

 3 Classification accuracies obtained from the proposed methods in comparisons of other spatial-spectral classifiers

	Classifier	RoF-MRF [14]	GCK [13]	RS-EMAPs [15]	Proposed method
	OA	86.24	88.35	92.14	93.15
	AA	92.76	92.62	94.71	96.23

ACKNOWLEDGMENT

The authors would like to thank Prof D. Landgrebe from Purdue University to provide the AVIRIS hyperspectral data. This study has been carried out with financial support from the French State, managed by the French National Research Agency (ANR) in the frame of the "Investments for the future" Programme IdEx Bordeaux-CPU (ANR-10-IDEX-03-02).


Kariman Abelin-Genevois
email: k.abelin-genevois@cmcr-massues.com

Erik Estivalezes

Jerome Briot

Annick Se ´vely

Jerome Sales De Gauzy

Pascal Swider

Spino-pelvic alignment influences disc hydration properties after AIS surgery: a prospective MRI-based study

Keywords: Adolescent idiopathic scoliosis, MRI, Spinal fusion, Intervertebral disc, Pelvic incidence, Disc hydration & Kariman

Purpose To analyze the disc hydration and volumetric changes of the intervertebral disc after scoliosis surgery depending on the sagittal spino-pelvic organization. Methods We conducted a prospective MRI study in 45 patients with surgically treated adolescent idiopathic scoliosis (AIS) with a minimum 2-year follow-up. Fusion ended at L1 (n = 13), L2 (n = 14), L3 (n = 11) or L4 (n = 7). Total disc (Vd) and nucleus volumes (Vn) were extrapolated from 3D reconstruction using a custom-made image processing software (Biomechlab Ò , Toulouse, France). Nucleus and external disc contours were semi-automatically detected on turbo spin echo T2-weighted sequence joined 3-mm sagittal cuts. Disc hydration was extrapolated from the nucleus-disc volume ratio (Vn/Vd). Radiographic sagittal parameters were measured pre-and post-operatively on full spine standing views (pelvic incidence, sacral slope, L1S1 lumbar lordosis). Lumbo-pelvic congruity was calculated by the ratio LL/SS according to Stagnara. Results Mean PI of the cohort was 55°(34°-85°). After surgical correction, lumbar lordosis was slightly increased by 3°(p = 0.02) decreasing lumbo-pelvic congruity from 1.37 to 1.27 (p \ 0.01). When pelvic incidence was less than 55°(mean PI 46°), nucleus volumes have increased on average by 30 % compared to the preoperative status in the unfused lumbar discs, while the total disc volumes has remained stable. Five-year follow-up (n = 13) confirmed the constant improvement of the disc hydration ratio. When PI was high (mean PI 64°), volumetric changes were very mild and significant changes in nucleus volumes and disc hydration ratio concerned only the intermediate lumbar levels (L2L3, L3L4 and L4L5). Conclusions This prospective MRI study showed a significant and sustainable improvement in T2 hypersignal of the disc, indirectly indicating improvement of disc hydration content after AIS surgery. Analysis of disc volumetric changes according to the pelvic incidence suggests that these changes are under the influence of the sagittal spino-pelvic alignment. PI seems to play a key role in the homeostasis of the discs under fusion and should be taken into account for preoperative planning. The restoration of the lumbo-pelvic congruence may help to limit early degenerative changes in the free-motion segment discs after AIS surgery. Hydration content was less sensitive to surgery when PI was high, suggesting higher shear stress in the lower discs. Longer follow-up is required to confirm this hypothesis.

Introduction

Spinal sagittal curvatures are specifically developed in humans together with sacrum verticalization as an answer to bipedalism. Ideally, the spino-pelvic organization is characterized by the correlation of the pelvic incidence (PI), an individually constant morphological parameter, and the sacral slope (SS) and pelvic tilt (PT), which are mathematically linked [START_REF] Legaye | Pelvic incidence: a fundamental pelvic parameter for three-dimensional regulation of spinal sagittal curves[END_REF]. Moreover, lumbar lordosis (LL) is strongly correlated to sacral slope [START_REF] Vialle | Radiographic analysis of the sagittal alignment and balance of the spine in asymptomatic subjects[END_REF]. Stagnara emphasized the importance of the lumbo-pelvic congruity, i.e., the relation LL/SS [START_REF] Stagnara | Reciprocal angulation of vertebral bodies in a sagittal plane: approach to references for the evaluation of kyphosis and lordosis[END_REF]. Sagittal angles create the most suitable alignment to the pelvic morphology, to provide a stable and economic erect posture in terms of energy expenditure. In AIS, spinal sagittal alignment is abnormal. Somerville demonstrated the relative overgrowth of the anterior column, thus describing the scoliotic deformity as a rotational lordosis [START_REF] Somerville | Rotational lordosis; the development of single curve[END_REF]. Main sagittal abnormalities are thoracic hypokyphosis and a moderate increase of LL. Thoracolumbar curves may induce thoracolumbar kyphosis. Spino-pelvic parameters remain strongly interrelated and comparable to normal population, except that the pelvic incidence is higher in AIS [START_REF] Mac-Thiong | Sagittal plane analysis of the spine and pelvis in adolescent idiopathic scoliosis according to the coronal curve type[END_REF][START_REF] Roussouly | Pre-and postoperative sagittal balance in idiopathic scoliosis: a comparison over the ages of two cohorts of 132 adolescents and 52 adults[END_REF].

Interest has been until now focused on the optimization of the sagittal correction, but did not explore the importance of the sagittal spino-pelvic organization in surgical planning and quality of correction. The surgical correction of the deformity, beyond stabilizing the deformity progression, aims the theoretical objective of halting the accelerated aging of the lumbar spine. The results of longterm historical cohort after spinal fusion with Harrington instrumentation have shown the importance to restore an adequate lumbar lordosis to prevent early degenerative changes and flat-back syndrome [START_REF] Danielsson | The prevalence of disc aging and back pain after fusion extending into the lower lumbar spine. A matched MR study twenty-five years after surgery for adolescent idiopathic scoliosis[END_REF]. Modern instrumentation introduced in the 1980s by Cotrel and Dubousset brought to the forefront the sagittal contouring of the spine.

Intervertebral disc (IVD) is an avascular structure formed by the annulus fibrosus and an inner hydrated complex, the nucleus pulposus, characterized by high water content. IVD hydration, depending on proteoglycans content, is a leading determinant of the disc mechanical response to loading stress. In physiological conditions, nucleus pulposus exhibits viscous, fluid-like behavior, with an effective aggregate modulus. Loss of water content is one of the first events leading to disc degeneration [START_REF] Hughes | The pathogenesis of degeneration of the intervertebral disc and emerging therapies in the management of back pain[END_REF]. Disc degeneration results in shear stiffness increase and decrease in aggregate modulus and loss of swelling pressure. Decreased hydration of NP results in decreased internal pressurization and disc height. These structural changes will result in transferring higher compressive loads to AF, compromising the disc function.

MRI is an accurate noninvasive tool that correlates signal intensity to water content in the nucleus [START_REF] Takashima | Correlation between T2 relaxation time and intervertebral disk degeneration[END_REF]. Previous MRI studies on scoliotic patients have found mildto-moderate degenerative changes at the lumbar disc level, most using the Pfirmann score to define the degenerative changes. Green et al. [START_REF] Green | Long-term magnetic resonance imaging follow-up demonstrates minimal transitional level lumbar disc degeneration after posterior spine fusion for adolescent idiopathic scoliosis[END_REF] showed that twothirds of non-instrumented discs demonstrated mild signs of early disc degeneration one decade after AIS surgery. In order to improve our understanding of the postoperative behavior of IVD after spinal fusion, we conducted an MRI study based on 3D reconstruction of the disc to quantify the volume and hydration of the intervertebral disc [START_REF] Violas | Quantification of intervertebral disc volume properties below spine fusion, using magnetic resonance imaging, in adolescent idiopathic scoliosis surgery[END_REF]. According to the level of instrumentation, the nucleus/disc ratio, indirectly indicating hydration content of the disc, increased as a consequence of nucleus size increase and hydration uptake, suggesting that the correction of scoliosis modified the lumbar disc homeostasis. However, to understand what factors influence the future of the free segment below spinal fusion, we have continued the prospective follow-up and monitored the radiological parameters of the AIS cohort. We designed the present study to investigate the influence of the spinopelvic morphology on the behavior of lumbar IVD in a cohort of surgically treated AIS. The main hypothesis is that the pelvic morphology influences disc properties and biology after surgical correction of AIS.

Methods

The protocol of the study was initially approved by the Institutional Review Board of the Ethic Committee. We consecutively included patients with AIS candidate to surgery, who were scheduled in our institution between 2005 and 2007.

Forty-five patients (40 males, 5 females) achieved the complete follow-up (2 years minimum) with prospectively collected clinical, radiological and MRI data. Mean age at surgery was 15.1 ± 1.7 years. Magnetic resonance imaging (MRI) was performed after informed consent before and during the postoperative follow-up (at least 2 years after surgery). Posterior instrumented fusion using multisegmental instrumentation was performed by one experienced spine surgeon in the same institution.

Long cassettes standing antero-posterior and lateral radiographs of full spine were performed preoperatively. Major curve magnitude was measured according to the Cobb method on AP radiographs. Curve flexibility was assessed in laying position, by right and left side-bending radiographs. Inferior limit of fusion was decided according to the first most symmetrical disc on the side bending radiographs. The most cephalad and symmetrically loading disc was chosen as the inferior limit of the construct.

The following radiographic spino-pelvic parameters were measured on lateral views by an independent experienced observer (spine surgeon, PhD student) (Fig. 1): lumbar lordosis (LL), sacral slope (SS) and pelvic incidence (PI). Pre-and post-operative values of the spinopelvic parameters were compared with two-tailed Student's t tests. The relation between all spinal and pelvic parameters was assessed using Pearson coefficients. The level of significance was set at 0.05.

IVD volumetric analysis

Each AIS patient enrolled in the prospective study underwent a standardized MRI protocol before surgery, 3 months after surgery and at least 2 years after surgery. To minimize the bias of diurnal variations of disc height and nucleus size, MRI examinations were scheduled in the morning (patients were called at the latest at 8:00 am) and patients were asked not to have any physical activity. Acquisitions of the full spine were performed using a turbo spin echo T2-weighted sequence in the sagittal plane on a Magneton Vision 1.5 T. (joined cuts of 3 mm). Disc volumetry was extrapolated using custom-made image processing software developed with Matlab Ò (Mathworks Ò ). The process is detailed in Fig. 2. Using our new software Biomechlab Ò (Laboratoire de Biome ´canique, Toulouse, France), a semi-automatic detection of the contours of each lumbar disc was performed. The external limit of annulus fibrosus and nucleus pulposus were contoured on each sagittal slice by an independent spine surgeon. Nucleus pulposus was identified as the most hydrated inner complex (T2 high-intensity signal). The contrast was adjusted according to the cerebro-spinal fluid in order to detect the most hydrated part of the disc. Each lumbar disc was composed of approximately 10 sagittal cuts. After 3D volume reconstruction, volume properties of the total disc and the nucleus were extracted at each unfused lumbar level. Volumetric values were analyzed postoperatively in comparison to the preoperative one and served as reference as no normative values were available at the time of our study.

Total disc and nucleus volumetric changes (DVd, DVn) were analyzed according to the preoperative pelvic incidence. Patients with PI inferior to the average PI value of the cohort (55°) were considered as low PI group. Patients with PI equal or superior to 55°were considered as high PI group. Inside high PI subgroup, a second analysis compared groups according to the extent of spinal fusion. Constructs ending at L1 or L2 level were defined as short fusion subgroup. Constructs ending down to L3 or L4 were considered as long fusion subgroup. Comparisons between pre-and post-operative disc and nucleus volumes were assessed by Student's t test (level of significance, p \ 0.05).

Results

Mean preoperative curve magnitude was 48°(36°-89°). Mean curve correction was 62 % (36-81 %).

Prior to surgery, mean LL was -57.6°± 11.2°. The pelvic parameters were measured at 55.2°± 11.3°for the PI, 42.8°± 10.9°for the SS. LL was moderately increased after spinal fusion (?3°, p = 0.02). PI and SS remained unchanged. The correlation between LL and SS was weaker after surgery (LL/SS = 1.27; r = 0.72, p \ 0.01) than preoperatively (LL/SS = 1.37; r = 0.59, p = 0.02) (Table 1).

Spino-pelvic parameters were separately analyzed according to the length of instrumentation (Table 2). In both surgical groups, pelvic parameters remained unchanged. Correlation between LL and SS was higher in the short fusion group (preoperative correlation 0.78, p \ 0.001; postoperative correlation 0.62, p = 0.007) than the long fusion group (preoperative correlation 0.65, p = 0.004; postoperative correlation 0.55, p = 0.016) and decreased postoperatively in both groups.

IVD volumetric changes

Disc volumetric changes were analyzed at each unfused lumbar level and results are presented separately according to the preexisting pelvic morphology (low or high pelvic P S Fig. 1 Spino-pelvic parameters measurement on standing sagittal view of the spine. Lumbar lordosis (LL) is measured between the superior endplate of L1 and S1. Sacral slope (SS) is measured between the horizontal line and the sacral plateau. Pelvic incidence (PI) is measured between the perpendicular line to the sacral plateau and the line passing by the midline of the center of femoral heads incidence). Subgroups were comparable in terms of age, initial Cobb angle and frontal balance (main curve correction, lumbar disc wedging and CSVL deviation).

The low PI group included 24 patients; all of them were followed 2 years or more. Mean PI was 46.1°(34°-54°). Fusion ended on L1 in eight patients, L2 in seven patients, L3 in nine patients and L4 in five patients. Table 3 summarizes disc (Vd) and nucleus volumes (Vn) variations quantified on 3 months, 2 years and 5 years postoperative MRI. Total disc volumes (Vd) were not modified by the surgical correction. Nucleus size significantly increased after 2 years in all free-motion lumbar levels except the first lumbar disc (L1L2, n = 8). Data at 5 years were available for 13 patients. They showed a marked increase of nucleus volumes at each level. Nucleus volumes increased at least by ?30 %.

The high PI group included 21 patients; all were followed at least 2 years postoperatively. Fusion ended on L1 in six patients, L2 in seven patients, L3 in seven patients and on L4 in one patient. Again, total disc volumes did not significantly change after the surgery. Two years after surgery, nucleus volumes increased at L2L3 and L3L4 but not in the distal parts of the lumbar lordosis (L1L2 L4L5 and L5S1) (Table 4). Nucleus volume varied differently according the extent of the fusion. Table 5 shows the volumetric changes when short fusion is performed on a high PI morphology.

The nucleus volume increased in the lower levels (1 or 2 levels below the inferior instrumented vertebra). Comparatively, nucleus volume increase were significantly lower in the subgroup with long fusion and high PI (Table 6), where moderate significance was only reached in the intermediate disc level L4L5 (?33.5 %, p \ 0.05).

Discussion

Longitudinal MRI quantification of disc volumetric properties after surgical correction of AIS showed a marked increase in the nucleus volumes within 2 years while total disc volumes remained unchanged. These results suggest a process of disc remodeling, independent of geometric remodeling induced by the surgical correction of the spinal deformity. Nucleus volume increase, directly linked to T2 hypersignal, accurately reflects the disc hydration status. Vn increase between pre-and post-operative period strongly suggests improvement of IVD hydration. We assume that this is related to the modification of the loading stress exerted on the discs, especially by a repositioning of the nucleus within the disc. Surgical correction of AIS, by reducing the disc wedging, especially at the apex of the lumbar lordosis, apparently improved functional conditions of the disc under loading stress. According to Setton, mechanical factors influence the biological response of the IVD cells [START_REF] Setton | Cell mechanics and mechanobiology in the intervertebral disc[END_REF]. IVD cells are highly sensitive to differences in cells and tissue (collagen fibers) geometry. The primary stimuli in NP cells are fluid pressurization and volume change. Accadbled et al. [START_REF] Accadbled | Influence of growth modulation on the effective permeability of the vertebral endplate. A porcine experimental scoliosis model[END_REF] assumed that static According to Green et al. [START_REF] Green | Long-term magnetic resonance imaging follow-up demonstrates minimal transitional level lumbar disc degeneration after posterior spine fusion for adolescent idiopathic scoliosis[END_REF], short constructs may preserve the free-motion segment from early degenerative changes with better stress distribution. However, inadequate sagittal imbalance may add considerable stress on the underlying discs. Our results suggest that the postoperative biomechanical behavior of IVDs depends on the fusion mass extent, but may differ according to the pelvic morphology. When pelvic incidence is low, the spinal shape is relatively flat with lower sacral slope and lumbar lordosis [START_REF] Legaye | Pelvic incidence: a fundamental pelvic parameter for three-dimensional regulation of spinal sagittal curves[END_REF]. Spinal fusion for AIS in this condition achieves optimal correction of scoliosis and sagittal contouring. This may explain that adequate surgical correction of AIS in the frontal plan, by rebalancing the disc asymmetry, may induce better volumetric changes and disc rehydration at all lumbar levels. In the low PI subgroup, AIS surgery improved both nucleus volume and hydration content at all lumbar levels. When PI is high, the ideal sagittal spinal shape is more curved [START_REF] Legaye | Pelvic incidence: a fundamental pelvic parameter for three-dimensional regulation of spinal sagittal curves[END_REF], surgical correction of AIS may have created a relative loss of lordosis and increase the constraints exerted on the remaining lumbar discs. As a consequence, only moderate remodeling effect was observed. AIS patients with high PI require larger sagittal correction, which may be challenging when initial curve is severe and stiff.

Ideal biomechanical conditions are reached after selective fusion with low PI. Assuming that the scoliotic discs have a biomechanical dysfunction proportionate to the severity of the curvature and their position relative to the apex [START_REF] Rajasekaran | ISSLS prize winner: a study of effects of in vivo mechanical forces on human lumbar discs with scoliotic disc as a biological model: results from serial postcontrast diffusion studies, histopathology and biochemical analysis of twenty-one human lumbar scoliotic discs[END_REF], the amount of correction required to induce significant and durable disc modifications is as higher as pelvic incidence is greater.

In AIS, spino-pelvic alignment differs from the normal population. Our results are similar to those from Roussouly et al. [START_REF] Roussouly | Pre-and postoperative sagittal balance in idiopathic scoliosis: a comparison over the ages of two cohorts of 132 adolescents and 52 adults[END_REF]: PI is higher, SS is higher and pelvis is more anteverted than normal population. LL is strongly correlated to SS. After surgical correction, parameters remained in the range of the preoperative values. Significant differences in the biomechanical behavior of the lumbar spine were observed depending on the pelvic incidence. Our results demonstrated that in low PI group, nucleus volume and disc hydration significantly improved in the long-term follow-up. Remodeling was effective for the lowest segments (lower arc of LL). In this subgroup, volumetric data 5 years or more after arthrodesis showed that remodeling was a constant and durable phenomenon, at all unfused lumbar discs. The highest improvement was observed in L5S1 level. Patients with high PI showed more modest volumetric and hydration changes compared to low PI group. These changes occurred only in the intermediate unfused lumbar segments (L2L3, L3L4). Volumetric changes of L5S1 were heterogeneous and not significant, probably reflecting stronger mechanical stress. Galbusera et al. [START_REF] Galbusera | Influence of sagittal balance on spinal lumbar loads: a numerical approach[END_REF] found sagittal parameters to influence spinal lumbar loads and muscle activity. Among them, LL was found to have a high relevance in determining the shear loads in comparison with the compressive loads. In this paper, the authors did not argument on the pelvic incidence. We can assume that for patients with a high PIinvolving a higher SS-theoretical LL should be more important to maintain the lumbo-pelvic congruity. Therefore, high PI should be related to higher shear stress after long fusion above the lumbar spine as it is in spondylolisthesis [START_REF] Labelle | Spino-pelvic sagittal balance of spondylolisthesis: a review and classification[END_REF][START_REF] Hammerberg | New concepts on the pathogenesis and classification of spondylolisthesis[END_REF][START_REF] Roussouly | Sagittal alignment of the spine and pelvis in the presence of L5-S1 isthmic lysis and low-grade spondylolisthesis[END_REF]. Whereas for short fusion, maximum mechanical stresses are absorbed by the adjacent segment allowing better hydration two levels under the fusion (including L5S1). Thus, in idiopathic scoliosis, it appears that the sagittal organization may have a leading role in the biomechanical conditions of the lumbar discs. In AIS surgical cohort, Roussouly et al. [START_REF] Roussouly | Pre-and postoperative sagittal balance in idiopathic scoliosis: a comparison over the ages of two cohorts of 132 adolescents and 52 adults[END_REF] showed that smaller PI in AIS correlated with better balance and better postoperative result. This probably reflects better biomechanical conditions and higher stability for the remaining mobile segments.

Limitations of the study

-Spino-pelvic parameters may be more accurately analyzed by the differential analysis of the two components of lumbar lordosis (upper and lower arc) according to the inflexion point, rather than an artificial set of L1S1 lordosis. We did not have such technology for the radiological analysis at the time we started the study. Moreover, we did not measure the thoracic kyphosis in order to simplify the radiographic analysis. Thoracic kyphosis is weakly related to the lumbar lordosis and to none of the pelvic parameters [START_REF] Vialle | Radiographic analysis of the sagittal alignment and balance of the spine in asymptomatic subjects[END_REF]. Therefore, it may not have any influence on the IVD volume variations. The role of thoracic kyphosis will be carefully addressed by another ongoing study focusing on the adjacent segment disease after spinal fusion.

Moreover, thoracic kyphosis measurement on full standing X-ray is of weak reproducibility due to the projection of the shoulders. -We did not assess the functional status of our patients.

In the medical charts, no report of lumbar pain or reduction of daily activity was noticed in the clinical follow-up while these patients were carefully watched annually. None of these patients required additional surgery. -This study focused on the signal intensity on MRI T2 sequences which specifically explore the nucleus signal intensity and has been shown to be correlated to disc hydration. However, this data is only an indirect sign of disc functional status. Rajasekaran et al. [START_REF] Rajasekaran | ISSLS prize winner: a study of diffusion in human lumbar discs: a serial magnetic resonance imaging study documenting the influence of the endplate on diffusion in normal and degenerate discs[END_REF] studied the diffusion pattern in the cartilage endplate. Alterations of this zone in MRI were associated with substantial changes in the diffusion pattern of lumbar IVDs in scoliotic patients. The addition of such information to our findings may give a more functional view of the IVD (immediate and long-term effect).

Conclusion

Although considerable advances have been made in 3D deformity correction after the advent of the Cotrel Dubousset instrumentation, surgeons should pay careful attention to lumbar lordosis and the lumbo-pelvic congruity. In case of inadequate lumbo-pelvic alignment, hydration of the disc may be permanently altered, leading to subsequent degenerative changes. LL should remain congruent with the pelvic morphology especially when extending the fusion to the lower lumbar levels and if PI is high. In the light of these results, it seems that the surgical planning should consider the pelvic morphology to limit the mechanical stress on the discs of the mobile segment underlying the fusion, as patients with high pelvic incidence experienced less disc remodeling after surgery when fusion was extended to the lumbar spine.

Conflict of interest None.

Fig. 2

 2 Fig. 2 3D reconstruction of the intervertebral disc from MRI T2 sagittal sequences

Table 1

 1 Spino-pelvic parameters changes after AIS surgical correction

		Preoperative	Postoperative	p value
		values	values
	PI	55.2°? 11.3°55.3°? 11.0°NS
	LL (L1S1)	-57.6°± 11.2°-59.9°± 11.8°0.022
	SS	43.2°± 10.8°44.4°± 9.4°NS
	LL/SS	1.37 ± 0.2	1.27 ± 0.2	0.007
	Pearson	r = 0.72	r = 0.59
	correlation
	Bold value indicates statistically significant

Table 2

 2 Influence of lumbar fusion extent on spino-pelvic parameters

		Short fusion	Long fusion
	PI	55.8°55.2°L
	L preop	-57.7°-56.6°P
	ostop	-53.9°p = 0.047* -55.9°p = 0.43*
	SS preop	42.1°43.6°P
	ostop	43.7°p = 0.28*	44.0°p = 0.37*
	LL/SS preop	1.42	1.34
	Postop	1.26 p = 0.006*	1.30 p = 0.39*
	Pearson correlation	0.78	0.65
	Coefficient	0.62	0.55

* Paired Student's t test. Significance level set at 0.05

Table 3

 3 Volumetric changes of the nucleus (Vn) and the disc (Vd) in the unfused lumbar spine in the low PI subgroup (pelvic incidence \55°) Preoperative values served as reference as no reference data exist in the normal population. p values are given by Pearson test between pre-and post-operative values at each follow-up period (3 months, 2 years and 5 years)

		3 months PO (%), n = 24	p value*	2 years PO (%), n = 24	p value*	5 years PO (%), n = 13	p value*
	DVd
	L5S1	1.5	0.38	6.7	0.49	-8.3	0.21
	L4L5	-0.1	0.34	6.0	0.43	-1.1	0.43
	L3L4	0.2	0.34	3.3	0.28	10.5	0.14
	L2L3	-4.5	0.47	-0.5	0.45	-1.9	0.25
	L1L2	0.8	0.30	10.6	0.31	8.8	0.44
	DVn
	L5S1	3.8	0.28	40.1	0.011	31.5	0.011
	L4L5	4.9	0.40	32.4	0.002	41.3	>0.001
	L3L4	12.0	0.13	26.2	0.002	30.1	0.011
	L2L3	-2.9	0.46	16.2	0.04	33.3	0.008
	L1L2	-11.4	0.42	0.0	0.22	32.7	0.005
	Bold values indicate statistically significant

*

Table 4

 4 Volumetric changes of the nucleus (Vn) and the disc (Vd) in the unfused lumbar spine in the high PI subgroup (pelvic incidence C55°)

		3 months PO (%), n = 21	p value	2 years PO (%), n = 21	p value	5 years PO (%), n = 5	p value
	DVd
	L5S1	-2.8	0.40	5.1	0.40	-13.5	0.15
	L4L5	-2.7	0.28	-3.8	0.44	-5.5	0.37
	L3L4	-2.4	0.35	0.0	0.28	-4.7	0.39
	L2L3	2.5	0.43	16.9	0.44	-2.5	0.22
	L1L2	10.9	0.48	11.8	0.36		NA
	DVn
	L5S1	1.2	0.44	14.3	0.06	7.8	0.36
	L4L5	6.8	0.15	17.9	0.01	21.0	0.01
	L3L4	4.3	0.32	17.6	0.02	32.9	0.02
	L2L3	5.8	0.26	16.8	0.08	30.9	0.04
	L1L2	9.4	0.37	24.3	0.17		NA
	Bold values indicate statistically significant

Table 5

 5

	Volumetric variations after selective fusion (LIV at L1		3 months PO (%), n = 13	p value	2 years PO (%), n = 12	p value
	or L2)	DVd
		L5S1	3.7	0.30	2.1	0.42
		L4L5	2.6	0.30	4.0	0.39
		L3L4	2.4	0.36	0.1	0.41
		L2L3	9.0	0.33	9.8	0.46
		L1L2	13.9	0.50	11.8	0.37
		DVn
		L5S1	10.3	0.36	21.2	0.03
		L4L5	0.6	0.40	12.7	0.01
		L3L4	5.9	0.49	17.6	0.07
		L2L3	5.8	0.43	11.6	0.16
		L1L2	9.4	0.39	24.3	0.35
		Bold values indicate statistically significant
	Table 6 Volumetric variations after long AIS fusion (LIV at L3		3 months PO (%), n = 8	p value	2 years PO (%)	p value
	or L4)	DVd
		L5S1	-1.1	0.40	26.7	0.20
		L4L5	-2.8	0.37	-3.0	0.23
		L3L4	-2.5	0.40	0.1	0.26
		DVn
		L5S1	-15.6	0.40	-6.4	0.25
		L4L5	20.6	0.03	33.5	0.04
		L3L4	0.9	0.36	17.3	0.42
		Bold values indicate statistically significant

Department of Pediatric Orthopaedic Surgery, Ho ˆpital des Enfants, CHU Purpan, Toulouse, France


Laurent André

Michel Franceschi

Pierre Chiquet

Arnaud Wuilleumier

Sulphur isotopes: a tool to understand the

come

1. BRGM, Orléans, France l.andre@brgm.fr 2. ENSEGID, Bordeaux, France 3. TIGF, Pau, France 4. BRGM, Bordeaux, France KEYWORDS: sulphur isotopeswater chemistryflow paths Chemical composition of waters from deep aquifers reflects the interactions between waters and reservoir rocks. The determination of an accurate chemical composition of waters is thus necessary to identify the origin of this mineralisation. The water chemistry of the Infra-Molassic Eocene aquifer (Aquitaine Basin -France) has been investigated through different studies highlighting various origins for water mineralization. Isotopic analyses helped, in agreement with hydrogeology, to identify geochemical basins whose properties modify the chemical water composition (André et al., 2002;Douez, 2007).

However, if the chemical composition of waters from this deep aquifer (about 500 to 1000 m depth) does usually not change with time, seasonal variations of sulphate concentrations occur in a specific area. About 200 analyses of sulphate collected during more than 10 years show variations between 10 and 110 mg SO 4 /L. These variations of sulphate concentrations are correlated with the water conductivity but they are anti-correlated with piezometric variations (reaching about 80 m per year) which are due to the seasonal storage of natural gas in the aquifer and are recorded about 30 km around the storage area. Several hypotheses are considered in order to explain these sulphate variations: mixing of waters from different aquifers, vertical transfers by advection or dispersion from the molasse aquitard, local biological processes… This study uses both isotopic tracers ( 18 O and 34 S of sulphates, 13 C and 14 C) and composition of trace elements (F, B, Ba, Sr…) in order to determine the origin of these variations. It will be based on existing chemical data but also on the acquisition of new ones. This chemical approach will be secondly coupled with a hydrological modelling of the specific area in order to understand the local behaviour of the aquifer (residence time, recharge processes, interaction with adjacent reservoirs/aquitards).


Lach Adeline

André Laurent

Lassin Arnault

Azaroual Mohamed

Cézac Pierre

Serin Jean-Paul

Estimation of Pitzer interaction parameters from heat capacity and osmotic coefficient for the NaOH-H 2 O system at temperatures from 0 to 150°C and concentrations up to NaOH(s) saturation

Keywords: Pitzer interaction model, NaOH electrolyte system, heat capacity, high saline solutions

Introduction

The aim of this study is to determine new Pitzer interaction parameters for the NaOH-H 2 O binary system from heat capacity and osmotic coefficient data between 0 and 150°C and up to NaOH solid salt saturation. As shown in Fig. 1, from the excess Gibbs free energy (G ex ) we can access to several properties [START_REF] Pitzer | Activity coefficients in electrolyte solutions[END_REF]: the osmotic coefficient (ϕ), the apparent relative molar enthalpy (L ϕ ) and the apparent molar heat capacity (C p,ϕ ). With the last property, we can calculate heat capacity (C p ) of an electrolyte solution.

Fig. 1: Links between the three derivatives of the Gibbs excess energy (G ex ).

As shown in our previous work [START_REF] Lach | [END_REF] the interpretation of osmotic coefficient data needs taking into account the partial dissociation of NaOH. In this case, the interaction parameters used are β 0 Na+/OH-, β 1 Na+/OH-, C φ Na+/OH-, ζ Na+/OH-/NaOH and λ NaOH/NaOH with the following temperature dependence [3]:

𝑌(𝑇)=𝑎 1 +𝑎 2 𝑇+𝑎 3 𝑇²+𝑎 4 /𝑇+𝑎 5 ln𝑇+𝑎 6 /(𝑇-263)+𝑎 7 /(680-𝑇)+𝑎 8 /(𝑇-227) (Eq. 1)
The C p,ϕ expression depends on the Y J parameters which are a combination of the first and second temperature derivatives of Eq. 1. The partial molar heat capacity of the solute at infinite dilution (C p °) is determined from the HKF equation of state for aqueous species given in Johnson et al. [4].

These equations were implemented in the geochemical code Phreeqc V3 [5]. Coupling with optimization software (PEST), new interaction parameters have been determined using simultaneously the heat capacity and osmotic coefficient data.

Results

In Fig. 2 we plotted the calculated (dotted lines) and experimental (open symbols) osmotic coefficient as a function of the molar fraction of total NaOH molality. For convenience of representation, the curves were shifted 1.2 for 0°C, 1 for 25°C and so on. In Fig. 3, we plotted the calculated (dotted lines) and experimental (open symbols) heat capacity as a function of NaOH molality. The results are obtained at saturated water vapour pressure.

Conclusion

In this study, we proposed a new Pitzer parameterization able to reproduce the osmotic coefficient and the heat capacity data of NaOH-H 2 O complex solutions up to solid salt (NaOH(s)) saturation from 0 and 150°C. The developed methodology can be applied to many other electrolyte systems, if sufficient experimental data are available to fully parameterize the systems.

Fig. 2 :Fig. 3 :

 23 Fig. 2: Osmotic coefficient, φ, for the NaOH-H 2 O binary system at several temperatures.