PRECISION AND RESOLUTION OF QUANTITATIVE IMAGING BY COMBINING SPECTRAL AND NON-SPECTRAL MATERIAL DECOMPOSITION

Abstract

The invention proposes to combine spectral image data with non-spectral image data in order to overcome limitations of the different data taking methods. Results from the methods are preferably combined as functions of spatial frequency so that spectral image data provide high accuracy at low frequencies, whereas the non-spectral image data helps reducing the noise at high frequencies. The invention enables a range of applications in different fields of X-ray imaging such as improved tissue contrast and tissue characterization.

Claims

1. A calculation unit for processing quantitative image data, wherein the calculation unit comprises, a receiving unit; and a processing unit, wherein the receiving unit is configured for receiving spectral image data of an object of interest; wherein the receiving unit is configured for receiving non-spectral image data of the object of interest, wherein the processing unit is configured for calculating material properties as a function of position from the spectral image data; wherein the processing unit is configured for calculating material properties as a function of position from the non-spectral image data; wherein the processing unit is configured for combining the material properties calculated from the spectral and from the non-spectral image data as a function of spatial frequency by weighting the spectral and the non-spectral image data depending on the spatial frequencies in the image.

2. The calculation unit according to claim 1, wherein the processing unit is configured to give stronger weight to the low-spatial-frequency parts of the spectral image data as compared to the low-spatial-frequency parts of the non-spectral image data; and wherein the processing unit is configured to give stronger weight to the high-spatial-frequency parts of the non-spectral image data as compared to the high-spatial-frequency parts of the spectral image data.

3. The X-ray imaging system, comprising a calculation unit of claim 1, and an imaging unit, wherein the imaging unit is configured to provide spectral and non-spectral image data to the calculation unit.

4. The X-ray imaging system according to claim 3, wherein the spectral and/or the non-spectral image data are phase contrast image data, differential phase contrast image data, or dark field image data.

5. A method for quantitative image data processing, the method comprising the following steps: Acquiring spectral image data of an object of interest; Calculating material properties as a function of position from the spectral image data; Acquiring non-spectral image data of the object of interest; Calculating material properties as a function of position from the non-spectral image data; and Combining the material properties calculated from the spectral and from the non-spectral image data as a function of spatial frequency comprising the step of weighting the spectral and the non-spectral image data depending on the spatial frequencies of the image.

6. The method according to claim 5, wherein the spectral image data are spectral tomosynthesis data and/or the non-spectral image data are non-spectral tomosynthesis data.

7. The method according to claim 5, wherein the step of acquiring the non-spectral image comprises using additional a priori assumptions and/or additional data input.

8. The method according to claim 5, wherein the step of combining the material properties calculated from the spectral and from the non-spectral image data as a function of spatial frequency is at least partly carried out in the spatial-frequency domain.

9. The method according to claim 5, wherein the step of combining the material properties calculated from the spectral and from the non-spectral image data as a function of spatial frequency is at least partly carried out in the spatial domain and comprises the step of a convolution with a filter kernel and/or other means of low-pass, band-pass or high-pass filtering.

10. The method according to claim 5, wherein the step of weighting as a function of spatial frequency gives stronger weight to the low-spatial-frequency parts of the spectral image data as compared to the low-spatial-frequency parts of the non-spectral image data, and wherein the step of weighting as a function of spatial frequency gives stronger weight to the high-spatial-frequency parts of the non-spectral image data as compared to the high-spatial-frequency parts of the spectral image data.

11. The method according to claim 5, wherein the step of calculating material properties as a function of position from the spectral image data and from the non-spectral image data includes the step of identifying or characterizing at least one of the following: properties identifying a lesion, such as a cyst or a tumor or a microcalcification; properties identifying glandular and adipose breast tissue or skin; concentration of a contrast agent inside the object of interest.

12. A computer program element, which, when executed by a processor, is adapted to carry out the method steps according to claim 5.

13. A computer readable medium, comprising a computer program element according to claim 12.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0035] Exemplary embodiments of the invention will be described in the following with reference to the following drawings.

[0036] FIG. 1 shows an X-ray imaging system with a calculation unit according to an embodiment of the invention;

[0037] FIG. 2 shows a calculation unit according to an exemplary embodiment of the present invention;

[0038] FIG. 3 shows basic steps of an exemplary method for enhanced quantitative image data processing according to an embodiment of the present invention;

[0039] FIG. 4 shows a further example of a method according to an embodiment of the invention;

[0040] FIGS. 5A and 5B show the comparison of volumetric breast-density maps obtained without and with a method according to an embodiment of the invention.

DETAILED DESCRIPTION OF EMBODIMENTS

[0041] FIG. 1 shows an X-ray imaging system 1, which is given by a mammography system using, for instance, a photon counter. The X-ray imaging system 1 comprises an X-ray source 10 mounted on a vertical support structure 12. Further, an X-ray detector arrangement 20 is mounted on the vertical support structure 12 comprising a first support surface 22, on which a patient can, for instance, arrange a breast onto. An adjustable compression plate, or compression paddle, 24 is arranged above the first support surface 22, allowing a breast to be arranged between the compression plate 24 and the first support surface 22. The compression plate is adjustable in height, as indicated by the double arrow 14, in order to adapt the distance between the first support surface and the breast touching surface of the compression plate. The X-ray detector arrangement may comprise an imaging unit, which is configured to provide spectral image data as well as non-spectral image data to a calculation unit of the X-ray imaging system, which is described hereinafter. Furthermore, the X-ray detector arrangement is adjustable in height, as indicated by the double arrow 16, so as to suitably adjust to the height of a patient.

[0042] The X-ray imaging system 1 further comprises data and supply connections 30. By means of these connections, raw image data acquired with the X-ray detector arrangement, in particular the imaging unit, can be provided to a calculation unit C, exemplarily comprised in a housing structure 32 in FIG. 1. It is noted that data and supply connections can be wired and/or wireless connections, as for example WLAN or Bluetooth connections. Further, image data can be stored on a computer readable medium such as a CD, DVD, portable flash drives or portable hard-disk drives, and can be provided to the calculation unit C by means of a suitable data provision unit, such as for instance a CD or DVD drive or a USB port connected to the calculation unit C.

[0043] The calculation unit C can be connected to a display unit D comprised in display housing structure 34, which is adjusted for visual representation of quantitative image data that have been processed by the calculation unit C. For instance, quantitative image data visualized by the display unit D can be maps of volumetric breast density, also referred to as glandularity maps, or maps of any other combination of materials, calculated from spectral and non-spectral image data, which have been acquired by the X-ray detector arrangement and which have been processed by the calculation unit C.

[0044] It is noted that the present invention is also related to other types of mammography X-ray imaging systems, for instance systems in which the patient is lying on a support structure with the face looking downwards, such as biopsy systems. Further, the X-ray imaging system may also comprise further movable structures to which the X-ray source and the X-ray detector arrangement can be mounted, for example the image may be acquired by scanning a narrow detector across the image field and/or the X-ray tube may be moved along an arc to obtain image data from multiple angles.

[0045] It is further noted that the present invention is not limited to mammography systems. The X-ray system can be any type of 2D or tomosynthesis X-ray imaging system that provides spectral and non-spectral image data.

[0046] The arrangement shown in FIG. 1 is schematic and exemplary. An X-ray imaging system according to the invention could, for instance, also have a common housing for the X-ray detector arrangement comprising the imaging unit, the calculation unit and the display unit.

[0047] FIG. 2 schematically shows a calculation unit C according to an exemplary embodiment of the invention. The calculation unit comprises a receiving unit 31 and a processing unit 33. The former receiving unit can be configured to receive spectral and non-spectral image data from an imaging unit 26. Thereby, the imaging unit 26 can be comprised in an X-ray detector arrangement as exemplarily shown in FIG. 1. The order in which the spectral image data and the non-spectral image data are received by the receiving unit 31 in FIG. 2 is arbitrary. Receiving may be successively or simultaneously. The receiving unit 31 can then supply the spectral and the non-spectral image data to the processing unit 33. It should be noted that the receiving unit 31 can also receive spectral and/or non-spectral image data, which have been acquired previously and are stored on a storage medium, which might be connected to the calculation unit for providing the respective image data to the receiving unit 31. The processing unit 33 calculates material properties as a function of position from the spectral image data and from the non-spectral image data. This can be done simultaneously or subsequently. The order in which calculation is performed by the processing unit is arbitrary. The processing unit 33 is further configured for combining the calculated material properties from the spectral and the non-spectral image data as a function of spatial frequency. The combined result can then be provided to a display unit 34. Optionally, the latter display unit 34 can be configured for displaying the combined result on the material properties as a function of spatial frequency.

[0048] In the following, basic steps of an exemplary method for processing quantitative image data are described with respect to FIG. 3. It should be noted that the X-ray detector arrangement together with the calculation unit C shown in FIG. 1 can be arranged for performing the method steps described in the following. The method comprises the following basic steps. In a first step 110, spectral image data of an object of interest are acquired. Thereby, the object of interest can be the breast or part of the breast of a patient. The spectral image data can be acquired by an X-ray detector arrangement comprising, for instance a photon-counting detector. The spectral image data can be acquired by multiple exposures of the object of interest at different X-ray energy spectra, obtained, for instance, by using multiple acceleration voltages in the X-ray tube and/or multiple filtrations. Alternatively, the spectral image data can be acquired by a single exposure using one X-ray spectrum and an energy-sensitive detector, which divides the detected photons into several bins according to their respective energy. It is also conceivable to combine these two approaches. i.e. using two different spectra and an energy sensitive detector to further enhance the spectral information. In a subsequent method step 112, material properties as a function of position are calculated from the spectral image data. The position can refer to position in physical space, for instance in 2D or 3D coordinates. A temporal dimension may be added optionally. Further, in method step 114, non-spectral image data of the object of interest are acquired. From these non-spectral image data, material properties as a function of position are calculated in step 116.

[0049] It should be noted that the spectral and/or the non-spectral image data may contain information on the phase of the X-rays that have passed through the object of interest instead of, or in addition to, information on the absorption of the X rays. Such phase-contrast information might be acquired as the differential of the phase with respect to position in the image. In the latter case it is referred to as differential phase contrast. It should be further noted that the spectral and/or the non-spectral image data may contain information on the small-angle X-ray scattering properties of the object instead of, or in addition to, information on the absorption of the X rays. Such information can be used to form dark-field images.

[0050] Acquisition of the spectral and non-spectral data may be in the reversed order or combined into a single step in which the non-spectral data is derived from the spectral data. In method step 118 the material properties calculated from the spectral and from the non-spectral image data are combined as a function of spatial frequency, where the spatial frequency in this sense represents structure size in the image; it is essentially a measure of how often sinusoidal components of the image repeat per unit of distance.

[0051] In optional method step 120, a visual representation of the material properties calculated from spectral and non-spectral image data is provided.

[0052] The spectral data acquired in step 110 can be acquired with a spectral photon-counting detector. In this case, the spectral detector generates, for example, low-energy (for instance lower half of spectrum) and high-energy (for instance upper half of spectrum) counts, thereby generating two image data sets in each image acquisition. The full-spectrum total (non-spectral) counts are simply the sum of the low- and high-energy counts. The low- and high-energy data sets each have a lower number of counts per pixel and therefore a higher quantum noise compared to the total-count data. The total-count data can then generate an image with maximal signal-to-noise ratio, whereas the low- and high-energy data each generate images with different average energies and a lower signal-to-noise ratio than the total-count image. The low- and high-energy count images can subsequently be combined to generate a spectral image. The spectral images obtained by this procedure will have increased noise compared to the total-count image because 1) the signal-to-noise ratio in each of the low- and high-energy count images is lower, and 2) the spectral processing is often equivalent to taking a difference between the two images, which is an operation that essentially adds the noise of the two images while the image signal is reduced.

[0053] According to an exemplary embodiment, the spectral data in method step 110 are acquired as spectral tomosynthesis data. Spectral tomosynthesis data can be acquired using a photon-counting detector, or, for instance sandwich detectors, kVp switching, filter switching, or multiple exposures.

[0054] The non-spectral data acquired in method step 114 can be data related to a non-spectral image acquired separately or the non-spectral image can be the total-count data acquired as part of the spectral image acquisition.

[0055] The exemplary method according to FIG. 3 may include as further optional method step, the sub-step 115 of using additional a-prior assumptions and/or additional data input in the acquisition of the non-spectral image data. For example, the non-spectral image data may or may not be normalized using input from external sensors, for instance, an independent thickness measurement of the breast such as the compression height associated with the position of the compression paddle or the thickness and/or shape of the breast determined by optical or other sensors. Other external sensors that may provide additional information to normalize the non-spectral image data include sensors related to the compression force, pressure sensors on the compression paddle or the breast support, or any non-X-ray radiation sensors such as ultrasound sensors, optical sensors, radio-wave sensors, or radar sensors. Further, the non-spectral image data may or may not be subject to image processing operations including detection of a homogeneous area, breast height models, histogram analysis, segmentation or clustering operations, or any other operation with the purpose of converting the pixel values to a physical property of the imaged object.

[0056] According to one embodiment, the non-spectral data in step 114 can be tomosynthesis data. In tomosynthesis, differentiation between different tissue types can be accomplished in three dimensions (3D) by segmentation of the image pixel values on a slice-by-slice basis. Pixel-by-pixel two-dimensional (2D) tissue maps can be calculated by averaging in the depth direction. However, tomosynthesis provides only limited 3D information because the angular range of the acquisitions is limited and the in-plane resolution is generally much better than the depth resolution. Further, the depth resolution is better for structures with a small in-plane extent and is worse for large structures. Hence, the precision of the 3D segmentation will depend on the in-plane (2D) extension of the structure because of the asymmetric shape of the point-spread function. The segmentation will be good only for small structures, corresponding to high-spatial-frequencies. Large structures, corresponding to low-spatial-frequencies will be blurred in the depth direction and the contribution from these structures will be overestimated as they will occupy a larger number of slices than corresponding to their actual extent in the height direction. The limiting case is a structure that occupies the full in-plane field-of-view for which there will be no height information and which is not possible to segment in any of the slices.

[0057] The non-spectral image data acquired in step 114 can be tomosynthesis data acquired by a partial rotation with a C-arm CBCT (cone beam computed tomography) system. In this case, the spectral image data acquired in step 110 can be acquired, for instance, by multiple scans with alternating filters and/or kVp.

[0058] Material properties calculated from the spectral image data and material properties calculated from the non-spectral image data may include normal breast tissue, such as glandular and adipose breast tissue, and skin. Furthermore, material properties may include a contrast agent, such as iodine. In contrast-enhanced imaging the invention could, for instance, improve the measurement of iodine concentration by reducing noise. The invention could also improve visibility of iodine in the breast, enhancing the prevalence of iodine in blood vessels and smaller structures. In addition, or instead of the aforementioned material properties, material properties can include breast lesions, such as cysts, tumors, or micro calcifications.

[0059] A further exemplary embodiment of a method according to the invention is shown in FIG. 4. FIG. 4 refers to the case where the non-spectral image data are acquired as part of the acquisition of the spectral image data in method step 111. As discussed in context of the method steps shown in FIG. 3, also in case of the exemplary embodiment shown in FIG. 4, the acquisition of the non-spectral data can comprise using additional a-priori information and/or additional data input, as indicated by method step 115. Examples are provided in context of the discussion of FIG. 3. From the spectral image data acquired in method step 111, material properties as a function of position are calculated in method step 112. As the non-spectral image data are acquired together with the spectral image data in step 111, the non-spectral data can be processed and material properties can be calculated from these data in method step 116. This method step, 116, can be performed in parallel, before or after method step 112. Eventually, the material properties calculated from the spectral image data in step 112 and calculated from the non-spectral image data in step 116 are combined in method step 118. As already discussed in context with FIG. 3 an optional method step 120 may follow to the method step 118 in FIG. 4. Optional method step 120 thereby refers to an adjustment of a visual representation of the combined material properties from the spectral and the non-spectral image data.

[0060] With reference to FIG. 3 and FIG. 4, the method step 118 can further comprise the following sub step, respectively. The method step 118 of combining the material properties calculated from the spectral and non-spectral image data may comprise the sub-step w of weighting the spectral and the non-spectral image data depending on the spatial frequency. The weighting can be done such that the low-spatial-frequency parts of the spectral image data get a stronger weight than the low-spatial-frequency parts of the non-spectral image data. Further, the weighting can give a stronger weight to the high-spatial-frequency parts of the non-spectral image data as compared to the high-spatial-frequency parts of the spectral image data. For instance, in an embodiment of the invention, weighting can be performed with a linear weighting function, which takes into account the aforementioned stronger weighting of spectral image data for low spatial frequencies and non-spectral image data for high spatial frequencies.

[0061] With further reference to method step 118 in FIG. 3 and FIG. 4, the combination of the material properties calculated from the spectral and from the non-spectral image data as a function of spatial frequency can be carried out in the spatial frequency domain. In this case the following relation may be used in order to obtain combined quantitative image data from the spectral and the non-spectral image data:

I.sub.enhanced=.sup.1(w.sub.spectral(f)(I.sub.spectral(x))+w.sub.non-spectral(f)(I.sub.non-spectral(x)))

I.sub.enhanced denotes the enhanced quantitative image data obtained from combining the spectral and non-spectral image data. Further, I.sub.spectral denotes the spectral image data and I.sub.non-spectral the non-spectral image. The functions w.sub.spectral and w.sub.non-spectral denote spatial-frequency dependent weighting functions and and .sup.1 are the Fourier and inverse Fourier transforms, respectively. A weighting according to the above equation is mathematically equivalent to the following calculation in the spatial domain:

I.sub.enhanced=v.sub.spectral(x)*I.sub.spectral(x)+v.sub.non-spectral(x)*I.sub.non-spectral(x)

[0062] In the latter case, v.sub.spectral(x)=.sup.1(w.sub.spectral(f)) and v.sub.non-spectral(x)=.sup.1 (w.sub.non-spectral(f)) are filter kernels and * denotes the convolution operator. It is noted that the two aforementioned calculations are mathematically equivalent. Differences on the end result can be attributed to numerical precision and quality of the software implementation, and the choice of method might depend on practicalities such as speed of running the software. It should also be noted that other methods exist for processing images as a function of spatial frequency and structure size, for instance wavelet methods and Gaussian/Laplacian pyramid methods. The invention is not limited to any particular method of processing.

[0063] FIG. 5A and FIG. 5B illustrate advantages of the implementation of some of the embodiments described above. Both FIG. 5A and FIG. 5B show maps of volumetric breast density or glandularity, i.e. the fraction of glandular tissue thickness relative the total thickness on a pixel-by-pixel basis. In case of FIG. 5A, the a map of glandularity is shown, which has been calculated from spectral tomosynthesis data by decomposing the image into glandular and adipose thicknesses. Even though the map shows the overall structures well, the amount of detail is limited. In case of the calculations leading to the map in FIG. 5B, both spectral and non-spectral image data have been used and combined according to embodiments of the invention as described above. In particular, the non-spectral data were obtained by segmenting the tomosynthesis volume in 3D on a slice-by-slice basis in order to find the glandular structures and separate these from the adipose background. The segmentation worked well for small structures, but was not reliable for larger structures because of the limited depth resolution. The spectral and non-spectral image data were subsequently combined by weighting with spatial frequencies in such a way that the low-spatial-frequency parts of the spectral data and the high-spatial-frequency parts of the non-spectral image data received stronger weights according to the above described weighting step w. As compared to the image in FIG. 5A derived from spectral image data, the image in FIG. 5B obtained from a combination of spectral and non-spectral image data shows a higher level of detail without increased noise. The latter improved or enhanced image quality is one specific objective of the present invention. It is noted, for completeness, that the images in FIGS. 5A and 5B are processed with a large pixel size (0.40.4 mm).

[0064] While the present invention has been illustrated and described in detail in the drawings and foregoing description, such illustration and description are to be considered illustrative or exemplary and not restrictive. The invention is not limited to the disclosed embodiments. Other variations to the disclosed embodiments can be understood and effected by those skilled in the art in practicing a claimed invention, from a study of the drawings, the disclosure and the dependent claims.

[0065] In the claims, the word comprising does not exclude other elements or steps, and the indefinite article a or an does not exclude a plurality. A single processor or other unit may fulfill the functions of several items re-cited in the claims. Any reference sign in the claims should not be construed as limiting the scope.