U.S. patent application number 14/917615 was filed with the patent office on 2017-08-24 for device and method for iterative reconstruction of images recorded by at least two imaging methods.
The applicant listed for this patent is KONINKLIJKE PHILIPS N.V.. Invention is credited to Frank BERGNER, Thomas KOEHLER, Roland PROKSA.
Application Number | 20170243378 14/917615 |
Document ID | / |
Family ID | 51392070 |
Filed Date | 2017-08-24 |
United States Patent
Application |
20170243378 |
Kind Code |
A1 |
KOEHLER; Thomas ; et
al. |
August 24, 2017 |
DEVICE AND METHOD FOR ITERATIVE RECONSTRUCTION OF IMAGES RECORDED
BY AT LEAST TWO IMAGING METHODS
Abstract
The present invention relates to a device (100) for iterative
reconstruction of images recorded by at least two imaging methods,
the device comprising: an extraction module (10), which is
configured to extract a first set of patches from a first image
recorded by a first imaging method and to extract a second set of
patches from a second image recorded by a second imaging method; a
generation module (20), which is configured to generate a set of
reference patches based on a merging of a first limited number of
atoms for the first set of patches and of a second limited number
of atoms for the second set of patches; and a regularization module
(30), which is configured to perform a regularization of the first
image or the second image by means of the generated set of
reference patches.
Inventors: |
KOEHLER; Thomas;
(NORDERSTEDT, DE) ; BERGNER; Frank; (HAMBURG,
DE) ; PROKSA; Roland; (NEU WULMSTORF, DE) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
KONINKLIJKE PHILIPS N.V. |
Eindhoven |
|
NL |
|
|
Family ID: |
51392070 |
Appl. No.: |
14/917615 |
Filed: |
July 29, 2015 |
PCT Filed: |
July 29, 2015 |
PCT NO: |
PCT/EP2015/067363 |
371 Date: |
March 9, 2016 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 11/006 20130101;
G06T 2211/408 20130101; G06T 2211/424 20130101 |
International
Class: |
G06T 11/00 20060101
G06T011/00 |
Foreign Application Data
Date |
Code |
Application Number |
Aug 15, 2014 |
EP |
14181164.6 |
Claims
1. Device for iterative reconstruction of images recorded by at
least two imaging methods, the device comprising: an extraction
module, which is configured to extract a first set of patches from
a first image recorded by a first imaging method and to extract a
second set of patches from a second image recorded by a second
imaging method; a generation module, which is configured to
generate a set of reference patches based on a merging of a first
limited number of atoms for the first set of patches and of a
second limited number of atoms for the second set of patches; and a
regularization module, which is configured to perform a joint
regularization of the first image and the second image by means of
the generated set of reference patches.
2. The device according to claim 1, wherein the extraction module
is configured to extract the first set of patches from an
attenuation image recorded as the first image and to extract the
second set of patches from a phase image recorded as the second
image.
3. The device according to claim 1, wherein the extraction module
is configured to extract the first set of patches from a
photo-electric image recorded as the first image and to extract the
second set of patches from a Compton-scatter image recorded as the
second image.
4. The device according to claim 1, wherein the generation module
is configured to generate the reference patches based on a linear
combination of atoms for the first set of patches and atoms for the
second set of patches.
5. The device according to claim 1, wherein the generation module
is configured to generate the set of reference patches based on an
affine combination, a conical combination, or a convex combination
of the first set of patches and the second set of patches.
6. The device according to claim 1, wherein the extraction module
is configured to extract as the first set of patches 2.times.2
pixel patches or 4.times.4 pixel patches or 8.times.8 pixel patches
or 16.times.16 pixel patches and to extract as the second set of
patches 2.times.2 pixel patches or 4.times.4 pixel patches or
8.times.8 pixel patches or 16.times.16 pixel patches.
7. The device according to one claim 1, wherein the generation
module is configured to generate the set of reference patches in
form of a generic dictionary.
8. The device according to claim 7, wherein the generation module
is configured to generate the generic dictionary comprising base
functions of two-dimensional discrete transformation.
9. A medical imaging system comprising a device according to claim
1.
10. A method for iterative reconstruction of images recorded by at
least two imaging methods, the method comprising the steps of:
extracting a first set of patches from a first image recorded by a
first imaging method and extracting a second set of patches from a
second image recorded by a second imaging method by means of an
extraction module; generating a set of reference patches based on a
merging of a first limited number of atoms for the first set of
patches and of a second limited number of atoms for the second set
of patches by means of a generation module; and performing a joint
regularization of the first image and the second image using the
generated set of reference patches by means of a regularization
module.
11. The method according to claim 10, wherein the step of
Extracting is based on extracting the first set of patches from an
attenuation image recorded as the first image and on extracting the
second set of patches from a phase image recorded as the second
image.
12. The method according to claim 10, wherein the step of
Extracting is based on extracting the first set of patches from an
transmission image recorded as the first image and on extracting
the second set of patches from a Compton-scatter image recorded as
the second image.
13. The method according to claim 10, further comprising the steps
of: generating the set of reference patches based on a linear
combination of the first limited number of atoms and the second
limited number of atoms.
14. The method according to claim 10, further comprising the steps
of: generating the set of reference patches based on an affine
combination, a conical combination, or a convex combination of the
first set of patches and the second set of patches.
15. Computer program comprising a program code for performing the
method according to claim 10, when the computer program runs on a
computer.
Description
FIELD OF THE INVENTION
[0001] The present invention relates to the field of dictionary
based iterative image reconstruction. Particularly, the present
invention relates to a device and a method for iterative
reconstruction of images recorded by at least two imaging
methods.
BACKGROUND OF THE INVENTION
[0002] For supervised image segmentation, and also for image
modeling, iterative image reconstruction is a method used to
reconstruct two-dimensional and three-dimensional images in certain
imaging techniques. For example, in computed tomography an image
may be reconstructed from projections of an object. A common
feature of dual energy X-ray computed tomography, spectral X-ray
computed tomography, and phase-contrast computed tomography is that
the medical imaging system generates two or even more images of an
object with different contrasts with sufficient geometric
alignment.
SUMMARY OF THE INVENTION
[0003] There may be a need to improve devices and methods for
iterative image reconstruction.
[0004] These needs are met by the subject-matter of the independent
claims. Further exemplary embodiments are evident from the
dependent claims and the following description.
[0005] An aspect of the present invention relates to a device for
iterative reconstruction of images recorded by at least two imaging
methods, the device comprising: an extraction module, which is
configured to extract a first set of patches from a first image
recorded by a first imaging method and to extract a second set of
patches from a second image recorded by a second imaging method; a
generation module, which is configured to generate a set of
reference patches based on a merging of a first limited number of
atoms for the first set of patches and of a second limited number
of atoms for the second set of patches; and a regularization
module, which is configured to perform a regularization of the
first image or the second image by means of the generated set of
reference patches.
[0006] The term "patch" as used by the present invention relates to
a subset of pixels of an intra-image area. A patch may comprise a
rectangular shape and may comprise an array of pixels.
[0007] The term "atom" as used by the present invention may refer
to a representative pattern occurring in a recorded image. A
multitude of atoms may be stored in a so-called dictionary.
[0008] In other words, the "merging" or combining of atoms allows
building patches that have a high similarity to the image patches
but without noise or with reduced noise. The term "reference
patches" might be understood as a name for the synthesized patches.
In the reconstruction the reference patch may be used for the
regularization: In the cost function, the reference patch can be
`mixed` into the final image. If the image comprises a noise level
about a certain threshold, more portions are used from the
reference patches. The operational term "merging" includes, but is
not limited to, forming of linear combinations and or scaling
operations and similar.
[0009] The term "limited number" or "limited number of atoms" as
used by the present invention may refer to a number of atoms which
is considered to be suitable and sufficient of a certain task of
image analysis or image processing or image reconstruction. In
particular, "limited number" includes the borderline case for a
single atom and "merging" is to be construe broadly to include
using said single atom.
[0010] The term "at least two imaging methods" may refer to any two
imaging methods or imaging techniques, which may be defined as
representing complementary or supplementary methods or imaging
techniques with respect to each other. For instance, an attenuation
image and a phase image for the case of phase-contrast imaging or a
photo-electric image and a Compton-scatter image for the case of
dual energy imaging may be referred to as complementary or
supplementary methods. The phase image may be regarded as a
complementary or supplementary image with respect to the
attenuation image and correspondingly the methods and techniques
used to capture these images are also referred to as complementary
or supplementary. In yet other words, contrast in the two images
(obtained from the two different imaging methods) stems from
different physical effects or principles.
[0011] A further, second aspect of the present invention relates to
a medical imaging system comprising a device according to the first
aspect or according to any implementation form of the first
aspect.
[0012] A further, third aspect of the present invention relates to
a method for iterative reconstruction of images recorded by at
least two imaging methods, the method comprising the steps of:
Extracting a first set of patches from a first image recorded by a
first imaging method and extracting a second set of patches from a
second image recorded by a second imaging method by means of an
extraction module; Generating a set of reference patches based on a
merging of a first limited number of atoms for the first set of
patches and of a second limited number of atoms for the second set
of patches by means of a generation module; and Performing a
regularization of the first image or the second image using the
generated set of reference patches by means of a regularization
module.
[0013] The present invention advantageously provides a method for
extracting patches from the images, matching the patches using a
linear combination of a limited number of atoms which yields a
reference patch. For example: minimize the squared error between a
patch and the linear combination of only two atoms. The cost
function proposed may prefer that the same atoms are selected in
both e.g. the photo and scatter image. This will prefer images with
similar structures but maybe different scaling. The calculated
reference patches may be assumed to have a high similarity to the
original patches but are almost noiseless or at least
noise-reduced. The reference patches may be used for regularizing
the image reconstruction: If the reconstruction is too noisy a
reducing of the difference between the patches in the iterated
image and the reference patches is performed.
[0014] The present invention advantageously performs a
regularization of the first image or the second image by means of
the generated set of reference patches, in other words, since the
generated set of reference patches is generated by merging a first
limited number of atoms from the first image and a second limited
number of atoms from the second images, similar atoms are used for
both images, which means that at least partially an overlapping of
the set of atoms may occur.
[0015] The term "similar" as used by the present invention may
refer to two geometrical objects if they both have the same shape,
or one has the same shape as the mirror image or an otherwise
transformed image of the other. The term "similar" may further
define that one object can be obtained from the other object by
uniformly scaling, for instance enlarging or shrinking, possibly
with additional translation, rotation and reflection.
[0016] The present invention advantageously provides uses a
database or a dictionary that been already created or is at least
generated elsewhere.
[0017] The present invention advantageously provides a dictionary
based regularization method in the iterative reconstruction
algorithm for computed tomography configurations that generate two
or more images with the same alignment.
[0018] The present invention advantageously provides a standard
dictionary based regularization for typical computer tomography
images, often similar structures and textures appear. A set of
small features is generated from a computer tomography image, for
instance patches of a certain size. The features may be called
atoms and the set of all atoms may be called the dictionary. The
atoms may be computed in a way that a linear combination with a
limited number of atoms forms a good approximation of most patches
of the image. In order to use the dictionary in the regularization,
the image may be partitioned into overlapping patches of the same
size as the atoms and the algorithm seeks for an image where each
of the patches may be reasonably well represented by a small number
of atoms.
[0019] The present invention advantageously provides an adapting
algorithm, wherein patches are extracted from each of the two
images recorded by the two complementary or supplementary imaging
methods, for instance, an attenuation image and a phase image for
the case of phase-contrast imaging or a photo-electric image and a
Compton-scatter image for the case of dual energy imaging. The
present invention advantageously allows representing these two
patches by the same subset of atoms.
[0020] The present invention advantageously allows that an atom
that may be used for one of the at least two recorded images can
also be used without additional effort for the other image recorded
by the second imaging method. The present invention advantageously
allows reconstructing images which have their edges lined up since
these edges are represented by the same atoms.
[0021] The present invention advantageously can be applied to dual
energy X-ray computed tomography with a dual source system or with
a fast kVp switching acquisition for two voltages. Decomposition
may be performed in the image domain, accordingly the two imaging
methods may be defined by X-ray computed tomography with--at least
two--different input X-ray spectra.
[0022] Supplementary or complementary images or methods for the
present invention may also be given by the upper and lower layer
images as acquired by a dual layer system.
[0023] According to an exemplary embodiment of the present
invention, the extraction module is configured to extract the first
set of patches from an attenuation image recorded as the first
image and to extract the second set of patches from a phase image
recorded as the second image. This advantageously allows an
efficient generation of various patches.
[0024] According to an exemplary embodiment of the present
invention, the extraction module is configured to extract the first
set of patches from a photo-electric image recorded as the first
image and to extract the second set of patches from a
Compton-scatter image recorded as the second image.
[0025] This advantageously allows an efficient extraction of
patches from complementary or supplementary images.
[0026] According to an exemplary embodiment of the present
invention, the generation module is configured to generate the set
of the reference patches based on a linear combination of the first
set of patches and the second set of patches.
[0027] According to an exemplary embodiment of the present
invention, the generation module is configured to generate the set
of reference patches based on an affine combination, a conical
combination, or a convex combination of atoms for the first set of
patches and a combination of atoms for second set of patches.
[0028] According to an exemplary embodiment of the present
invention, the extraction module is configured to extract as the
first set of patches 2.times.2 pixel patches or 4.times.4 pixel
patches or 8.times.8 pixel patches or 16.times.16 pixel patches and
to extract as the second set of patches 2.times.2 pixel patches or
4.times.4 pixel patches or 8.times.8 pixel patches or 16.times.16
pixel patches.
[0029] According to an exemplary embodiment of the present
invention, the generation module is configured to generate the set
of reference patches in form of a generic dictionary.
[0030] According to an exemplary embodiment of the present
invention, the generation module is configured to generate the
generic dictionary comprising base functions of two-dimensional
discrete transformations.
[0031] A computer program performing the method of the present
invention may be stored on a computer-readable medium. A
computer-readable medium may be a floppy disk, a hard disk, a CD, a
DVD, an USB (Universal Serial Bus) storage device, a RAM (Random
Access Memory), a ROM (Read Only Memory) or an EPROM (Erasable
Programmable Read Only Memory). A computer-readable medium may also
be a data communication network, for example the Internet, which
allows downloading a program code.
[0032] The methods, systems, and devices described herein may be
implemented as software in a Digital Signal Processor, DSP, in a
micro-controller or in any other side-processor or as a hardware
circuit within an application specific integrated circuit, ASIC,
CPLD or FPGA.
[0033] The present invention can be implemented in digital
electronic circuitry or in computer hardware, firmware, software,
or in combinations thereof, for instance in available hardware of
conventional medical imaging devices or in new hardware dedicated
for processing the methods described herein.
[0034] A more complete appreciation of the invention and the
attendant advantages thereof will be clearly understood by
reference to the following schematic drawings, which are not to
scale, wherein:
[0035] FIG. 1 shows a schematic diagram of a device for iterative
reconstruction of images recorded by at least two imaging methods
according to an exemplary embodiment of the present invention;
[0036] FIG. 2 shows a schematic diagram of a flow-chart diagram for
a method for iterative reconstruction of images recorded by at
least two imaging methods;
[0037] FIG. 3 shows a schematic diagram of a medical imaging system
comprising a device for iterative reconstruction of images recorded
by at least two imaging methods according to an exemplary
embodiment of the present invention;
[0038] FIG. 4 shows an attenuation image recorded by phase-contrast
imaging for explaining the present invention;
[0039] FIG. 5 shows a phase image recorded by phase-contrast
imaging for explaining the present invention;
[0040] FIG. 6 shows an X-ray transmission image;
[0041] FIG. 7 illustrates a dictionary used for dictionary-based
reconstruction;
[0042] FIG. 8 shows a Compton-scatter image recorded by X-ray
imaging for explaining the present invention; and
[0043] FIG. 9 shows a photo-electric image recorded by X-ray
imaging for explaining the present invention.
DETAILED DESCRIPTION OF EMBODIMENTS
[0044] The illustration in the drawings is purely schematic and
does not intend to provide scaling relations or size information.
In different drawings, similar or identical elements are provided
with the same reference numerals. Generally, identical parts,
units, entities or steps are provided with the same reference
symbols in the description.
[0045] Before turning to the reconstruction device and related
method as proposed herein, some principles of dictionary-based
regularization for iterative reconstruction are explained in the
following. An iterative reconstruction may be formulated as a
minimization problem:
min .mu. , .alpha. i w i 2 ( [ A .mu. ] i - l i ) 2 + .lamda. ( s (
E s .mu. - D .alpha. s 2 2 + v s .alpha. s 0 ) ) ##EQU00001##
where the first part is the commonly used data term with A being
the system matrix, .mu. an unknown image, l.sub.i measured line
integrals and w.sub.i some statistical weights. The second part,
the regularization part (weighted with a regularization parameter
.lamda.) contains a sum over all "patches", that is, image parts.
The regularization part represents a "penalty" incurred for each
image during the course of the itertation. A contribution of each
patch 5 to the penalty may contain two terms, the first one may be
the quadratic misfit of a local patch (extracted from an image .mu.
by the so-called extractor matrix E.sub.s) and a best-fitting
linear combination of "atoms" (with .alpha..sub.s being the
coefficient-vector). The second term may be the 0-norm of the
vector as of coefficients, meaning that there is a preference by
the algorithm to represent each patch by a minimum number of atoms.
The very last term in the regularization term can also be
formulated in the form of a constraint which is preferred for some
optimization algorithms. This cost function may be complex to
minimize directly, also because the involved 0-norm implies that
convexity of the cost function is not guaranteed. Therefore, the
cost function is typically minimized by minimizing in an
alternating manner the data term and the regularization term. The
data term is purely quadratic and optimized. FIG. 6 shows an X-ray
transmission image for illustration. More specifically, FIG. 6
shows an example of a sheep lung computed tomography image and a
set of small features, the patches that is,which are in this case
patches of size 8.times.8. The features are called atoms (briefly
referred to above) and the set of all atoms is called a dictionary.
The atoms were computed in this example in a way that a linear
combination forms a "good" approximation of most 8.times.8 patches
of the image. Note that this dictionary may also be a generic
dictionary, for example a dictionary consisting of the base
functions of a two-dimensional discrete cosine transformation. FIG.
7 illustrates a set of atoms. In order to use the dictionary in the
regularization, the image is partitioned into overlapping patches
of the same size as the atoms and the algorithm seeks for an image
where each of the patches can be reasonably well represented by a
small number of atoms.
[0046] Turning now to the device as proposed herein, FIG. 1 shows a
schematic diagram of a device for iterative reconstruction of
images recorded by at least two imaging methods.
[0047] The device 100 for iterative reconstruction of images may
comprise an extraction module 10, a generation module 20, and a
regularization module 30.
[0048] The extraction module 10 may be configured to extract a
first set of patches from a first image recorded by a first imaging
method and to extract a second set of patches from a second image
recorded by a second imaging method.
[0049] The generation module 20 may be configured to generate a set
of reference patches based on a merging of a first limited number
of atoms for the first set of patches and of a second limited
number of atoms for the second set of patches.
[0050] The generation of the set of reference patches may be
performed in a way that the reference patch matches the extracted
patches in a sufficient manner as may be defined by a matching
threshold.
[0051] The regularization module 30 may be configured to perform a
joint regularization of the first image and the second image by
means of the generated set of reference patches.
[0052] According to an embodiment of the present invention an
algorithm is adapted in the following way: Patches are extracted
from each of the two images recorded by at least two methods, the
methods being complementary or supplementary with respect to each
other. For instance, an attenuation image and a phase image is
recorded for phase contrast imaging or a photo-electric image and a
Compton-scatter image is recorded for dual energy imaging.
[0053] Further these two patches which represent the same anatomy
are used to generate a common subset of atoms. This may be
formulated by (taking differential phase contrast imaging as an
example)
min .mu. , .alpha. i w i 2 ( [ A .mu. ] i - l i ) 2 + i w i ' 2 ( [
A ' .delta. ] i - l i ' ) 2 + .lamda. ( s ( E s .mu. - D .alpha. s
2 2 + E s .delta. - D .alpha. s ' 2 2 + v s .alpha. s + .alpha. s '
0 ) ) ##EQU00002##
where we introduced another data term containing the real part of
the refractive index .delta., the differential forward operator A',
and the differential phase line integrals l'.sub.i. Furthermore,
the regularization term contains additionally the quadratic misfit
of the patches taken from the image .delta. and the attenuation
image .mu., the best-fitting linear combination of atoms with
.alpha.'.sub.s and .alpha..sub.s being the respective
coefficient-vectors for the phase and attenuation "channel".
[0054] Both images may be treated completely independently.
However, the enforcement of matching geometry (but not, or not
necessarily, of contrast/scale) is done jointly for the two imaging
channels. In this embodiment, said enforcement is implemented by by
the very last term of above cost function, where the absolute
values of the two vectors of coefficients are first added
component-wise before the 0-norm is taken. By this design, any atom
that is used for one of the images can be used without additional
"cost" by the other image. Consequently, the algorithm will prefer
reconstructed images which have their edges lined up since these
edges are represented by the same atoms.
[0055] FIG. 2 shows a schematic flow-chart diagram of a method for
iterative reconstruction of images recorded by at least two imaging
methods. The method may comprise the following steps.
[0056] As a first step of the method, extracting S1 a first set of
patches from a first image recorded by a first imaging method and
extracting a second set of patches from a second image recorded by
a second imaging method by means of an extraction module 10 may be
performed.
[0057] As a second step of the method, generating S2 a set of
reference patches based on a merging of a first limited number of
atoms for the first set of patches and of a second limited number
of atoms for the second set of patches by means of a generation
module 20 may be performed.
[0058] As a third step of the method, performing S3 a
regularization of the first image or the second image using the
generated set of reference patches by means of a regularization
module 30 may be performed.
[0059] According to a further embodiment of the present invention,
a dual layer X-ray computed tomography system may provide
additionally a high-quality so-called combined image that is
equivalent to a preexisting computed tomography image. The
reconstructed combined image may be also used as the source for the
dictionary. Specifically, the method comprises the steps:
generating a high-quality combined image .mu., reconstructing the
photo-electric image x.sub.p and the Compton or Compton-scatter
image x.sub.C by minimizing:
min x p , x C , .alpha. p , .alpha. C i w p , i 2 ( [ Ax p ] i - l
p , i ) 2 + i w C , i 2 ( [ Ax C ] i - l C , i ) 2 + .lamda. ( s E
s x p - .alpha. p , s E s .mu. 2 2 + E s x C - .alpha. C , s E s
.mu. 2 2 ) ##EQU00003##
[0060] A current patch from the combined image u may be extracted
and used as a single atom for the respective patch in x.sub.p,
x.sub.c and this single atom is scaled for each patch individually
for the photo-electric image and the Compton image with the
best-fitting scalar factor .alpha..sub.p,s and .alpha..sub.C,s.
[0061] The advantages of the embodiment are that no global
dictionary is involved anymore which also avoids the time consuming
and high parametric generation of this dictionary, the complex and
time consuming search for the best fitting subset of atoms is
replaced by a single atom that is known upfront, and the used atom
from the combined image is known to represent the local structure
of the patient accurately.
[0062] The described formula as noted above contains two
independent data terms, i.e., the joint regularization is combined
here with the concept of "single channel spectral MLIR". Of course,
the data term may also contain as an additional term the
correlation between the photo-electric and the Compton line
integrals, which results in the so-called "multi-channel spectral
MLIR". Furthermore, the data term may operate directly on the data
of the upper and lower layer, resulting in the so-called "fully
spectral MLIR".
[0063] FIG. 3 shows a schematic diagram of a medical imaging system
comprising a device for iterative reconstruction of images recorded
by at least two imaging methods according to an exemplary
embodiment of the present invention.
[0064] A medical imaging system 200 may comprise a device 100 for
iterative reconstruction of images recorded by at least two imaging
methods. The medical imaging system 200 may be a dual energy X-ray
computed tomography system with a dual source system or with a fast
kYp switching acquisition for two voltages.
[0065] FIG. 4 shows an attenuation image recorded by phase-contrast
imaging for explaining the present invention.
[0066] A common feature of dual-energy computed tomography,
spectral computed tomography, and phase contrast computed
tomography is that the system generates two or even more images of
an object with different contrasts with perfect geometric
alignment. FIG. 4 illustrates one type of two types of the images
obtained by a phase contrast computed tomography system,
demonstrating also the at least partially complementary contrasts
in the two images. The so-called "attenuation image" is shown in
FIG. 4.
[0067] FIG. 5 shows a phase image recorded by phase-contrast
imaging for explaining the present invention. FIG. 5 shows a second
type of the two types of the images obtained by a phase contrast
computed tomography system. The so-called "phase image" is shown in
FIG. 5.
[0068] FIG. 8 shows a Compton-scatter image recorded by X-ray
imaging for explaining the present invention.
[0069] A common feature of dual-energy X-ray computed tomography
and spectral X-ray computed tomography is that the system generates
two images, for instance, the photo-electric and the
Compton-scatter image or even more images, namely, some additional
images with contrast agent only of an object. These images have
different contrasts with perfect geometric alignment. FIG. 8
illustrates a first one of these two main categories, the so-called
scatter Compton-image.
[0070] FIG. 9 shows a photo-electric image recorded by X-ray
imaging for explaining the present invention.
[0071] FIG. 9 illustrates a second one of these two main
categories, the so-called photo-electric image obtained by a dual
layer X-ray computed tomography system. FIG. 9 and FIG. 8 may
describe a supplementary or complementary images or methods for the
present invention.
[0072] It has to be noted that embodiments of the present invention
are described with reference to different subject-matters. In
particular, some embodiments are described with reference to method
type claims, whereas other embodiments are described with reference
to the device type claims.
[0073] However, a person skilled in the art will gather from the
above and the foregoing description that, unless otherwise
notified, in addition to any combination of features belonging to
one type of the subject-matter also any combination between
features relating to different subject-matters is considered to be
disclosed with this application.
[0074] However, all features can be combined providing synergetic
effects that are more than the simple summation of these
features.
[0075] While the 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 present 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 and practicing the claimed invention, from a study of the
drawings, the disclosure, and the appended claims.
[0076] 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 controller or other unit
may fulfill the functions of several items recited in the claims.
The mere fact that certain measures are recited in mutually
different dependent claims does not indicate that a combination of
these measures cannot be used to advantage. Any reference signs in
the claims should not be considered as limiting the scope.
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