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WO2016193847A1 - Détermination de la classe histologique d'une tumeur à l'aide de cartes de coefficients de diffusion apparente obtenues à partir de données de résonance magnétique (rm) - Google Patents

Détermination de la classe histologique d'une tumeur à l'aide de cartes de coefficients de diffusion apparente obtenues à partir de données de résonance magnétique (rm) Download PDF

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Publication number
WO2016193847A1
WO2016193847A1 PCT/IB2016/052878 IB2016052878W WO2016193847A1 WO 2016193847 A1 WO2016193847 A1 WO 2016193847A1 IB 2016052878 W IB2016052878 W IB 2016052878W WO 2016193847 A1 WO2016193847 A1 WO 2016193847A1
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tumor
roi
map
computing
histogram
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PCT/IB2016/052878
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Lalit Gupta
Sundararaman VELANDAI KALYANARAMAN
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Koninklijke Philips N.V.
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0012—Biomedical image inspection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10072—Tomographic images
    • G06T2207/10088—Magnetic resonance imaging [MRI]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30004—Biomedical image processing
    • G06T2207/30096—Tumor; Lesion

Definitions

  • the following relates to the medical imaging arts, oncology arts, tumor assessment arts, and so forth.
  • Magnetic resonance (MR) imaging is a known technique for identifying and monitoring cancerous tumors.
  • MR imaging is non-invasive, or at most minimally invasive if a magnetic contrast agent is administered.
  • an MR image of a tumor is visually assessed by a radiologist or oncologist having the requisite expertise.
  • the MR imaging assessment can provide quantitative information on tumor size, location, and other anatomical characteristics.
  • Tumor (or cancer) staging may be performed using MR imaging, based on MR-observed anatomical features such as tumor location, tumor size, observed tumor spread to nearby tissues, the number of tumors present, and so forth.
  • tumor grade is an assessment of how well (or poorly) differentiated the tumor cells appear to be under the microscope.
  • Well-differentiated tumor tissue consists of cells that are visually close to those of normal tissue, and which are organized similarly to normal tissue.
  • poorly differentiated or undifferentiated tumor tissue consists of cells that deviate strongly from normal tissue cells, and have reduced or non-existent organization in the tumor tissue.
  • Tumor grade is useful because it has been generally observed that well-differentiated malignant tissue tends not to spread quickly; whereas, poorly differentiated (or undifferentiated) malignant tissue has a greater tendency to spread in the body and possibly metastasize.
  • a low grade tumor is well differentiated and tends to have a better prognosis; whereas, a high grade tumor is poorly differentiated or undifferentiated and tends to spread more rapidly, leading to a poorer prognosis.
  • Various cancer-type specific tumor histology grading schemes have been developed along these lines for cancers such as brain cancer, breast cancer, prostate cancer, and so forth. The tumor staging is distinct from the tumor grading.
  • the oncologist typically uses both imaging-based tumor staging and tumor histology grading, along with other information such as patient age, general health, demographic information, and so forth, in order to diagnose the cancer, assess patient prognosis and formulate and guide the oncology treatment. This requires that the patient undergo both MR imaging and a biopsy or other invasive procedure.
  • a tumor grading system comprises a computer programmed to grade a tumor by performing a tumor grading method on a region of interest (ROI) corresponding to the tumor in a magnetic resonance (MR) image.
  • the tumor grading method includes the operations of: generating a heterogeneity map corresponding to the ROI by computing a contrast measure for each pixel of the ROI; constructing a histogram of the computed contrast measures over the heterogeneity map; computing a kurtosis metric of the histogram; and grading the tumor based on the kurtosis metric.
  • the system may further comprise a user interface including at least one user input device and a display device, wherein the tumor grading method includes the further operation of receiving, via the user interface, identification of the ROI corresponding to the tumor in the MR image.
  • the system may further include an MR imaging system, wherein the tumor grading method includes the further operation of causing the MR imaging system to acquire the MR image as an apparent diffusion co-efficient (ADC) map.
  • ADC apparent diffusion co-efficient
  • a tumor grading method comprises: acquiring an apparent diffusion co-efficient (ADC) map using a magnetic resonance (MR) imaging system; receiving identification of a region of interest (ROI) in the ADC map corresponding to a tumor via a user interface device; and, using a computer: generating a heterogeneity map corresponding to the ROI by computing a contrast measure for each pixel of the ROI, constructing a histogram of the computed contrast measures over the heterogeneity map, computing a kurtosis metric of the histogram, and displaying, on the user interface, a tumor grade determined based on the kurtosis metric.
  • ADC apparent diffusion co-efficient
  • MR magnetic resonance
  • the heterogeneity map is generated by computing co-occurrence matrices with different offsets for sub-matrices defined in the ROI, and computing pixels of the heterogeneity map as contrast measures computed using the co-occurrence matrices.
  • the heterogeneity map is generated by computing texture features for sub-matrices defined in the ROI and computing pixels of the heterogeneity map as contrast measures computed using the texture features computed for the sub-matrices.
  • a non-transitory storage medium stores instructions readable and executable by a computer to perform a tumor grading method including the operations of: generating statistics on pixel gray level co-occurrences in a region of interest (ROI) corresponding to a tumor in an apparent diffusion co-efficient (ADC) map; and assigning a tumor grade to the tumor based on the generated statistics on pixel gray level co-occurrences in the ROI.
  • the operation of generating statistics on pixel gray level co-occurrences in the ROI includes generating a set of co-occurrence matrices with different offsets for each of a plurality of sub-matrices spanning the ROI.
  • the operation of assigning a tumor grade to the tumor based on the generated statistics on pixel gray level co-occurrences in the ROI includes generating a contrast map for the ROI based on the generated statistics on pixel gray level cooccurrences in the ROI, computing a kurtosis metric for a histogram of constrast values in the contrast map, and assigning the tumor grade to the tumor based on the computed kurtosis metric.
  • One advantage resides in providing non-invasive tumor grading using MR imaging that correlates well with conventional tumor histology grading.
  • Another advantage resides in providing MR imaging-based tumor grading without the administration of a magnetic contrast agent.
  • Another advantage resides in providing MR imaging-based tumor grading that is readily tuned for different cancer types by tuning the post-acquisition image processing.
  • FIGURE 1 diagrammatically shows magnetic resonance (MR) imaging-based tumor grading system.
  • FIGURE 2 diagrammatically illustrates certain data structures generated and used by the MR imaging-based tumor grading system of FIGURE 1.
  • FIGURE 3 upper plot shows a grading scale in which the tumor grade is monotonically non-decreasing with increasing value of the kurtosis metric; lower table presents the tumor grade definitions.
  • FIGURE 4 shows two examples of apparent diffusion co-efficient (ADC) maps of brain tumors and their corresponding heterogeneity/contrast maps generated by the MR imaging-based tumor grading system of FIGURE 1.
  • ADC apparent diffusion co-efficient
  • FIGURE 5 compares the output of the MR imaging-based tumor grading system of FIGURE 1 with three other tested MR-based tumor grading techniques as described herein, with tumor grades assigned by tumor histopathology indicated by circled numeral labels.
  • a tumor based on texture analysis of a magnetic resonance (MR) image of the tumor is performed on an MR image comprising an apparent diffusion co-efficient (ADC) map.
  • ADC map or image is a map of the magnitude of water diffusion in the tissue.
  • DWI diffusion weighted imaging
  • magnetic field gradient pulses are applied in a first direction to obtain sensitivity to diffusion along that direction, and this is repeated for at least two other directions (e.g. for three orthogonal directions total) to obtain the ADC map.
  • ADC map might be expected to be characteristic of these tissue features.
  • tumor grade is a metric of the tumor cellular structure/organization, it might thus be expected that an ADC map of the tumor contains information on the tumor grade.
  • heterogeneity or contrast
  • a combination of applying a heterogeneity (or contrast) analysis as disclosed herein to a brain tumor ADC map did provide a strong correlation with brain tumor grade. Without being limited to any particular theory of operation, it is believed that higher heterogeneity corresponds to higher degree of variability within the tumor. Multiple factors could contribute to this increased variability e.g. areas of different tumor grades, necrotic tissue, hemorrhage within the tumor, increased vascularity. Higher prevalence of these factors is associated with an aggressively growing tumor which is poorly differentiated or undifferentiated (i.e. of high tumor grade).
  • the heterogeneity or contrast map can be produced by generating statistics on pixel gray level co-occurrences in a region of interest (ROI) corresponding to the tumor in the ADC map. For example, co-occurrence matrices with different offsets can be computed for sub-matrices defined in the ROI, and pixels of the heterogeneity map are then computed as contrast measures for the co-occurrence matrices.
  • ROI region of interest
  • kurtosis is a measure of the extent to which a distribution is concentrated about its mean, that is, a measure of the extent to which a distribution is peaked.
  • Another way of viewing kurtosis is as a measure of the degree of curvature of the peak of the distribution, since a "more peaked" distribution has a higher curvature at its peak than a broader, less peaked distribution.
  • kurtosis metrics are known.
  • One kurtosis metric is the ratio of the fourth moment of the distribution ( ⁇ 4 ) to the square of the second moment (variance ⁇ ⁇ ) of the distribution, that is, this kurtosis metric is given as A variant of this metric is known as the excess kurtosis (or Pearson kurtosis), and is given by ⁇ 4
  • kurtosis metrics that are sometimes used include the fourth moment ⁇ 4 by itself, or an L-moment. These are illustrative examples: in general, a higher value of a kurtosis metric indicates a more peaked distribution (more heavily concentrated toward its mean) having a higher degree of curvature (that is, more sharply peaked).
  • An MR scanner 10 is controlled by an MR controller 12 to execute an apparent diffusion co-efficient (ADC) mapping pulse sequence 14 in order to acquire an ADC map 16 of a subject (e.g. a human oncology patient, or a dog, cat, or other veterinary oncology subject).
  • ADC apparent diffusion co-efficient
  • the illustrative MR scanner 10 is a Philips Achieva 1.5T MR scanner (available from Koninklijke Philips N.V., Eindhoven, the Netherlands); however, the MR scanner 10 can more generally be any other commercially available MR imaging system, or may be a custom-built MR scanner, that is capable of acquiring the ADC image or map 16. As is known in the art, the ADC image or map 16 is typically acquired without the use of any exogenous contrast agent being administered to the subject, as the ADC map 16 characterizes endogenous or intrinsic water diffusion.
  • the MR controller and other associated electronic data processing and/or control components are embodied in the illustrative embodiment by a computer 20, which includes a display device 22 (e.g. an LCD display device) and at least one user input device 24 (e.g. an illustrative keyboard, and/or a mouse, trackball, or other pointing device, and/or a touch- sensitive overlay of the display, or so forth).
  • the computer 20 provides user interfacing to enable a radiologist or other trained medical professional to operate the MR scanner 10 to execute the pulse sequence 14 to acquire the ADC map 16 (and, typically, also enables the radiologist to execute myriad other pulse sequences to acquire other types of MR images or data, e.g.
  • the illustrative computer 20 also provides a user interface 30, employing the display device 22 and the at least one user input device 24, via which a user can navigate through the ADC images 16 in order to select the slices that pass through a (suspected) malignant tumor of interest and to identify within each slice a region of interest (ROI) 32 corresponding to the tumor in the ADC image 16.
  • ROI region of interest
  • the disclosed image acquisition and processing operations may be physically embodied as a non-transitory storage medium that stores instructions readable and executable by a computer (e.g. computer 20) to perform a tumor grading method as disclosed herein.
  • the non-transitory storage medium may, for example, include one or more of a hard drive or other magnetic storage medium, an EPROM, flash memory, or other electronic storage medium, an optical disk or other optical storage medium, various combination(s) thereof, or so forth.
  • a stack of acquired ADC images 16 is displayed, and the user uses a mouse or other pointing device to select relevant slices, which is shown on the display in a separate window along with the ADC images 16.
  • the user preferably a radiologist, oncologist, or other medical professional trained to interpret MR images with respect to tumors or other oncological features
  • the mouse or other user input device is employed to draw a contour around the tumor in each chosen slice, thereby identifying the ROI 32 corresponding to the tumor.
  • automated or semi-automated segmentation and/or contouring may be provided as part of the user interface 30 to provide automated assistance in identifying the ROI 32 - for example, an edge detector-based contour delineation algorithm may be employed, in which the user defines an approximate encircling contour which is then automatically adjusted to align with a high intensity gradient corresponding to an "edge" in the image.
  • the illustrative computer 20 provides both MR control and image reconstruction functionality (that is, the computer 20 is programmed to implement the MR controller 12 to control the MR scanner 10 and to reconstruct acquired MR imaging data to generate the ADC image 16, or another chosen type of MR image) and post-acquisition image processing functionality such as the image navigation/ROI selection component 30 and subsequent image and data processing components such as described herein. It will be appreciated that the illustrative computer 20 may be replaced by two or more computers or other electronic data processing device(s).
  • the MR controller 12 is implemented as a control computer and a separate image reconstruction computer with high computing capacity, and the post-image acquisition processing is either also performed by the image reconstruction computer, or is peformed by a separate (third) computer dedicated to post-acquisition data processing tasks (for example, implemented as an image navigation workstation).
  • the input ADC maps 16 typically consists of stack of two-dimensional images, although acquisition of three-dimensional MR data is also contemplated, and the illustrative ROI 32 is a two-dimensional ROI defined in each slice. However, a three-dimensional ROI is also contemplated.
  • the ADC map 16 and the ROI 32 is optionally used for various operations in addition to the tumor grading process.
  • FIGURE 1 illustrates the tumor as represented by the ROI 32 and the surrounding anatomical context as represented by the ADC map 16 being used as input for staging of the tumor or cancer in a staging operation 34.
  • a tumor is staged based on its size, location respective to critical tissues, and other factors.
  • the overall cancer may be similarly staged based on the size/location of the principal tumor or tumors, along with other information such as the total number of tumors (obtainable from the ADC map 16 analyzed by the radiologist using the user interface 30), the patient's age, gender, general health, or other patient information, and so forth.
  • the illustrative staging process 34 uses the ADC image 16
  • the MR scanner 10 may execute another pulse sequence under control of the controller 12 in order to acquire another type of image (e.g. a proton image, Tl -weighted image, an image acquired using a contrast agent, or so forth) which is then used for staging the tumor and/or overall cancer.
  • a “co-occurrence" of a gray scale level refers to two pixels of an image (e.g. of ROI 32) separated by a chosen offset having designated gray scale levels.
  • the offset may be chosen as "immediately horizontally to the right"
  • the designated gray scale level may be "2" and "2" (the same in this case)
  • the number of co-occurrences of two pixels with gray scale level "2" immediately next to each other horizontally is then counted.
  • this may be repeated for each possible combination of gray scale level i and gray scale level j, thus producing a two-dimensional "co-occurrence matrix" having axes for i and j, respectively. If the gray scale levels range from 0, ... ,255 then the co-occurrence matrix would have dimensions 256x256. In practice, if the number of gray scale levels is large then the co-occurrence matrix may be sparse - in illustrative FIGURE 1 this sparseness is suppressed by reducing the number of gray scale levels via a re-scaling process 38. For example, by rescaling levels 0...7 as a first level, levels 8...
  • the number of gray levels is reduced from 256 to 8
  • the resulting co-occurrence matrix is of dimensions 8x8 with substantially reduced sparseness compared with the 256x256 co-occurrence matrix of the ROI without such gray level re-scaling.
  • a sub-matrix 40 also called a "kernel” or an “image kernel” herein
  • the kernel 40 is an ⁇ ⁇ ⁇ matrix centered on the ROI pixel P.
  • the co-occurrence matrix 42 has dimensions i, j each running over all possible gray scale levels (again, after the re-scaling 38 if such re-scaling is performed).
  • this processing (generating a plurality of 2D co-occurrence matrices at pixel P with different offsets d) is repeated for each pixel of the 2D ROI 32 to generate a representation 52 of the ROI in which each pixel is represented by a 3D co-occurrence matrix of the form of the 3D co-occurrence matrix 44 shown in FIGURE 2.
  • FIGURE 2 defines a sub-matrix at each pixel of the ROI 32
  • other approaches are contemplated for generating a set of co-occurrence matrices with different offsets for each of a plurality of sub-matrices spanning the ROI 32.
  • the N> ⁇ N kernels 40 defined around two immediately adjacent pixels of the ROI 32 will overlap.
  • the kernels are chosen to not overlap, but to span the ROI 32. For example, if the ROI is square with 2048x2048 pixels, then a kernel of dimensions 8x8 can be used, with the 2048x2048 pixel ROI divided into 256x256 non-overlapping kernels each of 8x8 pixels.
  • a 3D co-occurrence matrix analogous to the 3D co-occurrence matrix 44 can be computed for each of the 8x8 kernels as just described.
  • the resulting 2D ROI representation 52 would be made up of 256x256 "pixels" each represented by a 3D co-occurrence matrix analogous to the 3D co-occurrence matrix 44.
  • each "pixel" of the 2D ROI representation 52 is converted to a contrast or texture value using a suitable approach.
  • the contrast for each 3D co-occurrence matrix 44 is computed as:
  • indices i, V and j, j' index gray scale levels of the ROI, index k, k' indexes the co-occurrence matrices, and d k ⁇ d k ⁇ denotes the offset construct the k co-occurrence matrix.
  • the heterogeneity or contrast map 60 has the same number of pixels as the number of "pixels" of the 2D ROI representation 52 - this number of pixels may be the same as the number of pixels in the ROI 32 (as in the illustrative embodiment), or may be less than the number of pixels in the ROI 32 (as in the additional example in which a 2048x2048 ROI is processed in operation 50 using a 2D array of 8x8 non-overlapping kernels to generate the representation 52 as a 2D map of 256x256 "pixels").
  • texture features are computed for the image kernel, and a contrast measure for the kernel is computed based on the texture features for the image kernel.
  • texture features defined in terms of co-occurrence statistics (probabilities) are given in Haralick et al, "Textural Features for Image Classification", IEEE Trans. On Systems, Man and Cybernetics vol. SMC-3 No. 6 pages 610-21 (1973).
  • the contrast measure may be computed as a weighed combination of the texture features with higher contrast texture features being assigned relatively higher weights in the combination than lower contrast texture features.
  • 2D ROI 32 is processed to compute a grade for the tumor as follows.
  • a contrast histogram is computed for the contrast measures of the heterogeneity/contrast map 60.
  • Each contrast bin of the contrast histogram stores a count of the number of pixels of the heterogeneity/contrast map 60 having contrast measure values falling in that contrast bin.
  • the contrast bins of the contrast histogram are optionally normalized, e.g. by the total number of pixels in the heterogeneity/contrast map 60.
  • a kurtosis metric is computed for the distribution represented by the contrast histogram.
  • some suitable illustrative kurtosis metrics include: the excess kurtosis of the histogram; the ratio where ⁇ is the fourth moment of the histogram and ⁇ ⁇ is the variance of the histogram; the fourth moment ⁇ 4 of the histogram; or an L-moment of the histogram.
  • the kurtosis metric is a single scalar value representing the kurtosis of the distribution of contrast measures over the heterogeneity/contrast map 60 generated for the ROI 32. In general, a higher value of a kurtosis metric indicates a more peaked contrast histogram (more heavily concentrated toward its mean contrast value) having a higher degree of curvature (that is, more sharply peaked).
  • the correlation of the kurtosis metric computed in operation 64 with the tumor grade can be conceptually understood as follows. If the tumor is poorly differentiated or undifferentiated (that is, high tumor grade), then the contrast typically varies to a greater extent across the tumor (i.e. across the ROI 32). This leads to a contrast distribution (histogram) with one high peak (sharp spike) and small peaks around it, resulting in a high kurtosis value because of one high peak. On the other hand, if the tumor is well-differentiated (that is, low tumor grade), then the contrast should be roughly uniform due to the presence of well-defined tissue. This leads to a broader, rounder contrast distribution with no sharp peak, resulting in low kurtosis.
  • the x-axis of the contrast distribution histogram is normalized within a range.
  • the high grade tumor would yield one sharp peak and small peaks around it, however low grade tumor would yield one peak with very few/no small peaks around it. Therefore, in this implementation low grade tumor will have higher kurtosis and high grade tumor will show low kurtosis.
  • FIGURE 3 shows a grading scale in which the tumor grade is monotonically non-decreasing with increasing value of the kurtosis metric.
  • K Representing the value of the kurtosis metric as K, in this grading scheme the tumor grade (in a range 1-4) is assigned as follows:
  • T ⁇ , T 2 , T 3 are thresholds which are suitably determined by a calibration process in which a training set of oncology patients are both imaged to acquire ADC maps which are processed as described with reference to FIGURES 1 and 2 in order to generate kurtosis metric values for the patients, and also are biopsied to perform tumor histology grading to generate "ground truth" tumor grades. Thresholds ⁇ , T 2 , T 3 are then chosen to optimally correlate the tumor grade assigned based on kurtosis metric value with tumor histology grade for this training set. It will be appreciated that such training is readily performed for various types of tumors, e.g. brain tumors, breast cancer tumors, and so forth, and the kurtosis metric thresholds are then optimized to correlate with the specific tumor histology grading scheme used for each tumor type (where the tumor grade levels may have tumor type-specific nomenclatures).
  • a tumor grading operation 70 suitably uses the grading scheme of FIGURE 3 or the like to assign a tumor grade for the tumor represented by the ROI 32.
  • the tumor grade is displayed on the display device 22, optionally along with related information such as the heterogeneity/contrast map 60 to allow the radiologist to visually observe the heterogeneity across the tumor.
  • the heterogeneity value can be used to non-invasively infer tumour grade i.e. higher the tumour grade higher will be the heterogeneity.
  • the MR imaging employed a 1.5 Tesla MR scanner, with data acquired from 20 patients with Glioma brain tumors. Routine anatomic imaging was also performed using standard T1W, T2W, FLAIR and post-contrast T1W sequences.
  • To acquire the ADC map of a patient diffusion images were acquired by standard EPI-based diffusion weighted imaging (b-values of 0 and 1000). and the ADC map was generated using commercial software on the MR scanner console.
  • the co-occurrence matrices at different offsets d k were weighted by inverse of distance from the centre pixel i.e.
  • the co-occurrence matrices were concatenated along the third dimension (k) as described in operation 50 of FIGURE 1 to generate the 3D co-occurrence matrices of size /, /, K, where / and / are number of grey levels used for computing co-occurrence matrices and K represents number of neighbouring voxels or number of offsets.
  • the contrast metric was computed as described with reference to operation 54. This procedure was performed on each pixel of the ROI to obtain a Heterogeneity (or contrast) map as described with reference to the map 60 of FIGURE 1.
  • FIGURE 4 two examples are shown of this result.
  • the left-hand image is the ADC map
  • the right-hand image is the generated heterogeneity/contrast map (with the portions of the ADC map outside of the ROI shown in black).
  • the Heterogeneity kurtosis was computed from the histogram of heterogeneity map for the ROI corresponding to the brain tumor as already described with reference to FIGURE 1.
  • ADC mean the mean value of the ADC map in the ROI
  • ADC skewness the skewness of the ADC map in the ROI
  • ADC kurtosis the kurtosis of the ADC map in the ROI
  • heterogeneity kurtosis is used for the kurtosis metric computed from the heterogeneity map 60 in operation 64, to distinguish from the "ADC kurtosis” computed directly from the ADC map values.
  • ADC mean the patient group with tumor grade 3 has the highest aggregate ADC mean, while the patient group with tumor grade 4 has a lower aggregate ADC mean.
  • ADC skewness the patient group with tumor grade 1 has the highest skewness.
  • ADC kurtosis tumor grades 1 and 3 show the highest aggregate ADC kurtosis.
  • the illustrative tumor scoring examples score the tumor based on peakedness, i.e. kurtosis metric, computed for the heterogeneity/contrast map that is generated from an ADC map.
  • peakedness i.e. kurtosis metric

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Abstract

Selon la présente invention, pour effectuer une détermination de la classe histologique d'une tumeur par imagerie par résonance magnétique (RM), une carte de coefficients de diffusion apparent (CDA) (16) est acquise, et une région d'intérêt (RDI) (32) correspondant à une tumeur est identifiée dans la carte de CDA. Une carte d'hétérogénéité (60) correspondant à la RDI est générée par calcul d'une mesure de contraste pour chaque pixel de la RDI. Un histogramme des mesures de contraste sur la carte d'hétérogénéité est construit et, sur la base d'une mesure d'aplatissement de l'histogramme, la classe histologique de la tumeur est déterminée. Dans certains modes de réalisation, la carte d'hétérogénéité est générée sur la base de matrices de cooccurrence (44) avec différents décalages, générées pour des sous-matrices (40) couvrant la RDI. Dans d'autres modes de réalisation, la carte d'hétérogénéité est générée par calcul de caractéristiques de texture pour des sous-matrices couvrant la RDI. Les pixels de la carte d'hétérogénéité sont, d'une manière appropriée, des mesures de contraste calculées à l'aide des matrices de cooccurrence ou des caractéristiques de texture.
PCT/IB2016/052878 2015-06-03 2016-05-18 Détermination de la classe histologique d'une tumeur à l'aide de cartes de coefficients de diffusion apparente obtenues à partir de données de résonance magnétique (rm) WO2016193847A1 (fr)

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