METHOD AND SYSTEM FOR ESTIMATING BRAIN TISSUE DAMAGE WITHIN WHITE MATTER TRACTS FROM A QUANTITATIVE MAP

Abstract

A system and a method for mapping brain tissue damage from quantitative imaging data. The method is implemented by acquiring a quantitative map of a brain tissue parameter of said brain; acquiring a tractography map for said brain; superimposing a first map based on the quantitative map onto a second map based on the tractography map. Metrics are extracted from the superimposition that reflect a distribution of tract-specific quantitative values of the brain tissue parameter and the metrics of the brain are displayed.

Claims

1. A computer-implemented method for mapping brain tissue damage from quantitative imaging data, the method comprising: acquiring a quantitative map of a brain tissue parameter of the brain; acquiring or receiving a tractography map for the brain; superimposing a first map based on the quantitative map onto a second map based on the tractography map to form a superimposition; extracting from the superimposition metrics reflecting a distribution of tract-specific quantitative values of the brain tissue parameter; and displaying the metrics of the brain.

2. The computer-implemented method according to claim 1, wherein the first map is a deviation map or the quantitative map itself.

3. The computer implemented method according to claim 1, wherein the second map is the tractography map itself or a processed tractography map, and the method further comprises, prior to superimposing the first map onto the second map, processing the first map for having, for each of the voxels of the first map, an intensity value of the concerned voxel quantifying a deviation of the value of the brain tissue parameter for the voxel with respect to a reference value of the brain tissue parameter for the voxel.

4. The computer-implemented method according to claim 1, wherein the extracting step comprises using aggregate statistics for extracting the metrics.

5. The computer-implemented method according to claim 4, wherein the aggregate statistics are configured for computing a sum of voxel-wise qMRI values weighted by voxel-wise tract density values.

6. A system for mapping brain tissue damage from quantitative imaging data, the system comprising: a first interface for receiving or acquiring a quantitative map of a tissue parameter for a brain; a second interface configured for acquiring or receiving a tractography map; a memory for storing at least one of the quantitative map or the tractography map; a control unit including processor, said control unit being configured for carrying out the steps of the method according to claim 1; and a display connected to said control unit and configured for displaying the metrics obtained for the brain.

Description

BRIEF DESCRIPTION OF THE FIGURES

[0030] FIG. 1 illustrates a flowchart of a method for estimating tract-specific brain tissue damages from qMRI data according to the invention; and

[0031] FIG. 2 illustrates a system for mapping brain tissue damages from qMRI data according to the invention.

DETAILED DESCRIPTION OF THE INVENTION

[0032] Referring now to the figures of the drawing in detail, FIGS. 1 and 2 illustrate various embodiments describing the principles of the present disclosure; the figures are for illustration only and should not be construed in any way to limit the scope of the disclosure. Those skilled in the art will understand that the principles of the present disclosure may be implemented in any suitably arranged device. The numerous innovative teachings of the present application will be described with reference to exemplary non-limiting embodiments.

[0033] FIG. 1 describes the different steps of the method 100 carried out by a preferred embodiment of the system according to the invention which is illustrated by FIG. 2.

[0034] In FIG. 2, a control unit 202 of the system 200 according to the invention is preferably connected, for instance via a first interface a, to an MRI system 201. While qMRI will be taken as illustration of the present invention, other imaging systems might be connected to the system 200 according to the invention, as long as they can provide the system 200 with a quantitative map of a brain quantitative parameter.

[0035] The MRI system 201 typically comprises different coils and respective coil controllers configured for generating magnetic fields and RF pulses in order to acquire an MRI signal from a brain 206 under investigation. The MRI signal is transmitted by a receiver coil controller to the control unit 202. The latter might be configured for reconstructing qMRI maps of the brain 206 from the MRI signal. In such a case, the control unit 202 might be configured for controlling the MRI system so that the latter performs MR imaging enabling an acquisition of qMRI maps. Alternatively or additionally, the control unit 202 might be connected to a database or any other system for acquiring or receiving, e.g. via the first interface a, qMRI maps. The control unit 202 comprises typically a memory 203 and is connected to an interface, e.g. a display 204 for displaying images reconstructed from the received MRI signal.

[0036] According to the present invention, the system 200 is configured for carrying out the following steps:

[0037] At step 110, the system 200, e.g. its control unit 202, receives or acquires one or several qMRI maps 101 of the brain 206. The qMRI maps 101 might be obtained from MRI scans of the brain according to techniques that are known in the art and that are configured for providing quantitative MRI data.

[0038] According to the present invention, a qMRI map is a brain map made of voxels, wherein the intensity value of each voxel is a measure of a brain tissue parameter obtained via a quantitative magnetic resonance imaging technique. A qMRI map according to the invention is for instance: [0039] T1 map, measuring T1 relaxation time; [0040] T2 map, measuring T2 relaxation time; [0041] T2* map, measuring T2* relaxation time; [0042] T1 ρ map, measuring T1 ρ relaxation time; [0043] MT (magnetization transfer), measuring tissue myelination; [0044] any diffusivity map, such as tissue fractional anisotropy; [0045] myelin water imaging, measuring tissue myelination; [0046] quantitative conductivity map, measuring tissue electrical conductivity; [0047] quantitative susceptibility map, measuring tissue magnetic susceptibility; [0048] quantitative elastography map, measuring tissue mechanical stiffness.

[0049] At step 111 and optionally, the system 200, e.g. its control unit 202, creates or computes, for the brain tissue parameter and from the acquired or received qMRI map, a deviation map 102, the latter being configured for mapping, for the brain and for each voxel, deviations of the acquired value of the brain tissue parameter with respect to a reference value mapped for that voxel in a reference map. Typically, the deviation map might be created by evaluating voxel-wise z-scores. Of course, other metrics reflecting a degree of difference between measured brain tissue parameter and a standard or reference value (e.g. mean or median value) obtained from a healthy cohort can be used. Optionally, the deviation map may be masked or thresholded to only show significant deviations.

[0050] At step 120, the system 200 receives or acquires, notably via a second interface b of the control unit 202, a brain tractography map 103. The system 200 is then preferably configured for automatically identifying, in the brain tractography map, clusters of streamlines 104 that define, each, a fiber bundle (i.e., an axonal pathway of WM tracts). Each cluster defines thus a different fiber bundle.

[0051] At step 121, the system 200, e.g. its control unit 202, is preferably configured for extracting or creating, for each streamline cluster 104, a tract density map 105 of the brain, wherein each voxel intensity value in the tract density map represents a number of streamlines of the cluster passing through that voxel. In other words, one tract density map 105 is created or extracted per tract, i.e., per streamline cluster 104. This means also that the tractography map comprises, in its whole, multiple tract density maps 105, one for each tract.

[0052] At step 130, the system 200, e.g. its control unit 202, is configured for superimposing, for each cluster, the qMRI map, or if created, the deviation map 102, and the processed tractography map, i.e., the tract density map obtained for that cluster. By superimposing, it has to be understood that the tract density map and the qMRI (or deviation) map are registered to a common space using known in the art spatial registration techniques, the common space being for instance the atlas space or the space of the brain under investigation.

[0053] At step 140, the system 200, e.g. its control unit 202, is configured for extracting, from the superimposition, metrics reflecting a distribution of tract-specific quantitative deviations. For instance, the metrics are tract-specific qMRI biomarkers extracted using aggregate statistics, configured for computing for instance a sum of voxel-wise qMRI values weighted by voxel-wise tract density values. Optionally tract-specific qMRI biomarkers might be normalized by some tract properties, like the length of the considered tract. Of course, other aggregate statistics could be used, like a (weighted) sum, mean, median or standard deviation of the values on the tract. More complex statistics could also be implemented, like an analysis of a histogram of values (e.g. peak, area under curve, etc.

[0054] At step 150, the system 200 is configured for mapping the metrics, for instance by displaying via the display 204, a map of the metrics, e.g. of the tract-specific qMRI biomarker.

[0055] Finally, the previously described invention presents the following advantages with respect to prior art techniques: [0056] The resulting statistical tract-specific qMRI metrics are more specific to the brain function than a usual region of interest analysis; [0057] Tract-specific statistical metrics can be estimated without requiring diffusion imaging, therefore resulting in shorter examination times, opening the way to clinical applications; [0058] As diffusion imaging and tractography are not necessarily required, the present invention substantially improves inter-site variability induced by a use of different acquisition protocols or tractography algorithm; [0059] Employing a tractography atlas allows using pre-existing WM tracts defined at a very fine level, which improves the precision of the resulting statistical metrics. This would be difficult to achieve with clinical DWI, mainly due to technical limitations and time constraints such as the filtering of false positive streamlines; [0060] The interpretation of the relation between the deviation map and the tractography map is improved by extracting aggregate statistics which enables to analyze deviations for specific tracts. This is notably enabled by the spatial registration onto a common space of the tractography map with the qMRI map; [0061] The proposed concept might be applied to various kinds of qMRI maps (e.g. T1, T2, T1rho, T2*, MTR, MWF, FA, ADC, etc.) combined or not to other quantitative information stemming from a different modality in order to compute the deviation map.

[0062] To summarize, the present invention proposes to evaluate quantitative parameters along fiber tracts, combining two pieces of information relevant for characterization of a brain disease: the notion of “microstructural tissue alteration” detected through quantitative imaging, like qMRI, and the knowledge about how the location of this tissue alteration affects a pathway, thus the brain regions connected by the pathway and hence functions which are situated in these brain regions. It has been shown that the combination of these two complementary parameters adds clinical value as the derived imaging biomarkers, i.e., the metrics, correlate better with clinical symptoms and scores.

The following is a summary list of acronyms and the corresponding structure used in the above description of the invention:

[0063] MR magnetic resonance

[0064] MRI magnetic resonance imaging

[0065] qMRI quantitative magnetic resonance imaging

[0066] MS multiple sclerosis

[0067] WM white matter

[0068] MTR magnetization transfer ratio

[0069] CT computed tomography

LIST OF CITATIONS

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