Patent Application
20230320610
The present disclosure is directed, in general, to imaging techniques for imaging biological objects, such as tissues, using for instance Magnetic Resonance Imaging (MRI). More specifically, the present invention is directed to methods and systems for estimating brain tissue damage within white matter tracts from a quantitative map, notably from quantitative MRI (qMRI).
For some neurological pathologies, it has been shown that the amount of focal tissue damage (e.g., lesion, tumor) seen in MRI does not correlate with clinical scores reflecting the patient's well-being. For example, for the clinical examination of multiple sclerosis (MS) patients, radiologists typically evaluate the number of lesions that are visible in the MRI scan. However, this radiological metric (“lesion count”) correlates poorly with a patient's disability level as given by standard clinical metrics such as the “Expanded Disability Status Scale” used in MS. This discrepancy between radiological findings and symptoms is well-known and referred to as the ‘clinico-radiological paradox’ of Multiple Sclerosis [1]. This phenomenon is however also observed in other brain diseases.
To fill this gap, other, more informative measurements derived from diagnostic images (or ‘imaging biomarkers’) can be extracted from MRI scans. For example, diffusion imaging (i.e., probing the directionality of water molecules in tissue through MRI) can be used to derive fiber tracts, i.e., determining the route of bundles of axons within the brain. This allows to see which brain regions are connected, and where the connecting fiber tracts run. This so-called “connectome” view on the brain has shown great potential for characterizing neurological disorders in a more comprehensive manner. Knowing these pathways, focal tissue damage can be situated with respect to the fiber tracts, and hence the patient's symptoms correlated to the function of the brain regions which are connected by the affected fibers rather than just correlating a simple count or similar. To obtain these brain pathway maps (“connectomes”) through techniques called “fiber tracking,” advanced MRI diffusion data have to be available, which is rarely the case in routine clinical examinations.
In the past years, various imaging biomarkers have been investigated to improve the correlation between patient disability and imaging biomarkers. For instance, different studies were conducted to investigate focal damage locations specifically in different white matter (WM) tracts. In 1998, a study on 39 MS patients showed that lesion load on the manually delineated cortical spinal tract correlated better with EDSS than total lesion load [2]. In another study, the authors used the time before the patient requires bilateral support to walk as a disability metric to be correlated with the lesion load in major motor and associative tracts. A significant correlation was found between disability and voxel-wise lesion probability in the corticospinal tract, the superior longitudinal fasciculus and the right inferior fronto-occipital fasciculus [3].
Other methods are based on qMRI. An advantage of qMRI is that it measures absolute physical parameters of the tissue, resulting thus in better comparability between longitudinal scans of the same patients, between multi-site acquisitions or between different cohorts of subjects (e.g., healthy vs. pathological patients) in comparison to conventional “weighted” imaging. As qMRI provides comparable tissue parameters independent from hardware and other spurious effects, it enables the creation of “normative atlases”, i.e., brain maps of tissue parameters which define a range of normal values seen in healthy tissue. Having such an atlas, an individual patient dataset can be checked against it, resulting in a “deviation map” which identifies brain regions where the patient's tissue characteristics differ from what is expected in healthy tissue. For instance, it enables to quantify the extent of diffuse tissue damage (e.g., inflammation, myelin degradation, axonal loss, among others) in normal-appearing tissue, and can thus improve disease characterization. By allowing for such a single-patient assessment, already small changes can be detected, potentially improving diagnosis and follow-up assessments by correlating parameter variations with the undergoing microstructural changes [4]. Another work using qMRI for MS assessment showed that evaluating T1 relaxation time in normal-appearing tissue was a predictor of disease progression longitudinally using multiple linear regression [5]. From a general point of view, a good overview of quantitative imaging biomarkers and their correlation with disease status and disability is given in [6].
It is accordingly an object of the invention to provide a method and system which overcomes the above-mentioned disadvantages of the heretofore-known devices and methods of this general type and which provides for a method and a system that is capable of estimating tract-specific quantitative metrics, such as qMRI metrics, within a short examination time, in particular free of diffusion imaging, that enable a better correlation with clinical outcomes in patients, that is feasible during routine clinical examination, and the results of which can be compared between patients independently from the examination site/MRI imaging material.
With the above and other objects in view there is provided, in accordance with the invention, 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.
In other words, the objectives of the invention are achieved by a method and a system for imaging brain tissue damage within white matter tracts from a quantitative imaging technique, e.g. qMRI, according to the object of the independent claims. Dependent claims present further advantages of the invention.
In other words, the present invention concerns a computer-implemented method for imaging or mapping brain tissue microstructural damages from quantitative imaging data, like qMRI data, e.g. for mapping an extent of damage in white matter tracts for the brain, the method comprising:
According to the present invention, metrics are derived from quantitative MR imaging data along fiber tracks. By combining quantitative metrics from quantitative imaging, like qMRI, with spatial and functional information of the fiber tracks coming from the tractography map, the obtained metrics show increased clinical value.
With the above and other objects in view there is also provided, in accordance with the invention, a system in which the above-summarized method can be performed. The novel system is configured for mapping, preferably automatically, brain tissue microstructural damage from quantitative data, e.g. qMRI data. The system comprises:
a first interface for receiving or acquiring a quantitative map, e.g. a qMRI map, of a tissue parameter for the brain;
a second interface, which might be the same as the first interface, and which is configured for acquiring or receiving a tractography map;
a memory for storing the quantitative map and/or the tractography map;
a control unit comprising a processor, the control unit being configured for carrying out the steps of the previously described method. The control unit is thus in particular configured for using a tractography atlas and spatial registration between quantitative maps and WM tracts density maps for calculating the metrics;
a display connected to the control unit and configured for displaying the metrics obtained for the brain, e.g. via a brain map of the metrics.
The foregoing has broadly outlined the features and technical advantages of the present disclosure so that those skilled in the art may better understand the detailed description that follows. Additional features and advantages of the disclosure will be described hereinafter that form the object of the claims. Those skilled in the art will appreciate that they may readily use the concept and the specific embodiment disclosed as a basis for modifying or designing other structures for carrying out the same purposes of the present disclosure. Those skilled in the art will also realize that such equivalent constructions do not depart from the spirit and scope of the disclosure as defined by the set of claims.
The construction and method of operation of the invention, together with additional objects and advantages thereof will be best understood from the following description of specific embodiments when read in connection with the accompanying drawings.
Referring now to the figures of the drawing in detail,
In
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.
According to the present invention, the system 200 is configured for carrying out the following steps:
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.
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:
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.
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.
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.
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.
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.
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.
Finally, the previously described invention presents the following advantages with respect to prior art techniques:
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:
MR magnetic resonance
MRI magnetic resonance imaging
qMRI quantitative magnetic resonance imaging
MS multiple sclerosis
WM white matter
MTR magnetization transfer ratio
CT computed tomography
