The present application relates to the magnetic resonance arts. It is described with particular reference to imaging of features containing an ferrous materials, such as stem cells labeled with an iron oxide-based magnetic contrast agent, anatomical imaging using an iron oxide based magnetic contrast agent, deoxygenated blood imaging, and so forth. However, the following is amenable to other applications relating to imaging incorporating magnetic susceptibility contrast, such as probe imaging during interventional magnetic resonance, detection of in vivo foreign objects, and so forth.
An object or feature having a magnetic susceptibility that deviates from its surrounding creates local inhomogeneity of the main (B0) magnetic field. In a magnetic resonance image, such local inhomogeneity typically appears as a dark or low contrast region in the magnetic resonance image. Some examples of objects that may appear dark due to this effect in the context of imaging of surrounding human or animal tissue include metallic objects such as surgical instruments, implants or other devices, iron-containing substances like deoxygenated blood, iron oxide based contrast agents, or iron oxide-labeled cells. A metallic device or a gas-filled region has a magnetic susceptibility that differs substantially from surrounding tissue, and hence may appear dark in an image. The exploitation of this effect is an important tool for different MR imaging applications ranging from contrast agent (e.g. SPIO) detection to the localization of interventional devices such as catheters, implantable stents, and so forth.
Susceptibility contrast enhanced magnetic resonance imaging is usually performed using T2 or T2* weighted sequences. With these sequences the contrast is created by signal losses at the site of a local magnetic field disturbance, and so the feature causing the susceptibility contrast appears as a dark object. Unfortunately, these dark objects are not readily distinguishable in the image from other image features or artifacts generated by signal losses, or regions with lower proton density.
Several approaches have been proposed for obtaining positive (bright) contrast induced by magnetic susceptibility gradients. For example, EP 1 471 362 A1 discloses a method based on a gradient echo (GE) imaging sequence, in which imbalance of switched magnetic field gradients or additional gradients are applied in order to generate an image showing positive (bright) contrast between background tissue and objects producing local magnetic field inhomogeneities. This approach entails an a priori estimate of the strength of the susceptibility gradients, which is problematic. In one approach, an elaborate and time-consuming optimization procedure is performed to provide the susceptibility gradient strength estimate. Moreover, the use of a special magnetic resonance imaging sequence entails increased imaging time and complexity.
Still further, providing positive (bright) contrast in magnetic susceptibility gradient contrast imaging does not address the difficulty that there may be multiple sources of such contrast. For example, one application of magnetic susceptibility gradient contrast imaging is detection and study of cells or cell aggregations, in which the cells of interest are labeled with an iron oxide-based magnetic contrast agent. Using a positive (bright) magnetic susceptibility contrast imaging technique assures that the labeled cells will appear bright in the image, but does nothing to distinguish the labeled cells from other magnetic susceptibility gradient contrast sources such as air/tissue boundaries.
The following provides improvements, which overcome the above-referenced problems and others.
In accordance with one aspect, a method is disclosed comprising: acquiring magnetic resonance imaging data; generating a magnetic susceptibility gradient vector map from the magnetic resonance imaging data; and filtering the magnetic susceptibility gradient vector map to generate a magnetic susceptibility gradient image depicting magnetic susceptibility gradient information including at least some magnetic susceptibility gradient directional information.
In accordance with another aspect, a processor is disclosed that is programmed to perform a method comprising: acquiring magnetic resonance imaging data; generating a magnetic susceptibility gradient vector map from the magnetic resonance imaging data; and filtering the magnetic susceptibility gradient vector map to generate a magnetic susceptibility gradient image depicting magnetic susceptibility gradient information including at least some magnetic susceptibility gradient directional information.
In accordance with another aspect, an apparatus is disclosed comprising: a magnetic susceptibility gradient processor configured to generate a magnetic susceptibility gradient vector map from magnetic resonance imaging data; and a susceptibility gradient filter configured to filter the magnetic susceptibility gradient vector map to generate a magnetic susceptibility gradient image depicting magnetic susceptibility gradient information including at least some magnetic susceptibility gradient directional information.
In accordance with another aspect, a storage medium is disclosed storing instructions executable to perform a method comprising: generating a magnetic susceptibility gradient vector map from magnetic resonance imaging data; and filtering the magnetic susceptibility gradient vector map to generate a magnetic susceptibility gradient image depicting magnetic susceptibility gradient information including at least some magnetic susceptibility gradient directional information.
One advantage resides in facilitating distinguishing of different sources of magnetic susceptibility gradient contrast.
Another advantage resides in providing improved magnetic susceptibility gradient contrast in magnetic resonance images.
Another advantage resides in providing magnetic susceptibility gradient contrast including at least some susceptibility gradient directional information.
Another advantage resides in improved medical diagnostic, clinical, and related analyses achievable using characterization by magnetic resonance incorporating magnetic susceptibility contrast.
Still further advantages of the present invention will be appreciated to those of ordinary skill in the art upon reading and understand the following detailed description.
The drawings are only for purposes of illustrating the preferred embodiments, and are not to be construed as limiting the invention.
With reference to
A generally cylindrical whole-body coil 30 is optionally mounted substantially coaxially with the bore of the magnetic resonance scanner 10. The whole-body coil 30 may be, for example, a quadrature birdcage coil, transverse electromagnetic (TEM) coil, or so forth. Additionally or alternatively, one or more local radio frequency coils such as a surface coil or plurality of surface coils, a SENSE coil array, a torso coil, or so forth (not shown) can be employed. In the embodiment of
Although a horizontal bore-type scanner is illustrated as an example, it is to be appreciated that substantially any type of magnetic resonance scanner can be used, such as a vertical magnetic resonance scanner, an open magnetic resonance scanner, or so forth.
The magnetic resonance scanner 10 operates under the control of a scanner controller 40 to perform a selected magnetic resonance sequence, such as a three-dimensional echo-planar imaging (3D-EPI) sequence, to acquire k-space samples along a Cartesian grid or other configuration. The k-space samples are stored in a k-space data buffer 42. A reconstruction processor 44 applies a Fourier transform reconstruction algorithm suitable for reconstructing Cartesian k-space data, or applies another reconstruction algorithm that comports with the spatial encoding used in the k-space data acquisition, in order to generate a reconstructed image that is stored in an image buffer 46. A magnetic susceptibility gradient mapper 50 generates a magnetic susceptibility gradient vector (∇χ) map that is stored in a susceptibility gradient map buffer 52.
With continuing reference to
The magnetic susceptibility gradient mapper 50 extracts a three-dimensional magnetic susceptibility gradient map D∇χ from the complex three-dimensional magnetic resonance image data set DI using one-dimensional Fourier transformations. For each image voxel of the image data set DI (optionally excluding outermost edge voxels), one-dimensional Fourier transforms are computed in each of the three orthogonal Cartesian coordinate directions, such as in each of the three conventional x, y, and z dimensions.
The one-dimensional Fourier transforms are performed for subsets of n adjacent voxels separately in each dimension x, y, and z. In
The magnitudes of these vectors determined for all subsets of n voxels constitute an echo shift map SP. In some embodiments, the echo shift map SP has an n-fold reduced spatial resolution as compared to the three-dimensional magnetic resonance image data set DI. In other contemplated embodiments, the Fourier transforms is performed using a sliding window, and reduces the resolution by an amount smaller than n. Optionally, any resolution lost due to the performing of the Fourier transform is recovered by linear interpolation to generate the three-dimensional magnetic susceptibility gradient map Dvx with the same resolution as the image DI. Alternatively, the echo shift map SP can serve as the magnetic susceptibility gradient map. In
The illustrated reconstruction processor 44 and magnetic susceptibility gradient mapper 50 are examples. More generally, any suitable technique can be used to generate three-dimensional magnetic susceptibility gradient map D∇χ including susceptibility directional information from acquired magnetic resonance imaging data. For example, another contemplated approach employs specialized magnetic resonance sequences to acquire magnetic resonance imaging data from which appropriate processing can generate the three-dimensional magnetic susceptibility gradient map D∇χ including directional information directly without an intermediate image reconstruction operation. In other contemplated embodiments, the susceptibility gradient map is calculated in the (complex) image-domain, by using a phase map and fitting a linear slope to the phase of adjacent voxels in each direction of space, similar to the FFT in each direction of space. This provides similar information since the echo shift in k-space is reflected in a change in phase in image-space. This approach may entail unwrapping of the phase, especially in 3D, before fitting the linear slope.
With continuing reference to
In some embodiments, the susceptibility gradient filter 60 is configured to suppress large-scale magnetic susceptibility gradients, that is, susceptibility gradients that exhibit directional ordering on a relatively large scale. These embodiments are of advantage where the features of interest are expected to be of relatively small scale, such as imaging of labeled cells or of aggregations of labeled cells. In such an application, large-scale magnetic susceptibility gradient directional ordering is likely to be associated with an air/tissue transition or other larger scale anatomical feature not related to the labeled cells. In a suitable approach for suppressing large-scale magnetic susceptibility gradients, the directional ordering of the magnetic susceptibility gradients is determined on a per-pixel or per-voxel basis. If the directional ordering exceeds a threshold value, then the susceptibility gradient is suppressed as a large-scale susceptibility gradient.
With continuing reference to
where the voxel Vx,y,z is the central voxel, the symbol “←” denotes the replacement operation, and the expression to the right of the “←” symbol denotes the vector sum of the magnetic susceptibility vectors of the voxel Vx,y,z and its twenty-six nearest neighbors, that is, the vector sum of the magnetic susceptibility vectors of the twenty-seven voxels in the 3×3×3 cube of voxels centered on the voxel Vx,y,z, normalized by the scaling factor 1/27. The symbol ∥·∥ denotes the magnitude of the vector sum.
The magnitude of this vector sum kernel is likely to be large if the susceptibility vector has ordering over a spatial range at least as large as the kernel size, that is, the 3×3×3 cube of voxels, since in that case the susceptibility gradient vectors combined by the kernel of Equation (1) are oriented in the same general direction and will combine to produce a relatively large vector sum. Such is expected to be the case for a magnetic susceptibility gradient generated by air/tissue boundaries and other relatively large-scale features.
On the other hand, a small object such as a biological cell or small group of biological cells marked with an iron oxide-based magnetic contrast agent will produce a magnetic susceptibility gradient that has ordering over a typically small spatial range. In that case, the susceptibility gradient vectors combined by the kernel of Equation (1) are oriented in generally different directions, and will not combine to produce a relatively large vector sum. In other words, while the susceptibility gradient vectors due to the labeled cell or aggregation of labeled cells may have individually large magnitudes, the directions of these susceptibility gradient vectors are substantially randomly oriented, so that the susceptibility gradient vectors tend to cancel out in the vector sum of Equation (1), producing a small value.
The kernel of Equation (1) is an illustrative example, and other kernels can be used. For example, a larger kernel performing a vector sum over the 125 voxels of a 5×5×5 cube can be similarly used. Kernels that perform combinations other than vector sums are also contemplated, such as a kernel that sums only one component of the magnetic susceptibility vectors, e.g. the “x” component.
With reference to
For the illustrated example in which the kernel defines the scaled magnitude of a vector sum, the filtering further includes a thresholding operation. In a suitable approach, a graphical user interface dialog 86 provides the user with a slider 88 that the user can manipulate to select the amount of filtering. As indicated in the graphical user interface dialog 86, a large value of the threshold as input via the slider 88 results in filtering out only the largest features, that is, those features exhibiting the largest long-range directional ordering. Conversely, a small value of the threshold as input via the slider 88 results in filtering out all but the smallest features, that is, those features having very little long-range directional ordering. The illustrated slider 88 is optionally replaced by a numerical input, a discrete set of selections spanning the allowed threshold range, or so forth. The threshold selected using the graphical user interface dialog 86, or selected via another type of user input, or hard-coded into the filter 60, is applied by a thresholder 90. If the filtered value (in the illustrated example, the normalized vector sum magnitude) is below the threshold value, then the indicated long-range directional ordering is low enough that the susceptibility gradient feature likely corresponds to a cell or cell aggregation or other feature of similar size, and so a voxel magnitude transfer block 92 computes the magnitude of the selected voxel (that is, the central voxel of the applied kernel). Alternatively, if the filtered value is at or above the threshold value, then the indicated long-range directional ordering is too high, indicating that the susceptibility gradient feature likely corresponds to an air/tissue boundary or other large-scale feature that is not of interest, and so a voxel replacement block 94 replaces the selected voxel (that is, the central voxel of the applied kernel) with a default low brightness value.
The outputs of the blocks 92, 94 are combined to generate a susceptibility gradient image 96 with both magnitude and directional information. Magnitude information is included in the susceptibility gradient image 96 in that the magnitudes of those susceptibility gradient vectors having only short-range directional ordering are incorporated into the gradient image 96 via the voxel magnitude transfer block 92. Directional information is included in the susceptibility gradient image 96 in that those susceptibility gradient vectors having long-range directional ordering that is “too large” as indicated by the threshold are removed and replaced by the default low-brightness value, so that the susceptibility gradient image 96 retains only susceptibility gradients with short-range ordering compared with the threshold. The susceptibility gradient image 96 is suitably stored in the susceptibility gradient image buffer 62 shown in
With reference to
The illustrated kernel filtering is an illustrative example. The filter 60 can employ other types of filters. For example, the filter 60 can apply a filter including applying a vector field operation to the magnetic susceptibility gradient. For example, one vector field operation that is contemplated as being useful for identifying labeled cells or cell aggregations is the divergence operation. A suitable filter is as follows:
The divergence field operation is an operator that measures the tendency of the field to originate from or converge upon a given point. In the case of a labeled cell or cell aggregation, the susceptibility gradient should originate from or converge at the labeled cell or cell aggregation, and so Equation (2) should have a relatively large value for such regions. On the other hand, an air/tissue interface is more extended and does not have a point source configuration, and so the divergence value is relatively lower. In some embodiments, the output D∇χF of Equation (2) is thresholded as shown in
The filtering approach shown in
Alternatively, the threshold could be used to generate the susceptibility gradient image depicting magnetic susceptibility gradient magnitude but with magnetic susceptibility gradient magnitude directionally ordered over less than a selected spatial ordering range suppressed. Such an alternative approach is well-suited for imaging larger-scale features such as air/tissue interfaces while suppressing smaller-scale magnetic susceptibility gradient features that are unlikely to correspond to air/tissue interfaces.
The filtering approach shown in
With reference to
Such a tracking approach is expected to be useful when the source of the magnetic susceptibility gradient has long-range ordering corresponding to a flow. For example, an iron oxide-based magnetic contrast agent injected into the bloodstream can produce inflow of contrast agent into an organ of interest that can be tracked using the contemplated directional tracking approach. By acquiring successive images at different times during the influx of contrast agent into the organ of interest, the inflow can be accurately mapped over time, and features such as blood flow blockages identified. The resulting flow lines can be superimposed on the reconstructed image, or on the a magnitude image of the susceptibility gradient map D∇χ. More complex representations, for example in which the displayed flow line has a width at each point along the flow line corresponding to the magnitude of the susceptibility gradient vector at that point, are also contemplated.
With reference back to
The preferred embodiments have been described. Modifications and alterations may occur to others upon reading and understanding the preceding detailed description. It is intended that the invention be construed as including all such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Filing Document | Filing Date | Country | Kind | 371c Date |
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PCT/IB08/51672 | 4/30/2008 | WO | 00 | 10/27/2009 |
Number | Date | Country | |
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60914809 | Apr 2007 | US |