Farooq Ahmad, Michel Dojat, Bernard Gibaud, Gilles Kassel, Lynda Temal, bernard.gibaud@univ-rennes1.fr Frederic Furst, Gilles Kassel, Pascal Lando, Anne Lapujade, bernard.gibaud@univ-rennes1.fr Gilles Kassel, bernard.gibaud@univ-rennes1.fr Michel Dojat, Bernard Gibaud, Gilles Kassel, Lynda Temal, bernard.gibaud@univ-rennes1.fr Sabine Bruaux, Jean-Yves Fortier, Frederic Furst, Gilles Kassel, Pascal Lando, Anne Lapujade, bernard.gibaud@univ-rennes1.fr 2008-10-01 2009-04-12 2009-04-24 2009-11-20 2010-07-01 [DEF] A DATASET can be superimposed with another DATASET implies that the domains of the corresponding mathematical functions coincide. The aim of this relation is to create an equivalence class whose meaning is that any DATASET that is member of the equivalence class may be superimposed with any other member of the same class. Referring to a common dataset instance is just a practical way to manage that, avoiding to calculate the transitive closure of the relationship when we want to know whether two DATASETS instances can be superimposed or not. affecte à affects at allows to carry out permet de réaliser bears at porte à carries out at réalise à classifie à classifies at commente comments complies with est en conformité avec conceives at conçoit à controls at contrôle à defines définit describes décrit enables to fulfil permet de remplir expresses at exprime à formally orders ordonne formellement generates génère a pour destinataire à has for addressee at a pour agent à has for agent at a pour auteur has for author a pour bénéficiaire à has for beneficiary at a pour cardinal has for cardinal a pour consequence à has for consequence at a pour contributeur has for contributor a pour données à has for data at a pour date has for date a pour date de fin has for end date a pour fonction has for function a pour instrument à has for instrument at a pour grandeur has for magnitude a pour fabricant has for manufacturer a pour membre pendant has for member during a pour acronyme de modèle has for model acronym a pour nom de modèle has for model name a pour nom has for name a pour propriétaire has for owner a pour fonction propre has for proper function a pour date de publication has for publication date a pour résultat à has for result at a pour numéro de série has for serial number a pour date de début has for start date a pour sujet à has for subject at a pour substrat à has for substrate at a pour thème à has for theme at a pour unité has for unit a pour numéro de version has for version number intentionally participates at participe intentionnellement à est un bénéficiaire de à is a beneficiary of at est une conséquence de à is a consequence of at est une donnée de à is a data of at est un résultat de à is a result of at est un thème de à is a theme of at est un numéro de version de is a version number of est une formule bien formée de is a well formed formula of est affecté par à is affected by at est agent de à is agent of at est un destinataire de à is an addressee of at est un instrument de à is an instrument of at est auteur de is author of est porté par à is borne by at est capable de faire is capable to perform est réalisé par à is carried out by at est commenté par is commented by est conçu par à is conceived by at est contributeur de is contributor of est controllé par à is controlled by at est défini par is defined by est décrit par is described by est autorisé à faire is empowered to perform est égal à is equal to est exprimé par à is expressed by at est extensionnellement équivalent à is extensionally equivalent to [DEF] ACTION A is generated, or is produced, by ACTION B when A is done by doing B: “doing A by doing B”. [CIT] (Pacherie, 2008, p. 198): “At least three cases [of productions] must be distinguished. The first is indeed causal generation. Doing A by doing B counts as an instance of causal generation when the production of B causes the production of A. For instance, turning the light by flipping a switch or breaking the glass by dropping it on a hard surface count as instances of causal generation. The second case is conventional generation, where doing B (in circumstances C) counts as doing A in virtue of a rule or convention that stipulates that is so counts. Thus, signalling a left turn by extending one’s left arm or voting in favour of the motion by raising one’s hand are instances of conventional generation. The third case is circumstantial generation, where doing B counts as doing A only if certain circumstances obtain. For instance, one breaks the world record for in the 100 m for men by running it in 9,77 s only in circumstances where no one has yet run this distance in 9,77 a or less.” est généré par is generated by est supérieur à is greater than est supérieur ou égal à is greater than or equal to est inscrit sur à is inscribed on at est intéressé par is interested in est inférieur à is less than est inférieur ou égal à is less than or equal to est membre de pendant is member of during est ordonné par is ordered by est propriétaire de is owner of est physiquement réalisé par à is physically realized by at est publié par is published by est éditeur de is publisher of est réalisé par à is realized by at est référencé par à is referenced by at est reféré par is referred to by est rapporté par is rendered account by est sujet de à is subject of at est substrat de à is substrate of at est supervisé par à is supervised by at est le cardinal de is the cardinal of est le nom de is the name of est utilisé par à is used by at models at modélise à orders ordonne physically realizes at réalise physiquement à realizes at réalise à references at référence à refers to se réfère à rapporte renders account of supervise à supervises at supporte à supports at uses at utilise à BMP-expression BMP-file BMP-format DICOM-zip-file EGEE-grid GIF-expression GIF-file GIF-format IMA-expression IMA-file core-ontology-programs-software:IMA-file IMA-format IMG-expression IMG-file IMG-format JPEG-expression JPEG-file JPEG-format PDF-expression PDF-file PDF-format RAR-expression RAR-file RAR-format URI URI-format URL URL-format WSDL-file WSDL-language XML-expression XML-file XML-format ZIP-expression ZIP-file [DEF & CIT] Article “ZIP File Format” of Wikipedia: “The ZIP file format is a lossless data compression and archival format. A ZIP file contains one or more files that have been compressed, to reduce their file size, or stored as-is.” ZIP-format 1 1 archive-file [DEF & CIT] Article “Archive File” of Wikipedia: “An archive file is a file that is composed of one or more files along with metadata that can include source volume and medium information, file directory structure, error detection and recovery information, file comments, and usually employs some form of lossless compression.” archive-format archive-format-expression binary-document-file binary-file [DEF & CIT] Article “Binary File” of Wikipedia: “A binary file is a computer file which may contain any type of data, encoded in binary form for computer storage and processing purposes.” cluster [CIT] Article “Cluster” of Wikipedia: “A computer cluster is a group of linked computers, working together closely so that in many respects they form a single computer”. 1 collection-of-computer-language-expressions 2 collection-of-computers command-expression [EX] Examples of COMMAND EXPRESSIONS are “cd /etc;find . -name "r*"” or “chmod 755 dossier”. command-language [DEF] Article “Command language” of Wikipedia: “A command language is a domainspecific interpreted language; common examples of command languages are shell or batch programming languages.” [EX] An example of command language is: Korn Shell. compilable-program [EX] Examples of COMPILABLE PROGRAMS are PROGRAMS written in C language or ADA. compiler compiling-a-program [DEF] COMPILING A PROGRAM is an action consisting in translating a computer language expression in another expression specified in a lower level language. composite-expression [EX] Examples of COMPOSITE EXPRESSIONS are “int x = 3” or “for(int i = 0;i<3;i++) System.out.println(“hello”);”. computer [CIT] Article “Computer” of Wikipedia: “A computer is a machine that manipulates data according to a list of instructions… The defining feature of modern computers which distinguishes them from all other machines is that they can be programmed. That is to say that a list of instructions (the program) can be given to the computer and it will store them and carry them out at some time in the future”. computer-language-expression computer-under-linux computer-under-windows computing-grid computing-grid-middleware connected-computer data-description-language [DEF] DATA DESCRIPTION LANGUAGES are opposed to DATA MANIPULATION LANGUAGES in that the former aim at specifying the structure of data whereas the latter aim at specifying ways of processing data. core-ontology-programs-software:data-description-language-expression data-description-language-expression data-manipulation-language [DEF] DATA MANIPULATION LANGUAGES are opposed to DATA DESCRIPTION LANGUAGES in that the former aim at specifying data processing whereas the latter aim at specifying the data which are processed. data-manipulation-language-expression data-processing data-storing developing-software device-file [DEF & CIT] Article “Device File” of Wikipedia: “A device file or special file is an interface for a device driver that appears in a file system as if it were an ordinary file. This allows software to interact with the device driver using standard input/output system calls, which simplifies many tasks.” directory [DEF & CIT] Article “Directory” of Wikipedia: “In computing, a directory, catalog, folder or drawer is an entity in a file system which contains a group of files and/or other directories. A typical file system may contain thousands (or even hundreds of thousands) of directories. Files are kept organized by storing related files in the same directory. A directory contained inside another directory is called a subdirectory of that directory.” distributed-platform document-description-language document-description-language-expression domain-specific-computer-language [DEF] [Lando et al., 2008]: “Domain-Specific Computer Languages are non-Turingcomplete languages restricted to the writing of particular types of expressions (database queries, operating system commands, etc.).” domain-specific-computer-language-expression executable-program [CIT] Article “Executable Program” of Wikipedia: An executable or executable file, in computer science, is a file whose contents are meant to be interpreted as a program by a computer. executing-a-program [DEF] EXECUTING A PROGRAM is a RUNNING A PROGRAM where the PROGRAM, an EXECUTABLE PROGRAM or INTERMEDIATE CODE, is directly executed, without being previously translated into some intermediate representation. execution-platform 1 file [DEF & CIT] Article “Computer File” of Wikipedia: “A computer file is a block of arbitrary information, or resource for storing information, which is available to a computer program and is usually based on some kind of durable storage. A file is durable in the sense that it remains available for programs to use after the current program has finished. Computer files can be considered as the modern counterpart of paper documents which traditionally were kept in offices' and libraries' files, which are the source of the term.” general-image-file general-image-format [EX] Examples of general image description languages are: JPEG, GIF. glite [DIV] Instances of the concept GLITE correspond to various versions of this software. glite-grid globus-toolkit [DIV] Instances of the concept GLOBUS TOOLKIT correspond to various versions of this software. grid grid-middleware [CIT] Article “Grid Middleware” of gridipedia.eu: “Grid middleware can be seen as Operating System of the virtual Super Computer that in reality consists of many independent nodes on different sites, but appears to the user as one computational unit”. high-level-computer-language [DEF] Article “High-level computer language” of Wikipedia: A high-level programming language is a programming language that, in comparison to low-level programming languages, may be more abstract, easier to use, or more portable across platforms. Such languages often abstract away CPU operations such as memory access models and management of scope. [EX] Examples of high-level computer languages are: Java, C. image-description-language-expression image-file image-format intermediate-code [EX] Examples of INTERMEDIATE CODES are PROGRAMS generated by compiling Java SOURCE CODE. intermediate-language [DEF] An INTERMEDIATE LANGUAGE is used by a HIGH-LEVEL LANGUAGE to generate an intermediate code which is then compiled into an object code or a MACINE CODE. [EX] An example of intermediate language is: Java Bytecode. internet [CIT] Article “Internet” of Wikipedia: “The Internet is a global network of interconnected computers, enabling users to share information along multiple channels. Typically, a computer that connects to the Internet can access information from a vast array of available servers and other computers by moving information from them to the computer's local memory. The same connection allows that computer to send information to servers on the network; that information is in turn accessed and potentially modified by a variety of other interconnected computers.” interpretable-program [EX] Examples of INTERPRETABLE PROGRAMS are PROGRAMS written in Prolog, or in Python. interpreter interpreting-a-program [DEF] INTERPRETING A PROGRAM is a RUNNING A PROGRAM where the PROGRAM, an INTERPRETABLE PROGRAM, is first translated into some efficient intermediate representation and then immediately executed. intranet [CIT] Article “Intranet” of Wikipedia: “An intranet is a private computer network that uses Internet technologies to securely share any part of an organization's information or operational systems with its employees.” lexeme [EX] Examples of LEXEMES are “class” “MyClass” or “3.14”. library [DEF] Article “Library” of Wikipedia: “A library is a collection of subroutines or classes used to develop software. Libraries contain code and data that provide services to independent programs. This allows code and data to be shared and changed in a modular fashion.” linux [DIV] Instances of the concept LINUX correspond to various versions of this OPERATING SYSTEM. low-level-computer-language [DEF] Article “Low-level computer language” of Wikipedia: In computer science, a lowlevel programming language is a language that provides little or no abstraction from a computer's microprocessor. The word "low" does not imply that the language is inferior to high-level programming languages but rather refers to the small or nonexistent amount of abstraction between the language and machine language; because of this, low-level languages are sometimes described as being "close to the hardware." [EX] An example of low-level computer language is: MASM machine-language [DEF] Article “Machine language” of Wikipedia: “Machine language is a system of instructions and data directly executed by a computer's central processing unit.” markup-file markup-language [DEF] Article “Markup Language” of Wikipedia: “A markup language is a set of annotations to text that describe how it is to be structured, laid out, or formatted. Markup languages have been in use for centuries, and in recent years have also been used in computer typesetting and word-processing systems.” [EX] An example of markup language is: HTML. markup-language-expression core-ontology-programs-software:medical-image-file medical-image-file core-ontology-programs-software:medical-image-format medical-image-format [EX] Examples of medical image description languages are: DICOM, ANALYZE. middleware [CIT] Article « Middleware » of Wikipedia: “Middleware is computer software that connects software components or applications”. network [DEF] A NETWORK allows COMPUTERS that are interconnected to carrying out EXCHANGING DATA between each other. [CIT] Article “computer network” of Wikipedia: “A computer network is a collection of computers and devices connected to each other. The network allows computers to communicate with each other and share resources and information”. operating-system [CIT] Article “Operating system” of Wikipedia: “An operating system (OS) is a computer program that manages the hardware and software resources of a computer. At the foundation of all system software, the OS performs basic tasks such as controlling and allocating memory, prioritizing system requests, controlling input and output devices, facilitating networking, and managing files. It also may provide a graphical user interface for higher level functions. It forms a platform for other software”. physical-platform platform program [CIT] (Lando et al. 2008) “a program is a Data Manipulation Language Expression ordered by a Turing-complete language which can be run on a computer”. programming-language [DEF] [Lando et al., 2008]: “Programming languages are languages enabling the description of arbitrary Computational Actions [data processing].” programming-language-expression [EX] Examples of PROGRAMMING LANGUAGE EXPRESSIONS are “private int al;” or “if (n > 2) return n * factorielle(n - 1);”. query-expression [EX] Examples of QUERY EXPRESSIONS are “create table table1 (id Integer, col2 Integer, col3 Date)”or “select * from table1 where id=3”. query-language [DEF] Article “Query language” of Wikipedia: “Query languages are computer languages used to make queries into databases and information systems”. [EX] Examples of query languages are: SQL, SPARQL. regular-file [DEF & CIT] Article “Regular File” of Wikipedia: “A regular file is a file that is an ordinary file containing zero or more bytes of data.” resource-location-description-format resource-location-description-format-expression running-a-program [DEF] RUNNING A PROGRAM is an action by which a PLATFORM carries out the instructions of a PROGRAM. running-a-software [DEF] RUNNING A SOFTWARE is an action by which a PLATFORM carries out the instructions of the software’s main program. running-a-web-service software source-code [DEF] Article “Source code” of Wikipedia: source code (commonly just source or code) is any sequence of statements and/or declarations written in some humanreadable computer programming language. special-file [DEF] A SPECIAL FILE is a FILE which is used by an operating system for specific usages. stderr [DEF & CIT] Article “Standard Streams” of Wikipedia: A STANDARD ERROR FILE is an output stream typically used by programs to output error messages or diagnostics. It is a stream independent of standard output and can be redirected separately. stdin [DEF & CIT] Article “Standard Streams” of Wikipedia: A STANDARD INPUT FILE is a special data file (often text) going into a program. stdout [DEF & CIT] Article “Standard Streams” of Wikipedia: A STANDARD OUTPUT FILE is the stream where a program writes its output data. storage-grid storage-grid-middleware storage-platform 1 storing-programs stream-file 1 symbolic-link [DEF & CIT] Article “Symbolic Link” of Wikipedia: “In computing, a symbolic link (also symlink or soft link) is a special type of file that contains a reference to another file or directory in form of an absolute or relative path and that affects pathname resolution.” text-file [CIT] Article “Text File” of Wikipedia: “Text files are sequences of readable characters such as letters, digits, punctuation, or white space; and control characters such as section boundaries, rendering instructions for different languages, line feeds and carriage returns.” unix [DIV] Instances of the concept UNIX correspond to various versions of this OPERATING SYSTEM. unix-file-address unix-file-addressing-format virtual-machine windows [DIV] Instances of the concept WINDOWS correspond to various versions of this OPERATING SYSTEM. windows-file-address windows-file-addressing-format word-expression word-file word-format CT-dataset [DEF] COMPUTED TOMOGRAPHY (CT) is an imaging method in which a cross-sectional image of the structures in a body plane is reconstructed by a computer program from the x-ray absorption of beams projected through the body in the image plane. EEG-dataset [DEF] Electro encephalography (EEG) is the measurement of electrical activity produced by the brain as recorded from electrodes placed on the scalp. H1-MR-spectroscopy-dataset H1-chemical-shift-imaging-dataset dataset:H1-chemical-shift-imaging-dataset H1-single-voxel-spectroscopy-dataset MEG-dataset [DEF] Magneto encephalography (MEG) is an imaging technique used to measure the magnetic fields produced by electrical activity in the brain via extremely sensitive devices such as superconducting quantum interference devices (SQUIDs). MR-dataset [DIV] A MR DATASET is either a RECONSTRUCTED DATASET or a NON- RECONSTRUCTED DATASET. However, the applications considered in the context of the Neurolog project concern essentially RECONTRUCTED MR DATASETS. So, for simplicity, all the specializations of MR DATASETS currently defined in the ontology are RECONSTRUCTED DATASETS. This choice will be reconsidered later on to take into account both RECONTRUCTED and NON-RECONSTRUCTED MR DATASETS. MR-spectroscopy-dataset dataset:MR-spectroscopy-dataset [DEF] MR Spectroscopy (or MRS) allows particular chemical compounds or metabolites (substances taking part in chemical processes in living organisms) to be observed. We should distinguish between SINGLE VOXEL MR Spectroscopy and CHEMICAL SHIFT IMAGING. The first concerns a single (usually cuboidal) voxel, and provides a spectrum. The second is also called Spectroscopic imaging. It provides a coarse-resolution image of the spectra measured at each voxel. Absolute concentrations of the metabolites or concentration ratios of pairs of metabolites (usually referenced to Cr) can be derived from these spectra, leading to ABSOLUTE METABOLITE CONCENTRATION DATASET and METABOLITE CONCENTRATION RATIO DATASET. PET-dataset [DEF]POSITRON EMISSION TOMOGRAPHY (PET) is a nuclear medicine imaging method similar to computed tomography, except that the image shows the tissue concentration of a positron-emitting radioisotope. SPECT-dataset [DEF] SINGLE-PHOTON EMISSION COMPUTED TOMOGRAPHY (SPECT) is an imaging method in which gamma photon–emitting radionuclides are administered and then detected by one or more gamma cameras rotated around the patient, using the series of two-dimensional images to recreate a three-dimensional view. T1-weighted-DCE-MR-dataset [DEF] A T1 WEIGHTED DCE MR DATASET reflects the dynamics of diffusion of the exogenous contrast media from the blood pool into the extra vascular extracellular space (EES) of the brain at a rate determined by the blood flow to the tissue, the permeability of the Brain Blood Barrier (BBB), and the surface area of the perfusing vessels. T1-weighted-MR-dataset dataset:T1-weighted-MR-dataset T1-weighted-MR-template-dataset T2-star-weighted-DCE-MR-dataset T2-star-weighted-MR-dataset dataset:T2-star-weighted-MR-dataset [DEF] A T2 and T2 STAR WEIGHTED DCE MR DATASET reflects the T2 and T2 STAR of tissue decrease as the Gd contrast agent bolus passes through the brain. From the changes along time reflecting how the Gd contrast agent bolus passes through the brain, it is possible to estimate the cerebral blood flow (represented in milliliters of blood per gram of tissue per minute) as well as the cerebral blood volume (represented in milliliters of blood per gram of tissue). However, absolute quantification of perfusion is unreliable since the arterial input function is not well characterized and the relaxivity is unknown. T2-weighted-DCE-MR-dataset T2-weighted-MR-dataset dataset:T2-weighted-MR-dataset T2-weighted-template-dataset absolute-metabolite-concentration-dataset dataset:absolute-metabolite-concentration-dataset absolute-proton-density-dataset dataset:absolute-proton-density-dataset [DEF] The absolute proton concentration [1H] of a tissue defines the number of protons per unit volume of tissue. The absolute proton density of a tissue refers to the concentration of protons in this tissue, relative to that of the same volume of water at the same temperature. It is expressed in pu (percent units). anatomical-dataset bias-field-dataset dataset:bias-field-dataset bold-dataset dataset:bold-dataset [DEF] BOLD imaging is sensitive to blood oxygenation (but also to cerebral blood flow and volume). This modality is essentially used for detecting brain activation (functional MR). Echo planar imaging is used mainly with a Gradient sequence (T2*weighted). Presto or Spin echo (T2 weighted) sequences can also be used. calibration-dataset dataset:calibration-dataset [DEF] A CALIBRATION DATASET serves for calibration purposes. It represents a model that enables to transform non-calibrated data into calibrated data. For instance, in MR, a Field Map can be acquired for estimating the modification of the field B0 (Static Magnetic Field) or B1 (Radiofrequency Transmit Field) induced by the presence of the subject (i.e. the magnetic susceptibility of the tissues). From this data (called Magnetic Field Quality datasets) a transformation may be computed in order to compensate for this non-uniformity of the field: this transformation is a CALIBRATION DATASET. contrast-agent-angio-MR-dataset 1 1 1 dataset dataset:dataset [SA] DATASETS are divided among MR DATASETS, PET DATASETS, CT DATASETS, SPECT DATASETS, MEG DATASETS, and EEG DATASETS according to their modality. We chose to restrict the definition of these modality DATASETS. All the parameters derived from such modality DATASETS, such as QUANTITATIVE T1 DATASET, etc., are not considered as modality DATASETS. [SA] DATASETS are divided among ANATOMICAL DATASETS, FUNCTIONAL DATASETS, METABOLIC DATASETS, and HEMODYNAMIC DATASETS according to the kind of entity they explore. [SA] DATASETS are divided among REGISTRATION DATASETS, SEGMENTATION DATASETS, TEMPLATE DATASETS, etc., according to the kind of processing they are the result of. [SA] DATASETS are divided among RECONSTRUCTED DATASETS and NON-RECONSTRUCTED DATASETS whether they provide measurements at well-identified locations in 3D space. 1 dataset-metadata 1 dataset-set-of-values dataset:diffusion-tensor-dataset diffusion-tensor-dataset [DEF] The DIFFUSION TENSOR DATASET reflects the directional dependence of the diffusion properties, independently of the orientation of the tissue with respect to the directions of measurements. D is calculated from a non diffusion-weighted image plus six or more diffusion-weighted measurements along non collinear directions. dataset:diffusion-weighted-MR-dataset diffusion-weighted-MR-dataset [DEF] The Stejskal-Tanner imaging sequence is used to exploit diffusion of water molecules. Tissues with highly mobile water, such as cerebrospinal fluid (CSF) (strong diffusion) appear dark on images acquired in the presence of strong diffusion gradients, reflecting the dephasing of the contributing spins. A diffusion map can be calculated by combining at least two diffusion-weighted images that are differently sensitized to diffusion but remain identical with respect to the other parameters, spin density, T1, T2, TR, and TE. By using, for instance, the image S0 without diffusion weighting (b=0) and one diffusion-weighted image (b > 0), we can calculate a D value for each pixel. A parametric image containing these data is called a diffusion map or apparent diffusion map (ADC). The latter term emphasizes the fact that the D values obtained with this procedure depend on the experimental conditions (eg, direction of the sensitizing gradient and diffusion time delta). displacement-field-dataset [DEF] “A displacement field is an assignment of displacement vectors for all points in a body that is displaced from one state to another. A displacement vector specifies the position of a point or a particle in reference to an origin or to a previous position.” (Source: Wikipedia). dynamic-contrast-enhanced-MR-dataset [DEF] A DYNAMIC CONTRAST-ENHANCED MR DATASET reflects the dynamics of exogenous extracellular contrast media. dataset:field-map-dataset field-map-dataset dataset:floating-dataset floating-dataset [DEF] A DATASET is used as a FLOATING DATASET in the context of a Registration Data Processing. During the registration, the FLOATING DATASET (also called source) is resampled in the sampling grid provided by the REFERENCE DATASET. dataset:fractional-anisotropy-dataset fractional-anisotropy-dataset [DEF] The fractional anisotropy is proportional to the square root of the variance of the Eigen values divided by the square root of the sum of the squares of the Eigen values. functional-dataset hemodynamic-dataset magnetic-field-quality-dataset [DEF] A MAGNETIC FIELD QUALITY DATASET is an MR DATASET acquired to measure the non-uniformity of the static magnetic field B0 and linearity of its gradients, or to measure the radiofrequency field inhomogeneities. Such DATASETS are used to create FIELD MAP DATASETS and VOXEL DISPLACEMENT MAPS (which are CALIBRATION DATASETS) that are used to correct the deformation induced by those inhomogeneities. dataset:magnetic-field-quality-dataset-long-echo-time magnetic-field-quality-dataset-long-echo-time dataset:magnetic-field-quality-dataset-short-echo-time magnetic-field-quality-dataset-short-echo-time dataset:magnetization-transfer-ratio-dataset magnetization-transfer-ratio-dataset [DEF & CIT] The Magnetization Transfer Ratio may be defined as MTR=100x(M0-Ms)/M0 pu, where M0 represents the signal measured in absence of saturation, and Ms the signal in the presence of saturation applied to the bound proton pool. It is expressed as percentage units (pu). A magnetization transfer ratio dataset “gives an indication of the quantity of bound protons present in tissue; it is reduced in many pathologies, particularly de-myelinisation, and provides different information from conventional parameters such as T2”. (Tofts, 2004). dataset:mean-diffusivity-dataset mean-diffusivity-dataset [DEF] The mean diffusivity is the average value of the diffusion coefficients at this voxel. This can be estimated by the average of the eigen values of the diffusion tensor. mesh-dataset metabolic-dataset dataset:metabolite-concentration-ratio-dataset metabolite-concentration-ratio-dataset multiple-subjects-dataset [DEF] A MULTIPLE SUBJECTS DATASET is a dataset that does not refer to a single subject. For example, datasets that result from the statistical analysis of many datasets referring to multiple subjects are MULTIPLE SUBJECTS DATASETS. Such datasets may refer to multiple subjects, or may refer to a GROUP OF SUBJECTS. multiple-subjects-template-dataset dataset:non-reconstructed-dataset non-reconstructed-dataset [DEF] A NON-RECONSTRUCTED DATASET provides measurements that do not relate to a precise 3D location, such as measurements in Fourier space, or integration along a projection line (e.g. projection image in SPECT or CT). In contrast, a RECONTRUCTED DATASET provides a measurement of a quality of the explored object (e.g. brain tissues) at well-identified locations in 3D space, usually organized as a 2D and 3D regular sampling grid. nuclear-medicine-projection-dataset nuclear-medicine-tomo-dataset dataset:parameter-quantification-dataset parameter-quantification-dataset [DEF] This class of PARAMETER QUANTIFICATION DATASETS subsumes all the RECONSTRUCTED DATASETS that measure clinically relevant biological quantities of the tissues, such as T1 value, regional cerebral blood volume, fractional anisotropy, etc. They generally result of post-processing of acquired images, based on some estimation model. The values represented in those datasets are considered as measures because they are supposed to be “reproducible when measured again, and also comparable with measurements made by others in others locations” (Tofts, 2004). dataset:proton-density-weighted-MR-dataset proton-density-weighted-MR-dataset [DEF] All images have intensity proportional to proton density. Images with very little T1 or T2 weighting are called ‘PD-weighted’. proton-density-weighted-MR-template-dataset dataset:quality-procedure-dataset quality-procedure-dataset [DEF] A QUALITY PROCEDURE DATASET is a DATASET which was acquired to assess the quality of the data produced by a piece of imaging equipment. It may also be used to produce a CALIBRATION DATASET. For example, a phantom may be scanned to measure spatial scaling. dataset:quantitative-T1-dataset quantitative-T1-dataset [DEF] T1, also called longitudinal relaxation time (in milliseconds), is an intrinsic biophysical property of the tissue, related to macromolecule concentration, water binding and water content. dataset:quantitative-T2-dataset quantitative-T2-dataset [DEF] T2, also called transverse relaxation time (in milliseconds), is an intrinsic biophysical property of the tissues, also related to the bulk water content of the tissue. dataset:quantitative-T2-star-dataset quantitative-T2-star-dataset dataset:reconstructed-dataset reconstructed-dataset [DEF] A RECONTRUCTED DATASET provides a measurement of a quality of the explored object (e.g. brain tissues) at well-identified locations in 3D space, usually organized as a 2D and 3D regular sampling grid. In contrast, a NON RECONSTRUCTED DATASET provides measurements that do not relate to a precise 3D location, such as measurements in Fourier space, or integration along a projection line (e.g. projection image in SPECT or CT). dataset:reference-dataset reference-dataset [DEF] A DATASET is used as a REFERENCE DATASET in the context of a Registration Data Processing. The REFERENCE DATASET (also called target) provides the sampling grid according to which the FLOATING DATASET will be resampled. dataset:regional-cerebral-blood-flow-dataset regional-cerebral-blood-flow-dataset [DEF] Cerebral blood flow (represented in milliliters of blood per gram of tissue per minute) can be calculated from diverse measurements, such as a T2 or T2 STAR WEIGHTED DCE MR DATASET, or a SPIN TAGGING PERFUSION MR DATASET. dataset:regional-cerebral-blood-volume-dataset regional-cerebral-blood-volume-dataset [DEF] Cerebral blood volume (represented in milliliters of blood per gram of tissue) can be calculated from diverse measurements, such as a T2 or T2 STAR WEIGHTED DCE MR DATASET. dataset:regional-mean-transit-time-dataset regional-mean-transit-time-dataset [DEF] The regional mean transit time, rMTT, is the average time it takes the tracer substance to pass through the vascular bed within the voxel (rMTT= rCBV/rCBF). dataset:registration-dataset registration-dataset dataset:relative-anisotropy-dataset relative-anisotropy-dataset dataset:segmentation-dataset segmentation-dataset 1 single-subject-dataset [DEF] A SINGLE SUBJECT DATASET refers to exactly one subject, which means that it provides information about this particular subject. Such datasets may provide information concerning the subject’s anatomy, physiology or metabolism, or other kinds of information, such as geometrical transformations that enable to register the subject’s specific anatomy to an anatomical reference (provided by a template). single-subject-template-dataset spin-tagging-perfusion-MR-dataset [DEF] Spin tagging is a technique of measurement of blood perfusion, based on magnetically labeled arterial blood water as an endogenous tracer. SPIN TAGGING PERFUSION MR allows to obtain absolute values of perfusion of brain tissue by blood (cerebral blood flow) in ml blood/g tissue/ min. statistical-dataset template-dataset time-of-flight-angio-MR-dataset [DEF] Time-of-flight (TOF) is based on the phenomenon of flow-related enhancement of spins entering into an imaging slice. As a result of being unsaturated, these spins give more signal that surrounding stationary spins. With 2-D TOF, multiple thin imaging slices are acquired with a flow-compensated gradient-echo sequence. These images can be combined by using a technique of reconstruction such as maximum intensity projection (MIP), to obtain a 3-D image of the vessels analogous to conventional angiography. With 3-D TOF, a volume of images is obtained simultaneously by phase-encoding in the slice-select direction. velocity-encoded-angio-MR-dataset [DEF] Spins that are moving in the same direction as a magnetic field gradient develop a phase shift that is proportional to the velocity of the spins. This is the basis of phase-contrast angiography. Phase-contrast pulse sequence is used to encode the velocity of the spins. Stationary spins undergo no net change in phase after the two gradients are applied. Moving spins will experience a different magnitude of the second gradient compared to the first, because of its different spatial position. This results in a net phase shift. This information can be used directly to determine the velocity of the spins. Alternatively, the image can be subtracted from one acquired without the velocity encoding gradients to obtain an angiogram. dataset:voxel-displacement-map-dataset voxel-displacement-map-dataset dolce-particular:abstract Abstract, AB Meta-properties ABSTRACT is RIGID (+R). ABSTRACT is NOT CARRYING AN IDENTITY CRITERION (-I). ABSTRACT is NOT CARRYING A COMMON UNITY CRITERION (-U). ABSTRACT is NON-EXTERNALLY-DEPENDENT (-D). ABSTRACT is NONEMPTY. Properties [EP/SL] An ABSTRACT is a dolce-particular. [Ad3a’; EP/VR] An ABSTRACT has for parts only ABSTRACTS. [Ad3b; EP/VR] An ABSTRACT is a part of only ABSTRACTS. [Ad5a; EP/NMC] Every ABSTRACT is part of itself. [Ad8a’; EP/NMC] Every ABSTRACT1 which is not a part of an ABSTRACT2 is such that at least one ABSTRACT3 exists which is a part of ABSTRACT1 and which does not overlap with ABSTRACT2. Comment [CIT] [Masolo et al., 2003, p. 10] “Abstracts possess no causal power while concretes do.” [CIT] [Masolo et al., 2003, p. 18] “The main characteristic of abstract entities is that they do not have spatial nor temporal qualities, and they are not qualities themselves.” 1 dolce-particular:abstract-quality dolce-particular:abstract-region dolce-particular:accomplishment dolce-particular:achievement dolce-particular:amount-of-matter 1 dolce-particular:arbitrary-sum dolce-particular:atom dolce-particular:dependent-place 1 1 dolce-particular:endurant dolce-particular:eternal-region dolce-particular:event 1 dolce-particular:feature dolce-particular:mental-object dolce-particular:non-physical-endurant dolce-particular:non-physical-object dolce-particular:particular 1 1 dolce-particular:perdurant 1 1 dolce-particular:physical-endurant dolce-particular:physical-object 1 1 dolce-particular:physical-quality dolce-particular:physical-region dolce-particular:process dolce-particular:quale 1 1 dolce-particular:quality dolce-particular:region dolce-particular:relevant-part dolce-particular:social-object dolce-particular:space-region dolce-particular:spatial-location dolce-particular:state dolce-particular:stative dolce-particular:temporal-location 1 1 dolce-particular:temporal-quality dolce-particular:temporal-region dolce-particular:temporary-region dolce-particular:time-interval CT-image-storage-SOP-class [DEF] The CT IMAGE STORAGE SOP CLASS is a SOP Class intended for representing a CT image. 1 DICOM-SOP-class [DEF] A DICOM SOP class, or “service-object pair” class, is a functional unit of DICOM which combines a service class and an information object. DICOM-SOP-class-expression DICOM-expression DICOM-file DICOM-format [CIT] Article “DICOM” of Wikipedia: Digital Imaging and Communications in Medicine (DICOM) is a standard for handling, storing, printing, and transmitting information in medical imaging. It includes a file format definition and a network communications protocol. DIV] There is one current version of the DICOM FORMAT, DICOM-3, which we name CURRENT DICOM FORMAT. This version is modelled as an instance of the class DICOM FORMAT. DICOM-identifier 1 1 GIS-expression GIS-format [DEF] GIS is an image format introduced in France in the 90s in the context of the Groupe d’Intérêt Scientifique “Sciences de la Cognition”. [DIV] As there are no different versions for this format, the only known version is named CURRENT GIS FORMAT and is modelled as an instance of the class GIS format. GIS-header-file [DIV] Such files have for extension ‘.dim’. 1 INRIMAGE-expression INRIMAGE-file INRIMAGE-format MR-image-storage-SOP-class [DEF] The MR IMAGE STORAGE SOP CLASS is a SOP Class intended for representing a MR image. MR-spectroscopy-storage-SOP-class [DEF] The MR SPECTROSCOPY STORAGE SOP CLASS is a SOP Class intended for representing multi-frame MR Spectroscopy data. NIFTI-header-file [DIV] Such files have for extension ‘.hdr’. NII-file [DIV] Such files realize a whole NIfTI expression and have for extension ‘.nii’. NIfTI-expression NIfTI-format [DEF] The NIfTI image format was proposed in the context of the Neuroimaging Informatics Technology Initiative. This format is a variant of the ANALYZE FORMAT. [DIV] The current version (July 2006) is NIfTI-1. There is a new version in development, NIfTI-2. These versions are modelled as instances of the class NIFTI FORMAT. NM-image-storage-SOP-class [DEF] The NM IMAGE STORAGE SOP CLASS is a SOP Class intended for representing a NM multi-frame image. Both projection images and reconstructed images may be represented. PET-image-storage-SOP-class [DEF] The PET IMAGE STORAGE SOP CLASS is a SOP Class intended for representing a MR image. analyze-7.5-expression analyze-SPM-expression analyze-expression analyze-format [DEF] The ANALYZE FORMAT is an image format developed by the Mayo Clinic and used in their image processing Package Analyze. [DIV] There are two versions of the ANALAYZE FORMAT: the basic version, ANALYZE 7.5, and the extended version used notably by the SPM software which we name ANALYZE-SPM. These two versions are modelled as instances of the class ANALYZE FORMAT. analyze-header-file [DIV] Such files have for extension ‘.hdr’. deformable-spatial-registration-storage-SOP-class [DEF] A DEFORMABLE SPATIAL REGISTRATION STORAGE SOP CLASS is a SOP Class intended for representing a deformable registration grid, together with pre- and postregistration matrices to be applied prior and after the deformation. enhanced-CT-image-storage-SOP-class [DEF] The ENHANCED CT IMAGE STORAGE SOP CLASS is a SOP Class intended for representing a multi-frame CT image. enhanced-MR-image-storage-SOP-class [DEF] The ENHANCED MR IMAGE STORAGE SOP CLASS is a SOP Class intended for representing a multi-frame MR image. enhanced-PET-image-storage-SOP-class [DEF] The ENHANCED PET IMAGE STORAGE SOP CLASS is a SOP Class intended for representing a multi-frame MR image. medical-dataset-expression:medical-image-expression medical-image-expression segmentation-storage-SOP-class [DEF] A SEGMENTATION STORAGE SOP CLASS is a SOP Class intended for representing a segmentation result, i.e. a classification (binary or fractional) of pixels in one or several referenced images. It is a multi-frame image. spatial-registration-storage-SOP-class [DEF] A SPATIAL REGISTRATION STORAGE SOP CLASS is a SOP Class intended for representing a registration matrix. absolute-metabolite-concentration-estimation [DEF] Based on a spectrum measured in a single voxel or set of voxels, the absolute concentration of metabolite is estimated from the peak at the corresponding Lamor frequency (f0= γB0/2π) where g the gyromagnetic ratio depends of the considered metabolite (11.27 MHz/T for Na, 11.25 Mhz/T for P, and 40.08 fro F for B0=1T). The absolute metabolite concentration ratio is estimated by a specific fitting of the spectrum data relative to the considered metabolite. A calibration or the use of a model is required to access to the absolute value of the metabolite concentration. 2 absolute-proton-density-estimation [DEF] The specific procedure that leads to the quantitative estimation of the proton density defined tissue or structure. affine-non-rigid-registration [DEF] An AFFINE NON-RIGID REGISTRATION is an affine registration that estimates an affine but non-rigid geometrical transformation. affine-registration [DEF] An AFFINE REGISTRATION is a registration that estimates an affine geometrical transformation, usually represented by a 4 x 4 matrix. 1 bias-correction [DEF] A BIAS-CORRECTION is a RESTORATION that focuses on the compensation of bias. 1 bias-field-estimation [DEF] The measured magnetic resonance image is usually degraded by a bias field or intensity inhomogeneity (nonuniformity), which is induced primarily by the sensitivity profile of the radio frequency coil. The bias field is characterized by multiplicative smooth spatial variations that modulate the intensity of the true image data. (…) Because independent measurement of the bias field is very difficult and time-consuming, most of the reported correction techniques are postprocessing or retrospective methods, in which the bias field is estimated from the image itself after acquisition. Source: [Ji et al., 2007]. The estimation of a bias field may concern any kind of MR data. blood-oxygen-level-dependent-signal-changes-estimation [DEF] BLOOD OXYGEN LEVEL DEPENDENT SIGNAL CHANGES ESTIMATION is a method for determining which areas of brain are active under varying neurological stimulus conditions based on the magnetic property of the de-oxygenation of blood haemoglobin. The concentration variations of deoxyhaemoglobin due to neuron activation locally modifies the magnetic field. boundary-surface-and-region-based-segmentation [DEF] Boundary surface and region based segmentations are segmentations that involve a fusion of region- and boundary / surface based techniques, such as level sets fused with Bayesian classifications. boundary-surface-based-segmentation [DEF] Boundary based segmentations are segmentations that segment the image/volume based on edge or surfaces algorithms (Suri et al., 2002a,b). Such contours or surfaces may, correspond to sulci, cortex ribbon etc... brain-segmentation [DEF] A brain segmentation (also called skull-stripping, or brain extraction) is a segmentation that separates the brain from the other tissues such as skin, fat, bone etc. 1 calibration-model-application [DEF] A Calibration model application is the final stage of a calibration process, by which non-calibrated data are transformed into calibrated data by means of a calibration model, usually derived from calibration measurements obtained, e.g. from a phantom. 1 calibration-model-estimation [DEF] “Calibration is measuring the response of the instrument to a stimulus of known value, with the purpose of then being able to apply that knowledge to in vivo measurements.” (Tofts, 2004). Calibration measurements may be carried out in phantoms and in subjects. Calibration can be performed by measuring the response of the instrument in a specific situation (e.g. "resting state") without a specific stimulation as input. closing [DEF] A CLOSING is A MATHEMATICAL MORPHOLOGY FILTERING, in which the closing of A by B is obtained by the dilation of A by B, followed by erosion of the resulting structure by B. (Source: Wikipedia). coefficient-of-variation-calculation [DEF] A COEFFICIENT OF VARIATION CALCULATION is a STATISTICAL ANALYSIS in which the values taken by the mathematical function associated to the output dataset are calculated as the coefficient of variation of the values taken by the functions associated to the input dataset(s). The input datasets (“to be averaged”) must be associated with mathematical functions sharing the same domain and range. The COEFFICIENT OF VARIATION (CV) is a normalized measure of dispersion. It is defined as the ratio of the standard deviation to the mean. convolution [DEF] A CONVOLUTION is a FILTERING in which the “Filtered dataset” is obtained by convolution of the “dataset to be filtered” by a convolution kernel. cropping [DEF] A CROPPING is a resampling that affects only the domain of the mathematical function associated to the dataset “to be cropped”, by defining a subset of this domain, more precisely by reducing the number of samples and changing the upper and lower limit of one or more intervals of this domain. The values taken by the function associated to the dataset are not changed. dataset-arithmetical-operation [DEF] A DATASET ARITHMETICAL OPERATION is a DATASET PROCESSING is which the values taken by the mathematical function associated to the output dataset are calculated using an arithmetic operation applied to the values taken by the functions associated to the input dataset(s), e.g. sum, subtraction, etc. When the arithmetic operation has several operands, the datasets associated with each operand must be associated with mathematical functions sharing the same domain. 1 1 dataset-processing dataset-transformation [DEF] A DATASET TRANSFORMATION is a DATASET PROCESSING that creates a “transformed dataset” from a “to be transformed dataset” (e.g. a Fourier Transform). The mathematical function associated to the “transformed dataset” is derived from the mathematical function associated to the “to be transformed dataset”: especially, its domain intervals are defined in the transformed space, i.e. the frequency domain. “In mathematics, transform theory is the study of transforms. The essence of transform theory is that by a suitable choice of basis for a vector space a problem may be simplified — or diagonalized as in spectral theory”. (Source: Wikipedia). datasets-addition [DEF] A DATASETS ADDITION is a DATASET ARITHMETICAL OPERATION in which the arithmetic operation to be applied to the input datasets is an addition. datasets-blending [DEF] A DATASETS BLENDING is a DATASET ARITHMETICAL OPERATION in which the arithmetic operation to be applied to the input datasets is a blending operation (usually a linear combination of the input operands). datasets-division [DEF] A DATASETS DIVISION is a DATASET ARITHMETICAL OPERATION in which the arithmetic operation to be applied to the input datasets (namely a “DIVIDEND DATASET”, and a “DIVISOR DATASET”), is a division. datasets-logical-operation [DEF] A DATASETS LOGICAL OPERATION is a DATASET ARITHMETICAL OPERATION in which the arithmetic operation to be applied to the input datasets is a logical operation. datasets-multiplication [DEF] A DATASETS MULTIPLICATION is a DATASET ARITHMETICAL OPERATION in which the arithmetic operation to be applied to the input datasets is a multiplication. datasets-subtraction [DEF] A DATASETS SUBTRACTION is a DATASET ARITHMETICAL OPERATION in which the arithmetic operation to be applied to the input datasets (namely a “A DATASET”, and a “B DATASET TO BE SUBTRACTED FROM A”), is a subtraction. de-noising [DEF] A DENOISING is a RESTORATION that focuses on the compensation of noise. diffusion-tensor-calculation [DEF] A DIFFUSION TENSOR is calculated from a nondiffusion-weighted image plus six or more diffusion-weighted measurements along noncollinear directions. (Tofts, 2004) dilation [DEF] Dilation, in general, causes objects to dilate or grow in size. The amount and the way that they grow depend upon the choice of the structuring element. The operation of dilation is based on the maximum value among the neighboring pixels. distance-transform-processing [DEF] A DISTANCE TRANSFORM PROCESSING is a MATHEMATICAL MORPHOLOGY FILTERING transforming a binary image into a non-binary image that “highlights at each pixel of the image the distance to the nearest obstacle pixel. A most common type “obstacle pixel” is a boundary pixel in a binary image”. (Source: Wikipedia). distortion-correction [DEF] A DISTORTION-CORRECTION is a RESTORATION that focuses on the compensation of a distortion, based on a calibration model. erosion [DEF] Erosion, in general, causes objects to shrink. The amount and the way that they shrink depend upon the choice of the structuring element. Erosion is defined as a minimum operator, which assigns to every image pixel a minimum value from among their neighbors. The neighborhood is defined in mathematical morphology using a structuring element. 1 1 field-map-estimation [DEF] Variations in the main magnetic field B0, due to inhomogeneity, the presence of a body in the magnetic field etc…, provoke corresponding proton frequency variations (following the dependence of the proton precession frequency and the strength of the magnetic field, f0= γB0/2π where γ the gyromagnetic ratio egal 42.58 Mhz/T for H at B0=1T). These frequency variations can be estimated via a specific calibration procedure (including the measurement of two long and short time images), the field map estimation. Knowing the variations, corresponding spatial displacement i.e spatial image distortion can be corrected. filtering [DEF] A FILTERING is a DATASET PROCESSING applied to a “dataset to be filtered” and resulting in a “filtered dataset”. The transformation applied may be linear or non-linear. The domain of the mathematical functions associated to the “dataset to be filtered” and “filtered dataset” coincide. fourier-transformation [DEF] A FOURIER TRANSFORMATION is a DATASET TRANSFORMATION based on the Fourier Transform. fractional-anisotropy-calculation [DEF] FA = SQRT ( (λ1 – MD)2 + (λ2 – MD)2 + (λ3 – MD)2 ) / SQRT (λ12 + λ22 + λ32) With MD: Mean diffusivity λ1, λ2, λ3 are the eigenvalues of the diffusion tensor (Tofts, 2004) high-pass-filtering [DEF] A HIGH-PASS FILTERING is a CONVOLUTION “that passes high-frequency signals but attenuates (reduces the amplitude of) signals with frequencies lower than the cutoff frequency”. (Source: Wikipedia). intensity-normalization [DEF] An INTENSITY MODIFICATION is a resampling that affects only the range of the values taken by the mathematical function associated to the dataset “to be intensity-normalized”. [EX] Examples are: Bias field correction (with respect to an estimated bias); Histogram equalization; Intensity normalization (with respect to a reference); Image contrast adjustment (window / level); Image inversion. lesion-segmentation [DEF] A lesion segmentation is a segmentation that extracts structures such as tumours, or multiple sclerosis white matter lesions. low-pass-filtering [DEF] A LOW-PASS FILTERING is a DATASET CONVOLUTION “that passes low-frequency signals but attenuates (reduces the amplitude of) signals with frequencies higher than the cutoff frequency”. (Source: Wikipedia). magnetization-transfer-ratio-calculation [DEF & CIT] “The Magnetization Transfer Ratio may be defined as MTR=100x(M0-Ms)/M0 pu, where M0 represents the signal measured in absence of saturation, and Ms the signal in the presence of saturation applied to the bound proton pool. It is expressed as percentage units (pu). “ (Tofts, 2004). mathematical-morphology-filtering [DEF] Morphological image processing is a collection of techniques for digital image processing based on mathematical morphology. Since these techniques rely only on the relative ordering of pixel values, not on their numerical values, they are especially suited to the processing of binary images and greyscale images whose light transfer function is not known. (Source: Wikipedia). mean-calculation [DEF] A MEAN CALCULATION is a STATISTICAL ANALYSIS in which the values taken by the mathematical function associated to the output dataset are calculated using an arithmetic mean of the values taken by the functions associated to the input dataset(s). The input datasets (“to be averaged”) must be associated with mathematical functions sharing the same domain and range. [DIV] MEAN CALCULATION is used for building templates for inter-subject registration, i.e. datasets used to perform spatial normalization (alignment with respect to a common spatial reference, provided by a specific subject) by averaging image data obtained (using the same kind of imaging equipment) from a population of subjects. (Source: [Temal et al., 2006]). mean-diffusivity-calculation [DEF] “The most robust estimate of the diffusion properties of a voxel is given by the average of the eigenvectors of the Diffusion Tensor”. MD = (λ1 + λ2+ λ3) / 3 (Tofts, 2004) mesh-generation [DEF] A MESH GENERATION IS A DATASET PROCESSING that transforms a list of points in 2D or 3D space into a MESH, e.g. a list of polygons (resp. tetrahedrons) modelling the contour, or the surface, or the volume of the objects represented by these points. The input list of points may be specified as a particular ROI of a SEGMENTATION DATASET. metabolite-concentration-ratio-estimation [DEF] Based on a spectrum measured in a single voxel or set of voxels, the absolute concentration of metabolite is estimated from the peak at the corresponding Lamor frequency (f0= γB0/2π) where g the gyromagnetic ratio depends of the considered metabolite (11.27 MHz/T for Na, 11.25 Mhz/T for P, and 40.08 fro F for B0=1T). The metabolite concentration ratio is estimated by a specific fitting of the spectrum data relative to the considered metabolites (lactate, choline ect …). mono-modality-affine-non-rigid-registration [DEF] A MONO MODALITY AFFINE NON-RIGID REGISTRATION is an affine registration which estimates an affine but non-rigid geometrical transformation, in which the reference and the floating image have the same modality, which means that they represent the same kind of information, e.g. a T1-weighted MR signal. mono-modality-non-affine-registration [DEF] A MONO MODALITY NON-AFFINE REGISTRATION is a non-affine registration, in which the reference and the floating image have the same modality, which means that they represent the same kind of information, e.g. a T1-weighted MR signal. mono-modality-rigid-registration [DEF] A mono modality rigid registration is a rigid registration in which the reference and the floating image have the same modality, which means that they represent the same kind of information, e.g. a T1-weighted MR signal. multi-modality-affine-non-rigid-registration [DEF] A MULTI MODALITY AFFINE NON-RIGID REGISTRATION is an affine registration which estimates an affine but non-rigid geometrical transformation, in which the reference and the floating image represent different kinds of information, e.g. a T1-weighted MR signal and a T2-weighted MR signal, or a T1-weighted MR signal and a FDG tracer concentration (PET information). multi-modality-non-affine-registration [DEF] A MULTI MODALITY NON-AFFINE REGISTRATION is a non-affine registration, in which the reference and the floating image represent different kinds of information, e.g. a T1-weighted MR signal and a T2-weighted MR signal, or a T1-weighted MR signal and a FDG tracer concentration (PET information). multi-modality-rigid-coregistration [DEF] A multi modality rigid registration is a rigid registration in which the reference and the floating image have different modalities, which means that they represent different kinds of information, e.g. a T1-weighted MR signal and a T2-weighted MR signal, or a T1-weighted MR signal and a FDG tracer concentration (PET information). non-affine-registration [DEF] A NON-AFFINE REGISTRATION is a registration that estimates a non-affine geometrical transformation, usually represented by a 3D displacement field. normalization [DEF] A NORMALIZATION is a registration in which the reference and the floating image correspond, either to two different subjects, or to a subject and a template. opening [DEF] An OPENING is A MATHEMATICAL MORPHOLOGY FILTERING, in which “the opening of A by B is obtained by the erosion of A by B, followed by dilation of the resulting image by B”. (Source: Wikipedia). permutation [DEF] A PERMUTATION is a RESAMPLING that affects only the domain of the mathematical function associated to the dataset “to be re-permutated”. This permutation consists in applying a re-ordering of the samples along a specific interval, e.g.: x1, x2, …, x255 being re-ordered as x’1, x’2,…, x’255 based on the transformation x’1 = 256-x1. 2 quantitative-T1-estimation [DEF] Several methods can be used for the quantitative estimation of T1 in a defined tissue or structure. The most common method is based of the inversion of the MR signal (using a 180° pulse) and the acquisition of several T1-weighted images at different echo time during the recovery of the longitudinal magnetic component. 2 quantitative-T2-estimation [DEF] Several methods can be used for the quantitative estimation of T2 in a defined tissue or structure. based on the use of a spin echo sequence and the acquisition of several T2-weighted images at different echo time during the recovery of the transverse magnetic component. 2 quantitative-T2-star-estimation [DEF] Several methods can be used for the quantitative estimation of T2star in a defined tissue or structure. The most common method is based on the use of a gradient echo sequence and the acquisition of several T2star-weighted images at different echo time during the recovery of the transverse magnetic component. quantitative-parameter-estimation [DEF] Quantitative parameter estimation is the procedure that leads to the quantitative estimation of a specific physiological parameter. re-orientation [DEF] A RE-ORIENTATION is a resampling that affects only the domain of the mathematical function associated to the dataset “to be re-oriented”. This re-orientation can be specified in a 4x4 matrix. 1 1 reconstruction [DEF] A RECONSTRUCTION is a dataset processing by which one or more images are generated, based on measurements acquired by imaging equipment. Numerous very different kinds of reconstruction exist, depending on the different imaging modalities: CT, MR, PET, SPECT, MEG etc. Some of the reconstruction problems are: reconstruction from projections (e.g. CT, PET, SPECT), reconstruction from k-space acquisitions (MR), reconstruction of sources in MEG/EEG, reconstruction from radiofrequency data in ultrasound. Reconstruction algorithms are included in data acquisition software. region-based-segmentation [DEF] Region based segmentations are segmentations that segment the image/volume into different regions/sub-volumes (Suri et al., 2002a,b). Such regions may, e.g., correspond to white matter, grey matter and cerebrospinal fluid. Expectation Maximization techniques are examples of such techniques. regional-cerebral-blood-flow-estimation [DEF] Regional cerebral blood flow is estimated from specific MR sequences using invasive method (a tracer plus a T2star sequence) or non-invasive method via a spin tagging sequence. The principle of this measurement depends of the method used. For instance, the invasive method currently used in clinic determines the blood flow from the estimation of the cerebral blood volume divided by the estimation of the mean transit time. Cerebral blood volume and mean transit time are obtained from the gamma-fit on the T2* signal as a function of time. regional-cerebral-blood-volume-estimation [DEF] Regional cerebral blood volume is estimated from specific MR sequences using invasive method (a tracer plus a T2star sequence). The cerebral blood volume corresponds to area under the curve of the signal evolution during the bolus of the contrast agent. 1 1 regional-mean-transit-time-calculation [DEF] The transit time is classically estimated from specific MR sequences using invasive method (a tracer plus a T2star sequence). The method currently used in clinic determines the mean transit time by measuring the FWHM of the gamma-variate fit on the T2star signal measured during the injection. 1 1 registration [DEF & CIT] “A REGISTRATION is the process of transforming the different sets of data into one coordinate system. REGISTRATION is necessary in order to be able to compare or integrate the data obtained from different measurements. Medical imaging registration (e.g. for data of the same patient taken at different points in time) often additionally involves elastic (or nonrigid) registration to cope with elastic deformations of the body parts imaged. Nonrigid registration of medical images can also be used to register a patient's data to an anatomical atlas, such as the Talairach atlas for neuroimaging”. (Source: Wikipedia). “There are two steps involved in registering a pair of images together. There is the registration itself, whereby the set of parameters describing a transformation is estimated. Then there is a transformation, where one of the images is transformed according to the estimated parameters”. (Tofts, 2004). registration-with-distortion-correction [DEF] A REGISTRATION WITH DISTORTION CORRECTION is a registration in which the reference or the floating image (or both) undergoes a distortion correction, prior to the registration itself (e.g. based on a voxel displacement map derived from a field map). relative-anisotropy-calculation [DEF] The relative anisotropy is a normalized standard deviation representing the ratio of the anisotropic part of the tensor to its isotropic part. resampling [DEF] A RESAMPLING is a dataset processing that creates a « resampled » dataset from a dataset « to be resampled », by changing the sampling grid, or the range of dataset values, or both. The characteristics of the domain of the mathematical function associated to the « resampled » dataset are derived from those of the mathematical function associated to the dataset « to be resampled ». The number of intervals and their semantics remain unchanged, while the sampling characteristics of one or several of these intervals may be changed. The values taken by the function at each point of the new domain is derived from the values taken by the function associated to the dataset to be resampled, using a transformation function (e.g. closest pixel/voxel, interpolation). Moreover an additional transformation of those values may be applied, such as a linear windowing. A RESAMPLING does not change the nature of the information represented in the image: e.g. a T1weighted MR Dataset remains a T1-weighted MR Dataset. resolution-modification [DEF] A RESOLUTION MODIFICATION is a resampling that affects only the domain of the mathematical function associated to the dataset whose resolution has to be changed. The new resolution is specified using specific parameters or using a reference dataset which will provide the required sampling characteristics. restoration [DEF] A RESTORATION is a DATASET PROCESSING that consists is generating a “restored dataset” from a “to be restored dataset”, by compensating defects which degrade an image. Degradation comes in many forms such as motion blur, noise, and other specific acquisition equipment-related defects. The domain and range of the mathematical functions associated to the “restored dataset” and “to be restored dataset” coincide. rigid-registration [DEF] A RIGID REGISTRATION estimates a particular kind of linear geometrical transformation, with 3 rotations, 3 translations only, usually represented by a 4 x 4 matrix. 1 segmentation [DEF & CIT] “Segmentation refers to the process of partitioning a digital image into multiple regions (sets of pixels). The goal of segmentation is to simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. Image segmentation is typically used to locate objects and boundaries (lines, curves, etc.) in images. The result of image segmentation is a set of regions that collectively cover the entire image, or a set of contours extracted from the image (see edge detection). Each of the pixels in a region are similar with respect to some characteristic or computed property, such as colour, intensity, or texture. Adjacent regions are significantly different with respect to the same characteristic(s)”. (Source: Wikipedia). [DIV] The segmentation dataset that is the result of a segmentation can be superimposed with the dataset « to be segmented ». If a segmentation has for data several datasets, then they must all be superimposed together. Segmentations can be categorised according to several semantic axis: the first focuses on the kind of approach being used, e.g. boundary-based or region-based; the second focuses on the anatomical structures being segmented, e.g. lesions, brain etc. A third axis will be added later, focusing on the dimensionality of the Segmentation dataset produced. skeletonizing [DEF] A SKELETONIZING is a MATHEMATICAL MORPHOLOGY FILTERING transforming a binary image into another binary image that highlights “the skeleton of the shape represented in the input binary image. The skeleton of a shape is a thin version of that shape that is equidistant to its boundaries”. (Source: Wikipedia). smoothing [DEF] In statistics and image processing, to smooth a data set is to create a function that attempts to capture important patterns in the data, while leaving out noise. (Source: Wikipedia). standard-deviation-calculation [DEF] A STANDARD DEVIATION CALCULATION is a STATISTICAL ANALYSIS in which the values taken by the mathematical function associated to the output dataset are calculated as the standard deviation of the values taken by the functions associated to the input dataset(s). The input datasets (“to be averaged”) must be associated with mathematical functions sharing the same domain and range. statistical-analysis [DEF] A STATISTICAL ANALYSIS consists on producing statistical information from one or several datasets obtained from one or more individuals (e.g. functional brain maps derived from MRI data). (Source: [Temal et al., 2006]). structured-mesh-generation [DEF] A STRUCTURED MESH GENERATION is a MESH GENERATION in which the resulting MESH is a structured mesh. subcortical-segmentation [DEF] A subcortical segmentation is a segmentation that extracts structures such as ventricles, corpus callosum, hippocampus and basal ganglia (thalamus, pallidum, caudate nucleus etc.) thickening [DEF] A Thickening is a MATHEMATICAL MORPHOLOGY FILTERING used to grow selected regions of foreground pixels in binary images. Thickening is normally only applied to binary images, and it produces another binary image as output. thinning [DEF] A THINNING is A MATHEMATICAL MORPHOLOGY FILTERING, which “transforms the input image into a simplified, but topologically equivalent image”. (Source: Wikipedia). thresholding [DEF] A THRESHOLDING consist on marking Individual pixels in a grey scale image as “object” pixels if their value is greater than some threshold value (assuming an object to be brighter than the background) and as “background” pixels otherwise. Typically, an object pixel is given a value of “1” while a background pixel is given a value of “0.” The key parameter in thresholding is obviously the choice of the threshold. (Source: Wikipedia). tissues-segmentation [DEF] A tissues segmentation is a segmentation that extracts the tissues such as grey matter, white matter, cerebro-spinal fluid. unstructured-mesh-generation [DEF] AN UNSTRUCTURED MESH GENERATION is a MESH GENERATION in which the resulting MESH is an unstructured mesh. 1 voxel-displacement-map-calculation [DEF] The principle of MR image acquisition is to encode the space by controlled frequency variations using the application of additional gradients of magnetic field. Then, not mastered variations in frequency due to B0 inhomogeneity lead to spatial distortions. These spatial distortions can be corrected if frequency variations are known. The voxel displacement calculation, to correct for spatial distortions, is based on the estimation of the field map. wavelet-transformation [DEF] A WAVELET TRANSFORMATION is a DATASET TRANSFORMATION based on the Wavelet Transform. acronym acronym acronyme action ontoneurolog-extension-of-dolce:action action action action-role action role rôle d'action addressee addressee destinataire affected affected affecté agent agent-role agent role rôle d'agent agentive ontoneurolog-extension-of-dolce:agentive agentif agentive agentive-physical-object agentive physical object objet physique agentif agentive-social-object agentive social object objet social agentif animal-construction animal construction construction animale announcement annonce announcement announcing annoncer announcing artefact ontoneurolog-extension-of-dolce:artefact artefact artefact article article article artificial-object artificial object objet artificiel asking asking demander author auteur author authorization 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concentration concentration concept concept concept conceptual-action ontoneurolog-extension-of-dolce:conceptual-action action conceptuelle conceptual action conceptualization conceptualisation conceptualization consequent consequent conséquence contributor contributeur contributor data data donnée date date date defining defining définir definition definition définition deliberate-action action délibérée deliberate action deliberate-producing deliberate producing production délibérée demand demand demande describing describing décrire description description description designational-object designational object object désignationnel determinant determinant déterminant discourse discours discourse discourse-act acte de discours discourse act distal-intention distal intention intention distale enunciatee enunciatee énonciataire enunciator enunciator énonciateur even-integer even integer nombre entier pair exacting exacting exiger exactingness exactingness exigence experiencer experiencer expérimentateur experimental-artefact artefact expérimental experimental artefact 1 expression expression expression formal-expression ontoneurolog-extension-of-dolce:formal-expression expression formelle formal language formal-language formal language langage formel function fonction function functional-copied-entity entité copiée fonctionnelle functional copied entity functional-entity entité fonctionnelle functional entity generic-concept concept générique generic concept gestual-discourse discours gestuel gestual discourse happening chose qui arrive happening happening-consequent conséquence d'une chose qui arrive happening consequent imaginary-number imaginary number nombre imaginaire individual-concept concept individuel individual concept information information information informing informer informing 1 1 inscription ontoneurolog-extension-of-dolce:inscription inscription inscription instrument instrument instrument instrument-role instrument role rôle d'instrument integer integer nombre entier intention intention intention intentional-action action intentionnelle intentional action intentional-movement intentional movement mouvement intentionnel irrational-number irrational number nombre irrationnel language langage language lexical-item lexical item élément lexical linguistic-expression expression linguistique linguistic expression logical-expression expression logique logical expression logical-language langage logique logical language logical-operator logical operator opérateur logique 1 manufactured-object manufactured object objet manufacturé manufacturer fabricant manufacturer manufacturing fabriquer manufacturing member member membre message message message model model modèle model-acronym acronyme d'un modèle model acronym model-name model name nom d'un modèle motor-intention intention motrice motor intention name ontoneurolog-extension-of-dolce:name name nom natural-language langage naturel natural language natural-object natural object objet naturel negative-integer negative integer nombre entier négatif negative-real-number negative real number nombre réel négatif non-agentive-physical-object non-agentive physical object objet physique non-agentif non-agentive-social-object non-agentive social object objet social non-agentif non-negative-integer nombre entier non-négatif non-negative integer non-negative-real-number nombre réel non-négatif non-negative real number non-physical-action action non-physique non-physical action non-physical-artefact artefact non-physique non-physical artefact non-targeted-object non-targeted object objet non-ciblé noun nom noun number nombre number odd-integer nombre entier impair odd integer order commande order ordering commander ordering owner owner propriétaire owning owning être propriétaire physical-action action physique physical action positive-integer nombre entier positif positive integer positive-real-number nombre réel positif positive real number prime-number nombre premier prime number ontoneurolog-extension-of-dolce:producing producing producing produire producing-consequent conséquence d'une production producing consequent ontoneurolog-extension-of-dolce:proposition proposition proposition proposition protocol protocol protocole proximal-intention intention proximale proximal intention publication publication publication publisher publisher éditeur publishing publier publishing rational-number nombre rationnel rational number real-number nombre réel real number rendering-account-of rendering account of rendre compte de report rapport report representational-object objet représentationnel representational object request request requête requesting requesting requêter result result résultat 1 ontoneurolog-extension-of-dolce:running-an-artefact running-an-artefact faire fonctionner un artefact running an artefact scalar-quale scalar quale valeur scalaire scientific-article article scientifique scientific article scientific-publication publication scientifique scientific publication scientific-publisher scientific publisher éditeur scientifique serial-number numéro de série serial number social-agent agent social social agent social-artefact artefact social social artefact society society société ontoneurolog-extension-of-dolce:subject subject subject sujet substrate substrat substrate successful-action action réussie successful action successfully-manufacturing fabriquer avec succès successfully manufacturing successfully-producing produire avec succès successfully producing supervisor superviseur supervisor support support support ontoneurolog-extension-of-dolce:symbolic-action symbolic-action action symbolique symbolic action technical-artefact artefact technique technical artefact technical-report rapport technique technical report term term terme text text texte textual-discourse discours textuel textual discourse theme theme thème tool outil tool unit-of-measure unit of measure unité de mesure version-number numéro de version version number volume volume volume owl:Thing Frederic Furst, Gilles Kassel, Pascal Lando, Anne Lapujade, bernard.gibaud@univ-rennes1.fr 2009-04-24 Farooq Ahmad, Michel Dojat, Bernard Gibaud, Gilles Kassel, Lynda Temal, bernard.gibaud@univ-rennes1.fr 2009-11-20 Gilles Kassel, bernard.gibaud@univ-rennes1.fr 2009-04-12 Michel Dojat, Bernard Gibaud, Gilles Kassel, Lynda Temal, bernard.gibaud@univ-rennes1.fr 2008-10-01 Farooq Ahmad, Michel Dojat, Bernard Gibaud, Gilles Kassel, Lynda Temal, bernard.gibaud@univ-rennes1.fr 2009-11-20 Sabine Bruaux, Jean-Yves Fortier, Frederic Furst, Gilles Kassel, Pascal Lando, Anne Lapujade, bernard.gibaud@univ-rennes1.fr 2010-07-01