Abstract:
A system ( 10 ) and method ( 20 ) for planning a neurosurgical operation are provided the system ( 10 ) comprises an input ( 11 ) for receiving functional data ( 25 ) and anatomical data of a brain region ( 31 ), and a processor ( 12 ) configured to perform the method ( 20 ) according to the invention. The method ( 20 ) comprises analyzing ( 26 ) the functional data ( 25 ) to form a network representation ( 27 ) of functional connections, mapping the network representation ( 27 ) of the functional connections and the anatomical data to a common coordinate system, determining an expected function loss associated with a simulated removal of network nodes ( 32 ) or network connections ( 42 ) from the network representation ( 27 ), and identifying critical network connections and/or critical network nodes based on the expected function loss.

Description:
FIELD OF THE INVENTION 
       [0001]    This invention relates to a system for planning a neurosurgical operation, the system comprising an input for receiving anatomical data and functional data of a brain region, a processor for mapping the anatomical data and the functional data to a common coordinate system and identifying critical network connections and/or critical network nodes, and an output for providing a visual representation of the anatomical data and the functional. This invention further relates to a method and a computer program product for planning a neurosurgical operation. 
       BACKGROUND OF THE INVENTION 
       [0002]    Brain surgery commonly involves the removal or destruction of suspect regions. For example, for brain tumors surgical resection is often the first treatment of choice. For drug resistant epilepsy, removal or destruction of the epileptic focus by surgical means is an important treatment. Brain surgery does not always involve open surgery, but is nowadays also performed in non-invasive ways of utilizing, e.g., stereotactic radiosurgery. During brain surgery, it is very important not to damage critical structures and connections in the brain. Damaging brain tissue during an operation usually leads to function loss afterwards. The brain has some ability to recover when damaged, but this ability is limited Minimizing damage made during surgical intervention or radiotherapy is achieved by carefully planning the surgery using pre-operative medical images like MRI. 
         [0003]    Often intra-operative visualization (microscope, ultrasound, MR, X-ray) is used to provide imaging information during the procedure. Diffusion-tensor MRI (DTI) based tractography can be considered as a very advanced anatomic imaging modality that visualizes the important white-matter tracts in the brain that connect different brain regions. The registration of all these data to a common framework and the combined visualization of these data provide surgeons with a detailed anatomical map of the brain that supports them in determining an optimal surgical plan. 
         [0004]    Today, most advanced brain-surgery planning systems take into account both anatomical image information (contrast-enhanced MRI, CT, etc., that high-light specific brain anatomy) as well as functional image information (fMRI, PET, MEG, EEG, etc., that high-light specific brain function). Functional brain imaging is aimed at characterizing the brain in terms of its physiology, functional architecture and dynamics. Functional imaging can be based on analysis of data acquired using brain imaging modalities such as electro-encephalography (EEG), magneto-encephalography (MEG), functional magnetic resonance imaging (fMRI), or optical techniques such as near-infrared spectroscopy (NIRS). 
         [0005]    A neurosurgical planning system visualizing both functional and anatomical data is known from the US patent application published as US 2005/0273001 A1. The system described in that application uses fMRI images, DTI images and perfusion images of the brain and selectively displays them on top of one another. All displayed images are aligned with an anatomical image of the brain. The transparency of each layer can be adjusted. Although such a system has some advantages over systems using only anatomical imaging, there is a need for a smarter and more sophisticated combination of anatomical and functional data in order to allow the surgeon to make a better informed decision and to increase the ability to minimize function loss after the operation. 
         [0006]    It is an object of the invention to provide a system and method for planning a neurosurgical operation, which system and method improve the ability to avoid function loss after the operation. 
       SUMMARY OF THE INVENTION 
       [0007]    According to a first aspect of the invention, this object is achieved by providing a system for planning a neurosurgical operation, the system comprising an input, a processor and an output. The input is provided for receiving functional data and anatomical data of a brain region. The processor is configured to analyze the functional data to form a network representation of functional connections, the network representation comprising network nodes and network connections interconnecting the network nodes, to map the network representation of the functional connections and the anatomical data to a common coordinate system, to determine an expected function loss associated with a simulated removal of one of the network nodes or network connections from the network representation, and to identify critical network connections and/or critical network nodes based on the expected function loss. The output is provided for providing a visual representation of the network representation of the functional connections. 
         [0008]    This neurosurgical planning system takes into account the functional network properties of the brain in relation to their anatomical substrate. It is an essential feature of brain function that it relies on its network properties, i.e. function is not localized to individual units but arises also from network mediated interactions between units. Recent analysis techniques of functional imaging data are therefore aimed at not only quantifying activity in individual units (e.g. EEG channels or fMRI voxels) but also at quantifying the interactions and correlations between units being representative of the underlying network. 
         [0009]    The functional data which, e.g., may be fMRI, EEG, MEG and/or NIRS data is analyzed to form a network representation of functional connections in the brain. The functional data shows relations between mental or physical activities of the patient and neural activity in different parts of the patient&#39;s brain. Repeated, simultaneous or correlated activity in different brain regions indicates a functional connection between said regions. By analyzing the functional data, it is possible to identify functional connections and to make a network representation of said connections. The network representation comprises network connections and network nodes. The resulting network can be analyzed geometrically providing e.g. insight in properties like critical nodes (&#39;hubs&#39;) having high connectivity to other network parts. The network representation and the anatomical data are mapped to a common coordinate system, making it possible to link removal or damage of a specific brain region to changes in the network representation of the functional connections. Expected function loss following removal or damage of a specific brain area can then be determined using network theory and network analysis techniques. As a result of the network analysis, critical network connections and network nodes are identified and may be avoided in the upcoming neurosurgical operation. Also less critical network connections and network nodes may be identified. When planning a neurosurgical operation it may be decided to apply a more aggressive treatment to the less critical regions. 
         [0010]    By, e.g., identifying network connections and/or network nodes critical to neurological recovery, specifically these connections can be spared (conservative treatment) thereby optimizing the potential for post-surgical neurological recovery. Further, by identifying network connections and/or network nodes less critical for recovery (i.e. easily ‘rewirable’), more aggressive treatment in those areas can be performed. This planning system will allow for an effective treatment strategy that spares as much as possible the brain&#39;s intrinsic ability to recover, learn, and rewire functionality. 
         [0011]    The analysis of the received functional data for making the network representation of the functional connections may comprise calculating a pair wise correlation for a plurality of data channels comprised in the functional data. 
         [0012]    The expected function loss may, e.g., be determined using a topological network analysis of the network representation. This topological network analysis may, e.g., comprise calculating clustering coefficients of the network nodes and/or path lengths of the network connections. 
         [0013]    In a preferred embodiment, the system further comprises planning tools for planning a surgical trajectory avoiding the identified critical network connections and/or critical network nodes. 
         [0014]    The planning system preferably comprises an output for providing a visual representation of the network representation of the functional connections. The critical network connections and/or the critical network nodes may be highlighted in the visual presentation. Additionally, an appearance of the network connections and of the network nodes in the visual representation may depend on the expected function loss. The visual representation preferably also shows the anatomic data. 
         [0015]    According to a second aspect of the invention, a method is provided comprising receiving functional data and anatomical data of a brain region, analyzing the functional data to form a network representation of functional connections, the network representation comprising network nodes and network connections interconnecting the network nodes, mapping the network representation of the functional connections and the anatomical data to a common coordinate system, determining an expected function loss associated with a simulated removal of one of the network nodes or network connections from the network representation, and based on the expected function loss, identifying critical network connections and/or critical network nodes. 
         [0016]    According to a third aspect of the invention, a computer program product is provided for planning a neurosurgical operation. The program is operative to cause a processor to perform the method as described above. 
         [0017]    These and other aspects of the invention are apparent from and will be elucidated with reference to the embodiments described hereinafter. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0018]    In the drawings: 
           [0019]      FIG. 1  schematically shows a neurosurgical operation planning system according to the invention, 
           [0020]      FIG. 2  schematically shows a method for planning a neurosurgical operation, 
           [0021]      FIG. 3  schematically shows a distribution of functional sensors over a skull of a person to undergo neurosurgical surgery, 
           [0022]      FIG. 4  visualizes a network analysis of functional data obtained by the sensors of  FIG. 3 , 
           [0023]      FIG. 5  visualizes a removal of a network site from the network of  FIG. 4 , 
           [0024]      FIG. 6  visualizes a removal of a network connection from the network of  FIG. 4 , and 
           [0025]      FIG. 7  shows an exemplary combination of anatomical and functional data according to the invention. 
       
    
    
     DETAILED DESCRIPTION OF THE EMBODIMENTS 
       [0026]      FIG. 1  schematically shows a neurosurgical operation planning system  10  according to the invention. The system  10  comprises a processor  12  for performing the planning method according to the invention. An input  11  of the processor  12  is coupled to a source of functional data  16  and a source of anatomical data  17 . The source of functional data  17  may, e.g., be an fMRI, EEG, MEG and/or NIRS data source. Any type of data linking neuronal activity to brain functions or showing correlation between neuronal activity in different parts of the nervous system may serve as input for the operation planning method according to the invention. Alternatively, earlier obtained functional data may be stored on a storage means  13  coupled to the processor  12 . The storage means  13  may, e.g., be a hard disk, solid state memory or optical storage medium. In addition to functional data, the processor  12  also receives anatomical data. The anatomical data may come from anatomical imaging apparatuses  17 , such as an MRI or DTI apparatus. Any type of data describing the physical structure of the imaged brain sections may serve as input for the operation planning method according to the invention. The storage medium  13  may comprise earlier obtained anatomical data to be used in the planning method. 
         [0027]    The processor  12  combines the functional data and the anatomical data received at the input  11 . The combined information is used in a simulation for estimating the effects of possible surgical operations. The results of this simulation and/or estimation may be shown on a display  14  coupled to the processor  12 . A keyboard, mouse  15  or other pointing device is provided for allowing user input for initiating or controlling the claimed method and/or for manipulating the output shown on the display screen  14 . In the following, it will be described how the functional and anatomical data are combined to provide information about a planned neurosurgical operation and how the results of the planning method may be displayed. Additionally, an exemplary way of providing the functional data needed for the method according to the invention is disclosed. 
         [0028]      FIG. 2  schematically shows an exemplary method  20  for planning a neurosurgical operation. The method  20  starts with obtaining or receiving functional data. In this example, the functional data comprises at least two channels  25  corresponding to activity in different areas of a human or animal nervous system. The functional data may, e.g., be derived from functional brain imaging data such as fMRI images. Alternatively EEG, MEG and/or NIRS data sources are used for obtaining the functional data. In correlation step  21 , a coherence or correlation analysis may be used for determining functional connectivity of different brain areas. The correlation analysis  21  may e.g. calculate a correlation of time-courses of measured functional MEG data channels  25 . Correlation coefficients may be organized in a correlation matrix  26 , such that at position (i, j) in the matrix  26  the correlation coefficient for the channels  25  i and j is provided. Repeating the correlation calculation for all pairs of data channels  25  results in a complete matrix  26 . Instead of correlation coefficients, the correlation matrix  26  may comprise binary values indicating ‘correlation’ or ‘no correlation’. 
         [0029]    In network generating step  22 , a network  27  of functional connectivity is derived from the calculated correlations. The network  27  comprises network sites  32  representing the respective data channels. Each network site  32  may, e.g., correspond to a position of an EEG sensor or an area of function related neuronal activity in an imaged brain. Correlating network sites  32  are connected by network connections  41 . The network  27  thus shows which brain regions are functionally connected to which other brain regions. From the network  27  shown in  FIG. 2  it is clear that some network sites  32  are functionally connected to only one other network site  32  and some are connected to more network sites  32 . 
         [0030]    In network analysis step  23  a topological analysis of the functional connectivity network  27  is made. For this analysis, graph theoretic techniques may be used. For example, a clustering coefficient (connectivity density) or path length (distance measure between network sites  32 ) may be determined. Critical network sites  32  may be identified as sites  32  having a high clustering coefficient. Additionally, the effects of an intervention may be simulated and quantified in order to identify the relation between a simulated intervention (network modification) and the impact on the functioning of the network  27 . The purpose is to identify the more critical network sites  32  and connections  41  that should be spared in an intervention. 
         [0031]    In mapping step  24 , the functional network  27  is mapped to the patient&#39;s anatomy. For the mapping, the functional network  27  and the received anatomical data are mapped to a common coordinate system. Fiber pathways may be found using tractographic techniques. Network connections  41  may be mapped to the detected fiber tracts and network sites  32  to discovered neuronal structures. Combined images  29  may be generated showing the analyzed functional network  27  overlaid on the anatomical information. Color coding or other ways of highlighting may be used for indicating how critical a certain structure or connection is for the functioning of the brain. This will allow a surgeon to make an assessment of how planned interventions may have an impact on brain network functioning. 
         [0032]      FIG. 3  schematically shows a distribution of functional sensors  36  over a skull  31  of a person to undergo neurosurgical surgery. In this example, the functional data is obtained by  14  EEG sensors  36  distributed over the patient&#39;s skull  31 . The sensors  36  are arranged for registering neuronal activity in brain areas close to the sensors  36 . Each sensor  36  may provide a data channel  25  to be used as input for the method shown in  FIG. 2 . The data from the channels  25  represents the neuronal activity at neuronal network sites  32  at positions corresponding to the sensor  36  positions. Correlation coefficients indicating the correlation between two of the sensors may be used for making a 14 by 14 correlation matrix  26  as shown in  FIG. 2 . 
         [0033]      FIG. 4  visualizes the results of a network analysis of functional data obtained by the sensors  36  of  FIG. 3 . Correlating pairs of network sites  32  are indicated by drawing a connection  41 ,  42  between the functionally related network sites  32 . The more critical connections  42  are distinguished from the less critical connections  41  by using a thicker line to represent the connection  42 . Alternatively, color coding or other highlighting techniques may be used for indicating how critical the different connections  41 ,  42  are. Also for the network sites  32 ,  43  color coding or other types of highlighting may be used for indicating how critical the network sites  32 ,  43  are. 
         [0034]      FIG. 5  visualizes a damage or removal of a network site  54  from the network of  FIG. 4 . The removal is indicated by using a dotted circle representing the network site  54 , but may be indicated in different ways. As a result of this removal, other network sites may lose the connection to the functional network. This may cause temporary or permanent function loss. Temporary function loss may be compensated by the intrinsic capability of the brain to rewire damaged neuronal structures. 
         [0035]      FIG. 6  visualizes a removal of a network connection  65  from the network of  FIG. 4 . The removal is indicated by using a dotted line  65 , but may be indicated in different ways. As a result of this removal, two network sites  63  will lose the most important connection  65  to the functional network. This may cause temporary or permanent function loss. 
         [0036]      FIG. 7  shows an exemplary combination of anatomical and functional data according to the invention. The combined image  70  may be displayed on the display  14  of the neurosurgical planning system  10 . The combined image  70  comprises an anatomical image of a cross section of the patient&#39;s brain and an overlay of the functional network. The overlay shows network sites  71  and fiber tracts  72  connecting theses network sites  71 . More critical network sites are now drawn somewhat larger than the less important ones. 
         [0037]    It will be appreciated that the invention also extends to computer programs, particularly computer programs on or in a carrier, adapted for putting the invention into practice. The program may be in the form of source code, object code, a code intermediate source and object code such as partially compiled form, or in any other form suitable for use in the implementation of the method according to the invention. It will also be appreciated that such a program may have many different architectural designs. For example, a program code implementing the functionality of the method or system according to the invention may be subdivided into one or more subroutines. Many different ways to distribute the functionality among these subroutines will be apparent to the skilled person. The subroutines may be stored together in one executable file to form a self-contained program. Such an executable file may comprise computer executable instructions, for example processor instructions and/or interpreter instructions (e.g. Java interpreter instructions). Alternatively, one or more or all of the subroutines may be stored in at least one external library file and linked with a main program either statically or dynamically, e.g. at run-time. The main program contains at least one call to at least one of the subroutines. Also, the subroutines may comprise function calls to each other. An embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the processing steps of at least one of the methods set forth. These instructions may be subdivided into subroutines and/or be stored in one or more files that may be linked statically or dynamically. Another embodiment relating to a computer program product comprises computer executable instructions corresponding to each of the means of at least one of the systems and/or products set forth. These instructions may be subdivided into subroutines and/or be stored in one or more files that may be linked statically or dynamically. 
         [0038]    The carrier of a computer program may be any entity or device capable of carrying the program. For example, the carrier may include a storage medium, such as a ROM, for example a CD ROM or a semiconductor ROM, or a magnetic recording medium, for example a floppy disc or hard disk. Further the carrier may be a transmissible carrier such as an electrical or optical signal, which may be conveyed via electrical or optical cable or by radio or other means. When the program is embodied in such a signal, the carrier may be constituted by such cable or other device or means. Alternatively, the carrier may be an integrated circuit in which the program is embedded, the integrated circuit being adapted for performing, or for use in the performance of, the relevant method. 
         [0039]    It should be noted that the above-mentioned embodiments illustrate rather than limit the invention, and that those skilled in the art will be able to design many alternative embodiments without departing from the scope of the appended claims. In the claims, any reference signs placed between parentheses shall not be construed as limiting the claim. Use of the verb “comprise” and its conjugations does not exclude the presence of elements or steps other than those stated in a claim. The article “a” or “an” preceding an element does not exclude the presence of a plurality of such elements. The invention may be implemented by means of hardware comprising several distinct elements, and by means of a suitably programmed computer. In the device claim enumerating several means, several of these means may be embodied by one and the same item of hardware. The mere fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.