• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
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  • 优先权
    • 专利摘要:
    The invention relates primarily to a method of processing a plurality of data sets (J1-Jn) for exploitation by a molecular imaging method, each data set (J1-Jn) being defined by a set of spatial positions (Xi, Yi) to each of which is associated a set of molecular information (S (Xi, Yi)), characterized in that said method comprises in particular the following steps: - for each data set (J1-Jn) , cutting out the set of molecular information associated with each position (Xi, Yi) into several pieces of molecular information (T1-Tm) containing a reduced set of molecular information, - inserting the sections (T1-Tm) obtained for each position (Xi, Yi) of each data set (J1-Jn) in a database (BDD), so that at each position (XI, Yi) of each data set (J1-Jn) is associated a set of indexed molecular information sections (T1-Tm), - selecti following a request relating to a molecular information of interest, the one or more sections (T1-Tm) of the positions for the plurality of data sets (T1-Tm) containing the requested molecular information, and within each section (T1-Tm), said molecular information. The invention also relates to the corresponding data server (10).
    公开号:FR3016461A1
    申请号:FR1450176
    申请日:2014-01-10
    公开日:2015-07-17
    发明作者:Jonathan Stauber；Fabien Pamelard
    申请人:Imabiotech；
    IPC主号:
    专利说明:

    [0001] FIELD OF THE INVENTION The present invention relates to a method for processing molecular imaging data based on the use of a high-level database structure. volumetry, as well as on the corresponding data server. Molecular imaging generally encompasses all imaging techniques that essentially allow the molecular functioning of organs and / or organisms to be observed ex vivo or in vivo by minimally invasive or minimally disturbing biological or biophysical systems. observed. BACKGROUND TECHNOLOGY Molecular imaging is booming in the evolution of the number of technologies including MSI for "Mass Spectrometry Imaging" in English, MRI for "Magnetic Resonance Imaging", or Raman spectrometry. The use of these technologies makes it possible to carry out bio-distribution studies of endogenous or exogenous targeted compounds such as pesticides, drugs, proteins or lipids in order to study their roles in biological systems. The applications of these technologies with the help of software also make it possible to search for potential bio-markers in an unsupervised manner. However, the increase in data to be analyzed and interpreted in molecular imaging is a limitation for synthetic analyzes on several data sets, each associated with an image. The data then becomes difficult to exploit for synthetic or statistically analyzable representations. Indeed, the high volume of data set information is often greater than the size of the RAM of conventional computers (as opposed to supercomputers). In mass spectrometry imaging for example, images of 50 000 positions no longer make it possible to store all the raw information in memory and therefore require calculations to reduce the impact in memory and to enable the exploitation of data. these images. The calculations reduce the data and therefore induce biases by not taking into account all available information. In addition, the current analysis tools do not present any means of standardization of several data sets between them. There are many storage formats related mainly to automakers of dataset acquisition automata. However, none of these formats are adapted and optimized to be interrogated in a fine and rapid manner. Some fields of molecular imaging have developed storage formats such as Analysis 7.5 found in MRI (Magnetic Resonance Imaging) and mass spectrometry imaging. In mass spectrometry imaging, there is also the iMZML format, which is not a storage system for manipulating imaging data.
    [0002] HDF5 (Hierarchical Data Format 5) -based formats based on a hierarchical data organization have recently been used in the storage of high volume mass spectrometry imaging data. These formats make it possible to build remote query interfaces via Internet browsers. They allow faster statistical calculations than with non-hierarchical files. However, these formats do not provide a comprehensive and searchable comparative analysis of data in studies of multiple data sets simultaneously. There is therefore a need for a method for analyzing, comparing, and querying multiple data sets relative to each other without loss of information to avoid introducing bias analysis. OBJECT OF THE INVENTION The present invention aims to respond effectively to this need by proposing a method of processing a plurality of data sets intended to be exploited by a molecular imaging method, each set of data being defined by a set spatial positions to each of which is associated a set of molecular information, characterized in that it comprises in particular the following steps: for each data set, cut the set of molecular information associated with each position into several sections of molecular information containing a reduced set of molecular information, - inserting the obtained sections for each position of each data set into a database, so that at each position of each data set is associated a set of sections of data. indexed molecular information, - select in the database, following a query relating to Molecular information of interest, the one or more sections of positions for the plurality of data sets containing the molecular information of interest, and selecting, within each section, said molecular information of interest. The invention thus makes it possible to substantially reduce the loading time of the data to be analyzed, insofar as it is possible to retrieve, by selection of sections, the useful information in the database. Due to the cutting into sections of the signal without reduction of the spectrum, the invention also makes it possible to work without reducing the information in the context of combined pharmacokinetic, pharmacodynamic and quantification studies of endogenous or exogenous molecules in the same analysis to observe as a whole physiological events such as screening methods (comparison of distribution of several drug candidates), methods of quantification, methods of studying receptor occupancy or tissue screening, metabolism study or 3D reconstruction. The invention also makes it possible to optimize the use of the memory necessary for the statistical processing and the representation of the molecular imaging data on several data sets. According to one implementation, said method comprises the step of displaying the molecular information of interest in the form of a set of data mappings each corresponding to a data set.
    [0003] According to one embodiment, the set of molecular information is a spectrum with at least two dimensions. According to one embodiment, said method comprises the following steps: - cutting the set of molecular information into molecular information sections according to at least one predetermined step, - inserting a reference axis establishing a correspondence between point indexes of the different sections and the corresponding molecular information, and - select the section containing the molecular information of interest according to the reference axis and the predetermined pitch. According to one implementation, before or after the insertion of the sections into the database, said method comprises at least one pre-processing step consisting of spectral alignment and / or subtraction of background noise and / or normalization intra dataset. According to one implementation, said method alternatively comprises the step of resizing the spatial positions of the datasets having different spatial sizes so as to align with the dataset having the finest spatial size.
    [0004] According to one implementation, said method further comprises the step of extracting a set of data, such as a maximum intensity, an average intensity, an area under a peak, a signal-to-noise ratio of each peak of interest of the set of molecular information. According to one implementation, the extraction is performed according to peak selection criteria defined by a quality criterion of a signal-to-noise ratio and / or a spectral resolution. According to one implementation, said method comprises the step of normalizing the sets of molecular information of the plurality of data sets with each other so as to be able to compare the different sets of data with each other.
    [0005] According to one embodiment, to carry out the normalization step, said method may comprise the following steps: - choose a molecule or several endogenous or exogenous reference molecules common to the set of data sets, - calculate, for each set of data, a normalization factor according to this or these reference molecules, and - correcting the molecular information of interest with this normalization factor to obtain a set of standardized data sets. According to one embodiment, the normalization factor is defined from at least one reference molecule present on a set of samples corresponding to the data sets or in a particular reference area of the samples corresponding to the data sets. According to one implementation, said method comprises the step of visualizing, comparing, and analyzing, in the form of a plurality of data mappings, each of which comes from a standardized data set, the molecular information of interest with a scale. 10 color common to all the maps. According to one implementation, the common color scale is based on the smallest and largest intensity of all data sets for the molecule of interest. According to one embodiment, said data sets are obtained by a mass spectrometry method. According to one embodiment, said data sets are obtained by a Positron Emission Tomography (PET) or Magnetic Resonance Imaging (MRI) imaging method. The invention also relates to a data server comprising a memory storing software instructions for implementing at least part of the steps of the method of processing a plurality of data sets according to the present invention. BRIEF DESCRIPTION OF THE FIGURES The invention will be better understood on reading the description which follows and on examining the accompanying figures. These figures are given for illustrative but not limiting of the invention. FIG. 1 schematically represents the various steps of a mass spectroscopy method for obtaining data sets exploited by the method according to the present invention; Fig. 2 is a block diagram of the integrated database management system for carrying out the data processing method according to the present invention; Figure 3 is a schematic representation of the step of obtaining the sections of the spectra associated with each position of a data set; Figure 4 shows the steps for establishing the correspondence between a molecular information and the corresponding spectrum section; FIG. 5 schematically shows the inter-image normalization steps making it possible to accurately compare several images with each other. 1 () Identical, similar or similar elements retain the same references from one figure to another. DESCRIPTION OF AN EXEMPLARY EMBODIMENT OF THE INVENTION The following example of implementation of the method according to the invention is described for a MALDI mass spectrometry method. Of course, one could substantially identically use another imaging device that MALDI, as for example the sources: SIMS, DESI, LAESI, DIOS, ICP, MALDI Microscope, SNOM, SMALDI, LA-ICP, ESI (extraction liquid on fabric), MILDI, JEDI, ELDI etc. The method is equally applicable to any other method for producing usable data by a molecular imaging method such as PET ("Positron Emission Tomography") or MRI (Magnetic Resonance Imaging). Figure 1 schematically shows the different steps implemented by a MALDI mass spectrometry method. A first step E1 consists in cutting, generally by cryo-section, a slice of fabric 1 which is then placed on a blade 2. Then, in a step E2, a thin uniform layer of ionizing matrix 3 is deposited on the wafer. 1. The acquisition step E3 consists in generating automated laser pulses 4 having a predefined size, for example of the order of 100 micrometers per acquisition zone, in order to ionize the molecules of the matrix 3. Thus ionized molecules are analyzed in a manner known per se by a mass spectrometer. We can refer to the document FR2973112 for more details on the implementation of the method. We then obtain a set of data Jk defined by a set of spatial positions (Xi, Yj) to each of which is associated a set of molecular information.
    [0006] The spatial positions are defined according to a reference reference frame of axis X and Y. The set of molecular information is formed in this case in particular by a spectrum S with two dimensions indicating an intensity according to a mass molecular. For example, the data set Jk obtained has 50000 intensities (ie 50000 points) per spectrum over 20000 spatial positions Xi, Yj. The method according to the invention is based on the processing of a plurality of data sets J1-Jn. As shown in FIG. 3, for each set of data Jk that can be represented in the form of an image, the molecular spectrum S (Xi, Yj) associated with each position (Xi, Yj) is divided into several sections. T1-Tm spectra. T1-Tm sections are cut at a predetermined pitch P '. For example, for each spectrum of 50000 points per spatial position, each spectrum may be cut in a pitch P 'of 10000 points. In other words, the spectrum S (Xi, Yj) is divided into five sections T1-T5 of 10000 points each. In a variant, the pitch P 'used may be variable.
    [0007] Also defined is a reference axis Aref, shown in Figure 4, establishing a correspondence between point indexes of different sections T1-Tm and the corresponding molecular weight. Preferably, before insertion into a database BDD, the method implements at least one step of pre-processing the spectra S (Xi, Yj) via a module 100. This pre-processing step may consist of a spectral alignment . For this purpose, reference peaks are considered and the spectrum is shifted so as to position the corresponding peak values on these reference peaks. It will also be possible to subtract the background noise by recalibrating the spectrum according to a difference between the minimum value of the spectrum and a reference value to which the minimum value of the spectrum should correspond. In addition, different types of signal normalization (internal standardization) can be realized (by an internal standard, by using the total intensities ...).
    [0008] In other words, an intra-data normalization is carried out here, that is to say a standardization for each data set independently of the other data sets, which corresponds to steps that are known to those skilled in the art. This intra-data set normalization is performed in addition to the subsequent standardization steps between the .11-In datasets detailed below (inter-data set normalization) which will enable accurate and reliable comparison of datasets. .11-In between them. Furthermore, the method comprises the step of extracting a set of data, such as a maximum intensity Pk1-Pk1, an average intensity, an area under a peak, a signal-to-noise ratio of each peak of interest of the spectra. S (Xi, Yj). It is thus possible to extract the maximum intensity of each peak Pk1-Pk1 spectrum S (Xi, Yj) according to a method called "PeakPicking" in English. The extraction is performed according to selection criteria of peaks defined by a quality criterion of the signal-to-noise ratio and / or the spectral resolution.
    [0009] The sections Tl-Tm obtained for each position (Xi, Yj) of each data set .11-In as well as the reference axis Aref and the previously extracted peaks Pkl-PK1 are inserted in the database BDD as is shown in FIG. 2. Thus, at each position (Xi, Yj) of each data set .11-In are associated a set of indexed sections T1-TM and a set of peaks Pk1-PK1 and of complementary data mentioned above (intensities averages, areas of each peak). The database BDD is a high volume database of the type NoSQL (for "Not only Structured Query Language" in English) or SQL for visualization, management, interpretation and statistical analysis of the inserted data. For example, the database management system may be selected from the following non-exhaustive systems: Hypertable, Haddoop, Cassandra, MongoDB, PolyBase, Hadoop on Azure, Hive, Pig. The database system ensures the consistency, reliability, and relevance of the data contained. Replication and backup techniques can help ensure the reliability of the information.
    [0010] The steps for selecting the relevant information in the database BDD are described below. Following a request for a molecular information of interest, the one or more sections of the positions of the .11-In dataset sets containing the requested molecular information are selected. For this purpose, for each position Xi, Yj of each data set J1-Jn, the section Tp containing the requested molecular information is selected according to the reference axis Aref and the requested molecular information. Thus, in FIG. 4, a section of a five-step pitch spectrum P 'is illustrated to facilitate the understanding of this step. As is often the case for spectra from a mass spectrometry method, the molecular weight scale is not linear. In the case where the user requires information relating to a molecule having, for example, a molecular weight of 3 m / z, the reference axis Aref makes it possible to establish that the index of the point corresponding to this molecular weight value is the 9, so that it can be deduced using the pitch P 'that the section Tp containing the intensity of the molecular weight of interest is the second section T2. Alternatively, a table can be used which stores the minimum and maximum molecular weight limits and the indexing of the points of each corresponding T1-Tm section. In this case, for requested molecular information, the identifier of the T1-Tm section is selected as a function of these minimum and maximum limits. Within each T1-Tm section selected for each of the points of the set of data sets J1-Jn, the corresponding intensity of the requested molecular weight is then selected. It then becomes possible to visualize, in the form of a set of C1-CN data mappings each corresponding to a set of data J1-Jn, the distribution of the intensity of presence of the molecule of interest on the different maps C1. -CN. A color is assigned based on the intensity of the molecular information on a color scale. Alternatively, or in a complementary manner, extraction of the Pk1-PK1 peaks makes it possible to rapidly produce images providing a first analysis of the distribution of the intensity of a molecule of interest. Preferably, the method comprises the step of normalizing the molecular information of the data sets J1-Jn in order to be able to compare several data sets with each other (cf module 101). For this purpose, we choose a reference molecule Mref common to all J1-Jn data sets. Alternatively, several endogenous or exogenous molecules that are common to all of the J1-Jn datasets are selected. It is possible to take into account the presence of the reference molecule (s) Mref in the complete samples or more specifically in a reference zone of the corresponding samples. More precisely, for each data set J1-Jn, a normalization factor Fnor is calculated as a function of this or these reference molecules Mref. The molecular information of interest is then corrected with this normalization factor Fnor so as to obtain a set of standardized data sets J1-Jn. For example, for three data sets corresponding to the C1-C3 maps shown in FIG. 5, a reference molecule Mref = 500m / z having a reference intensity of value 10 is chosen in a reference zone Z. This molecule Mref has respectively a value of 5, 10 and 15 in the zone Z of the maps C1-C3. A normalization factor Fnor equal to the ratio between the reference value and the current value is then defined, ie a normalization factor Fnor equaling respectively 2, 1, and 10/15 for the maps C1, C2 and C3. This coefficient is then applied to all the intensity values of the spectra associated with the different positions of the set of data sets J1-Jn and in particular to the intensity values of a molecule of interest Mi = 300m / z at the position Xi; Yj. Normalization thus makes it possible to correct the intensities related to changes in experimental conditions due to the preparation of the sample and its analysis. Indeed, in the preceding example, it is noted that it would have been possible to arrive at an erroneous conclusion of a stronger presence of the molecule of interest Mi = 300 m / z in the sample corresponding to the C3 map. , whereas after application of the normalization factor Fnor, the study shows a homogeneous presence of the molecule of interest Mi in the three samples with an intensity of 200 at the position Xi; Yj. Following this standardization, it is then possible to visualize, compare and analyze, in the form of data maps C1'-C3 'the molecular information with a color scale common to all C1'-C3' maps (cf. module 102). The scale obtained is based on the smallest and largest intensity determined on all data sets for the molecule of interest Mi.
    [0011] Of course, it is possible to produce more than three data sets and corresponding mappings to analyze several J1-Jn data sets in a cross-functional fashion without having to load them all into memory. The imaging data can then be compared to highlight potential markers. It is also possible to create groupings of positions with a common behavior in order to allow a new interpretation of the data. In other words, the invention thus makes it possible, in a single tool, to compare and interrogate the data in order to obtain more relevant, standardized information whose quality is quantifiable. Several datasets can be loaded with quality controls inserted into each image, allowing data to be standardized with each other. In addition, the method may optionally make it possible to resize the data set positions J1 and J2 of the same biological sample having different spatial sizes so as to align with the dataset having the finest spatial size. It is then possible to combine the processing of a mapping from the mass spectrometer (MALDI imaging) with the treatment of a mapping resulting from a PET (Positron Emission Tomography) or MRI (Magnetic Resonance Imaging) type system. ) or MRI (Magnetic Resonance Imaging). It is also possible to represent several J1-Jn datasets in codistribution in a single image. It is then possible to carry out requests of the type "select all the values v, w of all the positions x and y (with the same spatial size) for the field z and a of the current cartography". The z and a fields represent the values of two different imaging technologies. It is also possible to perform temporary statistical processing that can be added by insertion requests in the database BDD. Thus, the method according to the invention provides great flexibility insofar as it is possible to add information on each position of the image at any time of the analysis method. The method is based on a client / server architecture. The database BDD is searchable by a single interface. The data is stored and interrogated in a single or clustered server architecture accessible via local or via a network (internal or external). The client software interface can be installed on the workstation or in the web interface version. The invention also relates to the data server 10 comprising a memory storing software instructions for implementing at least part of the steps of the method of processing a plurality of data sets J1-Jn. In addition, the amount of data processed can grow easily, so that the volume of the data to be analyzed is no longer a limitation to the analysis. The insertion of the data is efficient whatever the size of the database BDD.
    [0012] Of course, the foregoing description has been given by way of example only and does not limit the scope of the invention of which one would not go out by replacing the details of execution by any other equivalents.
    权利要求:
    Claims (16)
    [0001]
    REVENDICATIONS1. A method of processing a plurality of data sets (J1-Jn) to be exploited by a molecular imaging method, each data set (J1-Jn) being defined by a set of spatial positions (Xi, Yi) to each of which is associated a set of molecular information (S (Xi, Yi)), characterized in that it comprises in particular the following steps: - for each data set (J1-Jn), cut out the set of molecular information associated with each position (Xi, Yi) in several pieces of molecular information (T1-Tm) containing a reduced set of molecular information, - inserting the sections (T1-Tm) obtained for each position (Xi, Yi) of each data set (J1-Jn) in a database (BDD), so that at each position (XI, Yi) of each data set (J1-Jn) is associated a set of information pieces molecular (T1-Tm) indexed, - select in the database (BDD), following a re quest relating to a molecular information of interest, the one or more sections (T1-Tm) of the positions for the plurality of data sets (T1-Tm) containing the molecular information of interest, and - selecting, within each section (T1-Tm), said molecular information of interest.
    [0002]
    2. Method according to claim 1, characterized in that it comprises the step of displaying the molecular information of interest in the form of a set of data mappings (C1-Cn) each corresponding to a data set ( J1- Jn).
    [0003]
    3. Method according to claim 1 or 2, characterized in that the set 25 of molecular information is a spectrum (S (Xi, Yi)) of at least two dimensions.
    [0004]
    4. Method according to any one of claims 1 to 3, characterized in that it comprises the following steps: - cutting the set of molecular information (S (Xi, Yj)) into sections 30 of molecular information ( T1-Tm) following at least one predetermined step (P '), - inserting a reference axis (Aref) establishing a correspondence between point indexes of the different sections (T1-Tm) and the corresponding molecular information, and - selecting the section (T1-Tm) containing the molecular information of interest according to the reference axis (Aref) and the predetermined pitch (P ').
    [0005]
    5. Method according to any one of the preceding claims taken in dependence on claim 2, characterized in that, before or after the insertion of the sections (T1-TM) in the database (BDD), said method comprises least one pretreatment step consisting of spectral alignment and / or background noise subtraction and / or intra-data normalization (J1-Jn).
    [0006]
    6. Method according to any one of claims 1 to 5, characterized in that it comprises the subsidiary step of resizing the spatial positions (Xi, Yi) data sets (J1-Jn) having different spatial sizes of to align with the dataset (J1-Jn) with the finest spatial size.
    [0007]
    7. Method according to any one of claims 1 to 6, characterized in that it further comprises the step of extracting a set of data, such as a maximum intensity (Pkl-Pkl), a mean intensity, an area under a peak, a signal-to-noise ratio of each peak of interest of the set of molecular information.
    [0008]
    8. Method according to claim 7, characterized in that the extraction is performed according to peak selection criteria defined by a quality criterion of a signal-to-noise ratio and / or a spectral resolution.
    [0009]
    9. Method according to any one of claims 1 to 8, characterized in that it comprises the step of normalizing the sets of molecular information of the plurality of data sets (J1-Jn) between them so as to be able to compare the different datasets (J1-Jn) between them.
    [0010]
    10. Method according to claim 9, characterized in that to carry out the normalization step, said method comprises the following steps: - choose a molecule or several endogenous or exogenous reference molecules (Mref) common to all the sets of data (J1-Jn), - calculate, for each data set (J1-Jn), a normalization factor (Fnor) as a function of this or these reference molecules (Mref), and- correct the molecular information of interest with this normalization factor to obtain a set of standardized data sets (J1-Jn).
    [0011]
    11. The method of claim 10, characterized in that the normalization factor (Fnor) is defined from at least one reference molecule (Mref) present on a set of samples corresponding to the data sets (J1-Jn). ) or in a particular reference area (Z) of the samples corresponding to the data sets (J1-Jn).
    [0012]
    12. Method according to any one of claims 9 to 11, characterized in that it comprises the step of visualizing, comparing, and analyzing, in the form of a plurality of data mappings (C1'-Cn ') from each of a standardized data set (J1-Jn), the molecular information of interest with a color scale common to all mappings.
    [0013]
    Method according to claim 12, characterized in that the common color scale is based on the smallest and largest intensity of all data sets (J1-Jn) for the molecule of interest.
    [0014]
    14. Method according to any one of claims 1 to 13, characterized in that said data sets (J1-Jn) are obtained by a mass spectrometry method.
    [0015]
    15. Method according to any one of claims 1 to 13, characterized in that said data sets (J1-Jn) are obtained by an imaging method of Positron Emission Tomography (PET) or Imaging by Magnetic Resonance (MRI).
    [0016]
    A data server (10) having a memory storing software instructions for implementing at least a portion of the steps of the method of processing a plurality of data sets (J1-Jn) defined according to one of any of the preceding claims.
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    同族专利:
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    法律状态:
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    优先权:
    申请号 | 申请日 | 专利标题
    FR1450176A|FR3016461B1|2014-01-10|2014-01-10|METHOD FOR PROCESSING MOLECULAR IMAGING DATA AND CORRESPONDING DATA SERVER|FR1450176A| FR3016461B1|2014-01-10|2014-01-10|METHOD FOR PROCESSING MOLECULAR IMAGING DATA AND CORRESPONDING DATA SERVER|
    PCT/FR2015/050051| WO2015104512A1|2014-01-10|2015-01-09|Method for processing molecular imaging data and corresponding data server|
    CN201580004212.XA| CN105940402B|2014-01-10|2015-01-09|For handle molecular imaging data method and corresponding data server|
    EP15701558.7A| EP3092589A1|2014-01-10|2015-01-09|Method for processing molecular imaging data and corresponding data server|
    JP2016545874A| JP6630275B2|2014-01-10|2015-01-09|Method for processing molecular imaging data and corresponding data server|
    US15/110,430| US10013598B2|2014-01-10|2015-01-09|Method for processing molecular imaging data and corresponding data server|
    CA2935777A| CA2935777A1|2014-01-10|2015-01-09|Method for processing molecular imaging data and corresponding data server|
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