U.S. patent application number 14/706759 was filed with the patent office on 2016-11-10 for methods and systems for metal artifact reduction in spectral ct imaging.
The applicant listed for this patent is General Electric Company. Invention is credited to Hewei Gao, Debashish Pal, Kriti Sen Sharma.
Application Number | 20160324499 14/706759 |
Document ID | / |
Family ID | 57222165 |
Filed Date | 2016-11-10 |
United States Patent
Application |
20160324499 |
Kind Code |
A1 |
Sen Sharma; Kriti ; et
al. |
November 10, 2016 |
METHODS AND SYSTEMS FOR METAL ARTIFACT REDUCTION IN SPECTRAL CT
IMAGING
Abstract
Various methods and systems for spectral computed tomography
imaging are provided. In one embodiment, a method comprises
acquiring a first projection dataset and a second projection
dataset, detecting a location of metal in the first projection
dataset, applying corrections to the first and second projection
datasets based on the location of the metal, and displaying an
image reconstructed from the corrected first and second projection
datasets. In this way, metal artifacts may be substantially reduced
in dual or multi-energy CT imaging.
Inventors: |
Sen Sharma; Kriti; (Woburn,
MA) ; Gao; Hewei; (Pewaukee, WI) ; Pal;
Debashish; (Milwaukee, WI) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
General Electric Company |
Schenectady |
NY |
US |
|
|
Family ID: |
57222165 |
Appl. No.: |
14/706759 |
Filed: |
May 7, 2015 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
A61B 6/461 20130101;
A61B 6/032 20130101; G06T 2211/408 20130101; G06T 11/008 20130101;
G06T 2207/10081 20130101; A61B 6/5258 20130101; G06T 11/005
20130101; A61B 6/482 20130101 |
International
Class: |
A61B 6/00 20060101
A61B006/00; G06T 11/00 20060101 G06T011/00; A61B 6/03 20060101
A61B006/03 |
Claims
1. A method, comprising: acquiring a first projection dataset and a
second projection dataset; detecting a location of metal in the
first projection dataset based on a metal threshold; applying
corrections to the first and second projection datasets based on
the location of the metal; and displaying an image reconstructed
from the corrected first and second projection datasets.
2. The method of claim 1, wherein detecting the location of the
metal in the first projection dataset comprises generating a first
metal mask comprising voxels above the metal threshold in a first
backprojection of the first projection dataset.
3. The method of claim 2, further comprising generating a second
metal mask comprising voxels in a second backprojection of the
second projection dataset at a same location of the voxels in the
first metal mask.
4. The method of claim 1, further comprising generating a first
prior image based on the first projection dataset and the location
of the metal, and generating a second prior image based on the
second projection dataset and the first prior image.
5. The method of claim 4, further comprising segmenting the first
prior image into at least two regions based on selected threshold
values.
6. The method of claim 5, wherein generating the second prior image
based on the first prior image comprises generating the second
prior image based on the segmentation of the first prior image.
7. The method of claim 4, wherein generating the second prior image
based on the first prior image comprises transforming pixel values
of the first prior image based on an acquisition energy of the
second projection dataset.
8. The method of claim 4, wherein applying the corrections
comprises generating a first interpolated projection dataset based
on a forward projection of the first prior image, and generating a
second interpolated projection dataset based on a forward
projection of the second prior image.
9. The method of claim 8, wherein applying the corrections further
comprises: calculating a weighting function based on the first
projection dataset and the location of the metal; blending the
first interpolated projection dataset with the first projection
dataset based on the weighting function; and blending the second
interpolated projection dataset with the second projection dataset
based on the weighting function.
10. The method of claim 1, further comprising generating a first
metal image and a second metal image based on the metal
threshold.
11. The method of claim 1, wherein the first projection dataset
comprises a higher energy projection dataset and the second
projection dataset comprises a lower energy projection dataset.
12. A method, comprising: acquiring a first projection dataset and
a second projection dataset; generating a first metal-reduced
projection dataset based on the first projection dataset and a
second metal-reduced projection dataset based on the second
projection dataset and the first projection dataset; decomposing
the first and the second metal-reduced projection datasets into a
first metal-reduced material basis and a second metal-reduced
material basis, and the first and the second projection datasets
into a first material basis and a second material basis;
reconstructing a first metal-reduced material image based on the
first metal-reduced material basis, a second metal-reduced material
image based on the second metal-reduced material basis, a first
material image based on the first material basis, and a second
material image based on the second material basis; generating a
first metal image and a second metal image based on the first
material image and the second material image; generating a first
metal-corrected image by combining the first metal image and the
first metal-reduced material image; generating a second
metal-corrected image by combining the second metal image and the
second metal-reduced material image; and outputting at least one of
the first metal-corrected image, the second metal-corrected image,
and a mono-energetic image comprising a combination of the first
metal-corrected image and the second metal-corrected image.
13. The method of claim 12, wherein generating the first
metal-reduced projection dataset and the second metal-reduced
projection dataset comprises: generating a first metal mask based
on the first projection dataset, and a second metal mask based on
the first metal mask and the second projection dataset; generating
a first prior image based on the first projection dataset and the
first metal mask, and a second prior image based on the first prior
image; interpolating the first projection dataset based on the
first metal mask and the first prior image, and the second
projection dataset based on the second metal mask and the second
prior image; and blending the first interpolated projection dataset
with the first projection dataset to generate the first
metal-reduced projection dataset, and the second interpolated
projection dataset with the second projection dataset to generate
the second metal-reduced projection dataset.
14. The method of claim 13, wherein generating the second metal
mask based on the first metal mask and the second projection
dataset comprises selecting values of the second projection dataset
based on the first metal mask.
15. The method of claim 13, wherein generating the second prior
image based on at least the first prior image comprises
transforming the values of the first prior image to form the second
prior image.
16. The method of claim 13, wherein the first interpolated
projection dataset and the first projection dataset are blended
using a weighting function, the weighting function calculated based
on the first projection dataset and the first metal mask, and
wherein the second interpolated projection dataset and the second
projection dataset are blended using the weighting function.
17. The method of claim 13, wherein generating the first metal
image and the second metal image based on the first material image
and the second material image comprises: generating a
mono-energetic image based on the first material image and the
second material image; generating a binary metal mask by
thresholding the mono-energetic image with a metal threshold
calculated based on the first projection dataset; and generating
the first metal image by multiplying the first material image with
the binary metal mask, and the second metal image by multiplying
the second material image with the binary metal mask.
18. The method of claim 12, wherein the first projection dataset
comprises a higher energy projection dataset and the second
projection dataset comprises a lower energy projection dataset.
19. An imaging system, comprising: an x-ray source that emits a
beam of x-rays toward an object to be imaged, the x-ray source
configured to emit x-rays with a high energy and a low energy; a
detector that receives the x-rays attenuated by the object; a data
acquisition system (DAS) operably connected to the detector; and a
computer operably connected to the DAS and programmed with
instructions in non-transitory memory that when executed cause the
computer to: acquire, via the DAS, a first projection dataset and a
second projection dataset; detect a location of metal in the first
projection dataset; generate a first metal-corrected projection
dataset based on the first projection dataset and the location of
the metal; and generate a second metal-corrected projection dataset
based on the second projection dataset and the location of the
metal.
20. The system of claim 19, further comprising a display, and
wherein the computer is further programmed with instructions in the
non-transitory memory that when executed cause the computer to
output an image to the display, the image reconstructed based on
the first metal-corrected projection dataset and the second
metal-corrected projection dataset.
Description
FIELD
[0001] Embodiments of the subject matter disclosed herein relate to
diagnostic imaging, and more particularly, to metal artifact
reduction for dual energy spectral computed tomography (CT)
imaging.
BACKGROUND
[0002] Dual or multi-energy spectral computed tomography (CT)
systems can reveal the densities of different materials in an
object and generate images acquired at multiple monochromatic x-ray
energy levels. In the absence of object scatter, a system derives
the behavior at a different energy based on a signal from two
regions of photon energy in the spectrum: the low-energy and the
high-energy portions of the incident x-ray spectrum. In a given
energy region of medical CT, two physical processes dominate the
x-ray attenuation: Compton scattering and the photoelectric effect.
The detected signals from two energy regions provide sufficient
information to resolve the energy dependence of the material being
imaged. Detected signals from the two energy regions provide
sufficient information to determine the relative composition of an
object composed of two hypothetical materials.
[0003] In some cases, the presence of metal (e.g., in the form of
metal implants, dental fillings, and so on) may interfere with the
x-ray attenuation, thereby causing metal artifacts in reconstructed
images. For single energy acquisition, there are known metal
artifact reduction algorithms which effectively reduce the presence
of metal artifacts in the reconstructed image.
[0004] However, a simple application of known metal artifact
reduction algorithms to dual energy CT leads to additional image
artifacts. For example, a metal artifact reduction algorithm known
to work well for single energy acquisition may be applied
independently to both the high and the low channels in dual energy
CT. As a result, different amounts of metal correction may occur in
each channel. Material decomposition utilizes data from both
channels, and new artifacts arise due to the inconsistency of metal
correction between the channels.
BRIEF DESCRIPTION
[0005] In one embodiment, a method comprises acquiring a first
projection dataset and a second projection dataset, detecting a
location of metal in the first projection dataset, applying
corrections to the first and second projection datasets based on
the location of the metal, and displaying an image reconstructed
from the corrected first and second projection datasets. In this
way, metal artifacts may be substantially reduced in dual or
multi-energy CT imaging.
[0006] It should be understood that the brief description above is
provided to introduce in simplified form a selection of concepts
that are further described in the detailed description. It is not
meant to identify key or essential features of the claimed subject
matter, the scope of which is defined uniquely by the claims that
follow the detailed description. Furthermore, the claimed subject
matter is not limited to implementations that solve any
disadvantages noted above or in any part of this disclosure.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] The present invention will be better understood from reading
the following description of non-limiting embodiments, with
reference to the attached drawings, wherein below:
[0008] FIG. 1 is a pictorial view of an imaging system according to
an embodiment of the invention.
[0009] FIG. 2 is a block schematic diagram of an exemplary imaging
system according to an embodiment of the invention.
[0010] FIG. 3 is a perspective view of one embodiment of a CT
system detector array.
[0011] FIG. 4 is a perspective view of one embodiment of a CT
detector.
[0012] FIG. 5 is a high-level flow chart illustrating an example
method for metal artifact reduction according to an embodiment of
the invention.
[0013] FIG. 6 is a high-level flow chart illustrating an example
method for guided metal mask generation according to an embodiment
of the invention.
[0014] FIG. 7 is a high-level flow chart illustrating an example
method for guided prior image generation according to an embodiment
of the invention.
[0015] FIG. 8 is a high-level flow chart illustrating an example
method for guided adaptive normalized metal artifact reduction
according to an embodiment of the invention.
[0016] FIG. 9 is a high-level flow chart illustrating an example
method for guided metal image generation according to an embodiment
of the invention.
[0017] FIG. 10 is a pictorial view of a CT system for use with a
non-invasive package inspection system according to an embodiment
of the invention.
DETAILED DESCRIPTION
[0018] The following description relates to various embodiments of
image reconstruction for dual energy spectral imaging. In
particular, methods and systems for metal artifact reduction are
disclosed. The operating environment of the present invention is
described with respect to a sixty-four-slice computed tomography
(CT) system, such as the CT imaging system shown in FIGS. 1-4. As
described herein above, the presence of metal in an object being
imaged (e.g., a patient, packages, etc.) may interfere with x-ray
attenuation during CT imaging, thereby leading to metal artifacts
in reconstructed images of the object. In dual or multi-energy CT
imaging, multiple projection datasets may be acquired, where each
projection dataset corresponds to a different acquisition energy. A
method for dual or multi-energy imaging, such as the method
depicted in FIG. 5, may include applying a metal artifact reduction
algorithm to the multiple projection datasets, where the
application of the metal artifact reduction algorithm to one of the
multiple projection datasets is used to guide the application of
the metal artifact reduction algorithm to the other projection
datasets. In particular, since the attenuation of x-rays through
metal is greater at lower energies than at higher energies, the
projection dataset acquired at the highest energy may be used to
guide metal artifact reduction in the lower energy projection
datasets. The metal artifact reduction algorithm may include
performing guided metal mask generation, as depicted in FIG. 6. The
metal artifact reduction algorithm may further include performing
guided prior image generation, as depicted in FIG. 7. Even further,
the metal artifact reduction algorithm may include performing
guided adaptive normalized metal artifact reduction, as depicted in
FIG. 8. The metal artifact reduction algorithm generates an
estimate of what the image would look like if no metal were
present. However, since physicians may be confused by the absence
of the metal in the final reconstructed image, images containing
only the metal may be generated, as depicted in FIG. 9, so that the
metal can be added back to the corrected images to provide an
accurate image free of metal artifacts. Finally, the metal artifact
reduction algorithm may be utilized for luggage/package inspection
systems, such as the system depicted in FIG. 10.
[0019] Though a sixty-four-slice CT system is described by way of
example, it will be appreciated by those skilled in the art that
the invention is equally applicable for use with other multi-slice
configurations. Moreover, the invention will be described with
respect to the detection and conversion of x-rays. However, one
skilled in the art will further appreciate that the invention is
equally applicable for the detection and conversion of other high
frequency electromagnetic radiation. The invention will be
described with respect to a "third generation" CT scanner, but is
equally applicable with other CT systems as well as vascular and
surgical C-arm systems and other x-ray tomography systems.
[0020] As used herein, the term "metal" is used to denote objects
or voxels in the image corresponding to high x-ray attenuation
properties even if those objects are not metal.
[0021] Referring to FIGS. 1 and 2, a CT imaging system 10 is shown
as including a gantry 12 representative of a "third generation" CT
scanner. Gantry 12 has an x-ray source 14 that projects a beam of
x-rays 16 toward a detector assembly or collimator 18 on the
opposite side of the gantry 12. Detector assembly 18 is formed by a
plurality of detectors 20 and data acquisition system (DAS) 32. The
plurality of detectors 20 sense the projected x-rays that pass
through a medical patient 22, and DAS 32 converts the data to
digital signals for subsequent processing. Each detector 20
produces an analog electrical signal that represents the intensity
of an impinging x-ray beam and hence the attenuated beam as it
passes through the patient 22. During a scan to acquire x-ray
projection data, gantry 12 and the components mounted thereon
rotate about a center of rotation 24.
[0022] Rotation of gantry 12 and the operation of x-ray source 14
are governed by a control mechanism 26 of CT system 10. Control
mechanism 26 includes an x-ray controller 28 that provides power
and timing signals to an x-ray source 14 and a gantry motor
controller 30 that controls the rotational speed and position of
gantry 12. An image reconstructor 34 receives sampled and digitized
x-ray data from DAS 32 and performs high speed reconstruction. The
reconstructed image is applied as an input to a computer 36 which
stores the image in a mass storage device 38.
[0023] Computer 36 also receives commands and scanning parameters
from an operator via console 40 that has some form of operator
interface, such as a keyboard, mouse, voice activated controller,
or any other suitable input apparatus. An associated display 42
allows the operator to observe the reconstructed image and other
data from computer 36. The operator supplied commands and
parameters are used by computer 36 to provide control signals and
information to DAS 32, x-ray controller 28, and gantry motor
controller 30. In addition, computer 36 operates a table motor
controller 44 which controls a motorized table 46 to position
patient 22 and gantry 12. Particularly, table 46 moves patient 22
through a gantry opening 48 of FIG. 1 in whole or in part.
[0024] As shown in FIG. 3, detector assembly 18 includes rails 17
having collimating blades or plates 19 placed therebetween. Plates
19 are positioned to collimate x-rays 16 before such beams impinge
upon, for instance, detector 20 of FIG. 4 positioned on detector
assembly 18. In one embodiment, detector assembly 18 includes 57
detectors 20, each detector 20 having an array size of 64.times.16
of pixel elements 50. As a result, detector assembly 18 has 64 rows
and 912 columns (16.times.57 detectors) which allows 64
simultaneous slices of data to be collected with each rotation of
gantry 12.
[0025] Referring to FIG. 4, detector 20 includes DAS 32, with each
detector 20 including a number of detector elements 50 arranged in
pack 51. Detectors 20 include pins 52 positioned within pack 51
relative to detector elements 50. Pack 51 is positioned on a
backlit diode array 53 having a plurality of diodes 59. Backlit
diode array 53 is in turn positioned on multi-layer substrate 54.
Spacers 55 are positioned on multi-layer substrate 54. Detector
elements 50 are optically coupled to backlit diode array 53, and
backlit diode array 53 is in turn electrically coupled to
multi-layer substrate 54. Flex circuits 56 are attached to face 57
of multi-layer substrate 54 and to DAS 32. Detectors 20 are
positioned within detector assembly 18 by use of pins 52.
[0026] In the operation of one embodiment, x-rays impinging within
detector elements 50 generate photons which traverse pack 51,
thereby generating an analog signal which is detected on a diode
within backlit diode array 53. The analog signal generated is
carried through multi-layer substrate 54, through flex circuits 56,
to DAS 32 wherein the analog signal is converted to a digital
signal.
[0027] As described above, each detector 20 may be designed to
directly convert radiographic energy to electrical signals
containing energy discriminatory or photon count data. Thus, in an
alternate preferred embodiment, each detector 20 includes a
semiconductor layer fabricated from CZT. Each detector 20 also
includes a plurality of metallized anodes attached to the
semiconductor layer. Such detectors 20 may include an electrical
circuit having multiple comparators thereon which may reduce
statistical error due to pileup of multiple energy events.
[0028] Referring back to FIGS. 1 and 2, a discussion is now
presented in connection with a decomposition algorithm. An image or
slice is computed which may incorporate, in certain modes, less or
more than 360 degrees of projection data to formulate an image. The
image may be collimated to desired dimensions using tungsten blades
in front of the x-ray source and different detector apertures. A
collimator typically defines the size and shape of the beam of
x-rays 16 that emerges from the x-ray source 14, and a bowtie
filter may be included in the system 10 to further control the dose
to the patient 22. A typical bowtie filter attenuates the beam of
x-rays 16 to accommodate the body part being imaged, such as head
or torso, such that, in general, less attenuation is provided for
x-rays passing through or near an isocenter of the patient 22. The
bowtie filter shapes the x-ray intensity during imaging in
accordance with the region-of-interest (ROI), field of view (FOV),
and/or target region of the patient 22 being imaged.
[0029] As the x-ray source 14 and the detector array 18 rotate, the
detector array 18 collects data of the attenuated x-ray beams. The
data collected by the detector array 18 undergoes pre-processing
and calibration to condition the data to represent the line
integrals of the attenuation coefficients of the scanned object or
the patient 22. The processed data are commonly called
projections.
[0030] In dual or multi-energy imaging, two or more sets of
projection data are typically obtained for the imaged object at
different tube peak kilovoltage (kVp) levels, which change the peak
and spectrum of energy of the incident photons comprising the
emitted x-ray beams or, alternatively, at a single tube peak
kilovoltage (kVp) level or spectrum with an energy resolving
detector of the detector array 18. Regarding terminology, a set of
projection data obtained at a higher tube kVp level may be
interchangeably referred to herein as a high kVp dataset or a high
energy dataset, while a set of projection data obtained at a lower
tube kVp level may be interchangeably referred to herein as a low
kVp dataset or a low energy dataset.
[0031] The acquired sets of projection data may be used for basis
material decomposition (BMD). During BMD, the measured projections
are converted to a set of density line-integral projections. The
density line-integral projections may be reconstructed to form a
density map or image of each respective basis material, such as
bone, soft tissue, and/or contrast agent maps. The density maps or
images may be, in turn, associated to form a volume rendering of
the basis material, for example, bone, soft tissue, and/or contrast
agent, in the imaged volume.
[0032] Once reconstructed, the basis material image produced by the
CT system 10 reveals internal features of the patient 22, expressed
in the densities of the two basis materials. The density image may
be displayed to show these features. In traditional approaches to
diagnosis of medical conditions, such as disease states, and more
generally of medical events, a radiologist or physician would
consider a hard copy or display of the density image to discern
characteristic features of interest. Such features might include
lesions, sizes and shapes of particular anatomies or organs, and
other features that would be discernable in the image based upon
the skill and knowledge of the individual practitioner.
[0033] In addition to a CT number or Hounsfield value, an energy
selective CT system can provide additional information related to a
material's atomic number and density. This information may be
particularly useful for a number of medical clinical applications,
where the CT number of different materials may be similar but the
atomic number may be quite different. For example, calcified plaque
and iodine-contrast enhanced blood may be located together in
coronary arteries or other vessels. As will be appreciated by those
skilled in the art, calcified plaque and iodine-contrast enhanced
blood are known have distinctly different atomic numbers, but at
certain densities these two materials are indistinguishable by CT
number alone.
[0034] A decomposition algorithm is employable to generate atomic
number and density information from energy sensitive x-ray
measurements. Multiple energy techniques comprise dual energy,
photon counting energy discrimination, dual layered scintillation
and/or one or more other techniques designed to measure x-ray
attenuation in two or more distinct energy ranges. As an example, a
compound or mixture of materials measured with a multiple energy
technique may be represented as a hypothetical material having the
same x-ray energy attenuation characteristics. This hypothetical
material can be assigned an effective atomic number Z. Unlike the
atomic number of an element, effective atomic number of a compound
is defined by the x-ray attenuation characteristics, and it needs
not be an integer. This effective Z representation property stems
from a well-known fact that x-ray attenuation in the energy range
useful for diagnostic x-ray imaging is strongly related to the
electron density of compounds, which is also related to the atomic
number of materials.
[0035] The basis for the present disclosure is the fact that high
energy photons penetrate metal more easily than low energy photons.
As a result, high energy projection datasets include fewer metal
artifacts in comparison to low energy projection datasets. For this
reason, the location of metal may be more accurately determined in
the high energy projection datasets, since there are fewer metal
artifacts obscuring the location of metal. As described further
herein, a method for metal artifact reduction in spectral CT
imaging may comprise applying metal artifact reduction to each
projection dataset, where the application of metal artifact
reduction to the highest energy projection dataset is used to guide
the application of metal artifact reduction to the lower energy
projection datasets. In this way, metal artifact reduction may be
consistently applied to a plurality of projection datasets, thereby
reducing (or substantially eliminating) the generation of new
artifacts during material decomposition in dual or multi-energy CT
imaging.
[0036] FIG. 5 is a high-level flow chart illustrating an example
method 500 for metal artifact reduction according to an embodiment
of the invention. In particular, method 500 relates to guided metal
artifact reduction for spectral (i.e., dual or multi-energy)
imaging, wherein metal artifact reduction applied to a first
projection dataset is used to guide metal artifact reduction
applied to a second projection dataset. As described further
herein, the first projection dataset may correspond to a high
energy projection dataset while the second projection dataset may
correspond to a low energy projection dataset. Method 500 may be
described with reference to the system and components shown in
FIGS. 1-4, however the method may be applied to other systems
without departing from the scope of the present disclosure.
[0037] Method 500 may begin at 505. At 305, method 300 may include
acquiring a first projection dataset and a second projection
dataset. For example, the first projection dataset may comprise a
high energy projection dataset and the second projection dataset
may comprise a low energy projection dataset. The first and second
projection datasets may be acquired using any dual or multi-energy
CT imaging technology, including but not limited to fast kV
switching, two-tube two-detector (2T2D), dual layer, rotate-rotate,
photon counting, and so on. After acquiring the datasets, method
500 may continue to 510.
[0038] At 510, method 500 may include preparing the first and
second projection datasets for processing. Preparing the first and
second projection datasets for processing may include, as
non-limiting examples, time-aligning views between the first and
second projection datasets, interpolating missing data, applying
gain normalization, applying data corrections for detector
artifacts, and so on. After preparing the datasets for processing,
method 500 may continue to 515.
[0039] At 515, method 500 may include determining if metal is
present in the field of view (FOV). For example, a metal implant
may be located within the patient 22 and within the FOV scanned by
the imaging system 10. In such an example, the metal may reflect or
otherwise interfere with the transmission of x-rays 16 through the
metal, thereby introducing metal artifacts in images reconstructed
from the acquired projections. In one example, determining if metal
is present in the FOV may comprise determining if an operator has
indicated, via the operator console 40, that metal is present
within the FOV. Additionally or alternatively, determining if metal
is present in the FOV may comprise detecting metal within the body.
Such metal detection may be carried out prior to the acquisition
scan, for example during a scout scan or prior to the patient 22
laying on the table 46. As described further herein, metal artifact
reduction (MAR) may be applied to the acquired projection data to
substantially reduce metal artifacts in images reconstructed from
the acquired projection data. Determining if metal is present in
the FOV therefore determines whether or not the method 500 will
apply MAR to the acquired projection data.
[0040] In some examples, the FOV may comprise the target field of
view (TFOV). For example, in cardiac imaging, a high-resolution
image of a small sub-region of the patient's anatomy may be
desired. In such an example, if the entire patient is scanned, the
full field of view (FFOV) comprises the all acquired projection
data, while the TFOV may only comprise a subset of the acquired
projection data, for example the subset may include the heart of
the patient. If metal is present in the FFOV but not in the TFOV,
for example if the patient has a metal implant in his or her leg
but the TFOV is centered on the patient's heart, metal artifacts
may not occur in the TFOV despite the presence of metal in the
FFOV. In such examples, determining if metal is present in the FOV
may comprise determining if metal is present in the TFOV. However,
in some examples the FOV may comprise the FFOV. For example, if the
patient has a metal implant in his or her torso but the TFOV is
centered on the heart, metal artifacts may occur in the TFOV even
if the metal is present outside of the TFOV. In such examples,
determining if metal is present in the FOV may comprise determining
if metal is present in the FFOV. In yet other examples, determining
if metal is present in the FOV may comprise determining if metal is
present in the FFOV, regardless of the proximity of the metal to
the TFOV.
[0041] If metal is present in the FOV, method 500 may continue to
both 520 and 540. Note that method 500 includes two branches
performed in parallel. Steps 520 through 530 comprise a non-MAR
branch wherein metal images are generated and no metal artifact
reduction is performed, while steps 540 through 565 comprise a MAR
branch wherein MAR images are generated and metal artifact
reduction is performed.
[0042] At 540, method 500 may include performing guided metal mask
generation to generate a metal mask and a metal trace for each
projection dataset. A metal mask comprises a projection dataset
comprising views including metal, while a metal trace comprises an
image dataset comprising views including metal. As described
further herein, the metal trace may be generated by backprojecting
the metal mask. Generating a metal mask may comprise performing
simple thresholding on a (processed) projection dataset, such as a
projection dataset acquired at 505 and processed at 510. For
example, for dual energy imaging wherein a higher energy projection
dataset and a lower energy projection dataset are acquired, a first
metal mask may be generated by applying simple thresholding to the
higher energy projection dataset with a first metal threshold. A
second metal mask may then be generated based on the first metal
mask and the lower energy projection dataset. In particular, the
second metal mask may select values in the lower energy projection
dataset corresponding to the positions of the first metal mask. A
method for performing guided metal mask generation is described
further herein with regard to FIG. 6.
[0043] At 545, method 500 may include performing guided prior image
generation to generate a prior image for each projection dataset.
Performing guided prior image generation may comprise generating a
first prior image based on the first projection dataset, and then
generating a second prior image based on the second projection
dataset and the first prior image. A method for performing guided
prior image generation is described further herein and with regard
to FIG. 7.
[0044] At 550, method 500 may include applying normalized metal
artifact reduction (NMAR) to each prior image. NMAR comprises a
method for replacing corrupted sinogram samples in an original
projection dataset. For example, the first projection dataset is
normalized by a forward projection of the first prior image to
create a first normalized projection dataset. Then, the corrupted
samples in the first projection dataset are replaced (or inpainted)
using linear interpolation on the first normalized projection
dataset. The location of the corrupted samples in the first
projection dataset may be determined using the first metal trace.
Finally, the linearly-interpolated projection dataset is then
de-normalized with the forward projection of the first prior image.
The first de-normalized projection dataset, hereinafter referred to
as the first NMAR projection dataset, comprises values from the
forward-projected first prior image in the region corrupted by
metal.
[0045] The second projection dataset is similarly normalized by a
forward projection of the second prior image, corrupted samples in
the normalized projection dataset are inpainted using linear
interpolation in the regions of the second normalized projection
dataset indicated by the second metal mask, and the
linearly-interpolated projection dataset is then de-normalized with
the forward projection of the second prior image, thereby creating
a second NMAR projection dataset.
[0046] In some examples, the metal trace may be subtracted from the
projection dataset to create a metal-free projection dataset (i.e.,
a projection dataset with metal-filled regions removed rather than
replaced), and NMAR may be applied to the metal-free projection
dataset rather than the projection dataset itself. The NMAR
projection datasets comprise an approximation of what the original
projection datasets would look like if metal were not present in
the patient, and feature a high resolution since the NMAR
projection datasets are based on the original projection datasets,
which have the highest resolution possible. However, resolution of
the projections near the metal may be degraded due to the
interpolation, so after applying NMAR method 500 may proceed to
555.
[0047] At 555, method 500 may include performing guided adaptive
NMAR (ANMAR) with guided ANMAR weighting to generate ANMAR
projection datasets. ANMAR may be utilized to improve resolution in
the vicinity of metal. ANMAR comprises blending the NMAR projection
datasets with the difference of original views (i.e., projection
datasets) and the metal trace using a weighting function. The
weighting function may be generated based on the first NMAR
projection dataset and the first difference between the first
projection dataset and the first metal trace, and may be used to
perform ANMAR for both the first NMAR projection dataset and the
second NMAR projection dataset. In this way, the application of
ANMAR in the second channel (specifically, to the second NMAR
projection dataset) may be guided by the application of ANMAR in
the first channel (specifically, to the first NMAR projection
dataset). A method for performing guided ANMAR is described further
herein with regard to FIG. 8.
[0048] After performing ANMAR to generate the first and second
ANMAR projection datasets, the metal artifact reduction algorithm
is complete. That is, the ANMAR projection datasets comprise
fully-corrected (with respect to metal artifacts) metal-free
projection datasets.
[0049] At 560, method 500 may include performing material
decomposition based on the ANMAR projection datasets. Performing
material decomposition comprises decomposing the ANMAR projection
datasets into a first material basis and a second material basis.
Decomposition may be performed using, for example, basis material
decomposition (BMD) wherein the projections are converted to a set
of density line-integral projections as described herein above and
known in the art. The material bases may comprise, for example, a
water basis and an iodine basis. In other examples, the material
bases may comprise different combinations of materials.
[0050] At 565, method 500 may include performing TFOV
backprojection for each material basis to generate MAR material
basis images. Performing TFOV backprojection may comprise applying
an analytic reconstruction technique, such as filtered back
projection (FBP), to the TFOV.
[0051] Thus method 500 includes the generation of MAR images, which
approximate what an uncorrected image would look like if no metal
were present. In other words, the MAR images are, at least for the
most part, free of metal and metal artifacts. However, since the
metal is present, an end user (e.g., an imaging operator, a
physician, a patient, etc.) expects to see the metal when reviewing
the final image. As described further herein, metal images may be
generated and superimposed over the MAR images to create accurate
images of the scanned volume without metal artifacts.
[0052] Returning to 515, recall that if metal is present, method
500 also proceeds to 520. At 520, method 500 may include performing
material decomposition (MD) based on the first and second
projection datasets. Performing MD comprises decomposing the first
and second projection datasets into a first non-MAR material basis
and a second non-MAR material basis. Decomposition may be performed
using, for example, BMD as described above. The material bases may
comprise, for example, a water basis and an iodine basis. In other
examples, the material bases may comprise different combinations of
materials.
[0053] After performing MD, method 500 may continue to 525. At 525,
method 500 may include performing TFOV backprojection for each
material basis. For example, FBP may be applied to the first
non-MAR material basis and the second non-MAR material basis to
generate, respectively, a first non-MAR material basis image and a
second non-MAR material basis image. Note that the first non-MAR
material basis image and the second non-MAR material basis image
may not directly correspond to the first projection dataset and the
second projection dataset, but instead each non-MAR material basis
image may be based on a combination of the first projection dataset
and the second projection dataset.
[0054] After generating the non-MAR material basis images, method
500 may continue to 530. At 530, method 500 may include performing
guided metal image generation. A method for performing guided metal
image generation is described further herein with regard to FIG. 9.
The method may include, as a non-limiting example, generating a
mono-energetic image based on the first non-MAR material basis
image and the second non-MAR material basis image. The method may
further include generating a third metal mask by thresholding the
mono-energetic image with the first metal threshold, where the
first metal threshold is the same metal threshold used to generate
the first metal mask at 540. The method may further include
generating a binary metal mask by converting the third metal mask
to a binary format. After generating the binary metal mask, the
method may further include generating a first metal image by
multiplying the binary metal mask and the first non-MAR material
basis image, and generating a second metal image by multiplying the
binary metal mask and the second non-MAR material basis image. The
first and second metal images thus comprise only the metal portions
of the first and second non-MAR material basis images.
[0055] At 535, method 500 may include determining if metal is
present in the FOV. Since metal was already determined to be
present at 515, method 500 may continue to 570.
[0056] At 570, method 500 may include combining metal images and
MAR images. In particular, the first and second metal images
generated at 530 may be combined respectively with the first and
second MAR images generated at 565. In this way, the combined
images may include the metal present in the FOV, as expected by the
imaging system operator and/or physician, but without metal
artifacts arising from the metal. Further, since the metal artifact
reduction on each channel (i.e., the first and the second channel,
or the high and the low energy channel) is synchronized, no
additional artifacts are introduced during material
decomposition.
[0057] At 575, method 500 may include performing post-processing on
the combined images. Post-processing may include any additional
artifact reduction algorithms, alignment, stitching, and so on. In
examples where metal is not present in the FOV, post-processing may
include generating a mono-energetic image based on the non-MAR
material basis images. Generating a mono-energetic image may
comprise, as an example, generating a weighted sum of the non-MAR
material basis images. In examples where metal is present in the
FOV, post-processing may include generating a mono-energetic image
based on the combined images (i.e., the images comprising a
combination of metal and MAR images). Generating a mono-energetic
image may comprise generating a weighted sum of the combined
images.
[0058] Finally, at 580, method 500 may include outputting the
post-processed images. Outputting the post-processed images may
comprise outputting the images to a display, such as display 42,
for review by an imaging system operator and/or a physician.
Outputting the post-processed images may also comprise outputting
the images to non-transitory memory, such as mass storage 38, for
subsequent retrieval and review. Method 500 may then end.
[0059] Returning to 515, if no metal is present, method 500 may
continue to 520. At 520, method 500 may include performing material
decomposition (MD) based on the first and second projection
datasets. As described above, performing MD comprises decomposing,
via BMD for example, the first and second projection datasets into
a first material basis and a second material basis.
[0060] Continuing at 525, method 500 may include performing TFOV
backprojection for each material basis to generate a first material
basis image and a second material basis image.
[0061] At 530, method 500 may optionally include performing guided
metal image generation. However, since no metal is present in the
FOV, there is no reason to perform guided metal image generation
and therefore action 530 may not be performed.
[0062] At 535, method 500 may include determining if metal is
present in the FOV. Since no metal is present, method 500 may
continue to 575. At 575, method 500 may include performing
post-processing on the material basis images. As described above,
post-processing may include performing any additional artifact
reduction algorithms, alignment, stitching, and so on. In some
examples, post-processing may include generating a mono-energetic
image based on the material basis images.
[0063] Finally, at 580, method 500 may include outputting the
post-processed image. Outputting the post-processed images may
comprise outputting the images to a display, such as display 42,
for review by an imaging system operator and/or a physician.
Outputting the post-processed images may also comprise outputting
the images to non-transitory memory, such as mass storage 38, for
subsequent retrieval and review. Method 500 may then end. In this
way, metal artifact reduction may not be applied to the projection
datasets when no metal is present in the FOV.
[0064] Thus, a method for metal artifact reduction in dual or
multi-energy spectral computed tomography imaging is provided. The
method may include guiding an application of metal artifact
reduction to a lower energy projection dataset based on an
application of metal artifact reduction to a higher energy
projection dataset, so that the metal artifact reduction may be
consistently applied in each channel. While a general and
high-level description of guided metal artifact reduction is
described herein above with regard to FIG. 5, several subroutines
for guided metal artifact reduction are further described herein
with respect to FIGS. 6 through 9.
[0065] FIG. 6 is a high-level flow chart illustrating an example
method 600 for guided metal mask generation according to an
embodiment of the invention. In particular, method 600 relates to
generating a first metal mask for a first projection dataset, and
using the first metal mask to guide the generation of a second
metal mask for a second projection dataset. Method 600 may comprise
a subroutine of the method 500 depicted in FIG. 5. In particular,
method 600 may comprise the action 540 of method 500. Method 600
may be described with reference to the system and components shown
in FIGS. 1-4, however the method may be applied to other systems
without departing from the scope of the present disclosure.
[0066] Method 600 begins at 605. At 605, method 600 may include
performing full field of view (FFOV) backprojection to generate a
first and a second uncorrected image based on the first and second
projection datasets.
[0067] At 610, method 600 may include generating a first metal mask
based on the first uncorrected image. Generating the first metal
mask may comprise segmenting the first uncorrected image using a
threshold value to indicate metal voxels within the first
uncorrected image. The segmentation generates the first metal mask
by applying a metal threshold to the first uncorrected image and
inserting any voxels above the metal threshold value into the first
metal mask. The metal threshold may comprise a first metal
threshold, where the first metal threshold comprises a metal
threshold applicable to the first projection dataset. Pseudocode
for generating the first metal mask is depicted at 612. For each
i.sup.th voxel in the first uncorrected image (FFOV.sub.first), if
the value of the first uncorrected image at a given voxel is
greater than the first metal threshold (i.e., if
FFOV.sub.first(i)>T.sub.metal,first), then the first metal mask
at that voxel is set to the value of the first uncorrected image at
that voxel (i.e., mask.sub.first(i)=FFOV.sub.first(i)). Otherwise,
for example if the value of the first uncorrected image at that
voxel is less than the first metal threshold, then the value of the
first metal mask at that voxel is set to zero (i.e.,
mask.sub.first(i)=0).
[0068] After generating the first metal mask, method 600 may
continue to 615. At 615, method 600 may include generating a second
metal mask based on the first metal mask and the second uncorrected
image. Pseudocode for generating the second metal mask is depicted
at 617. For each i.sup.th voxel in the image, if the first metal
mask has a non-zero value (i.e., if mask.sub.first(i)>0), then
the second metal mask at that voxel is set to the value of the
second uncorrected image at that voxel (i.e.,
mask.sub.second(i)=FFOV.sub.second(i)). Otherwise, for example if
the first metal mask has a zero value, then the second metal mask
at that voxel is set to zero (i.e., mask.sub.second(i)=0). In this
way, the generation of the second metal mask is guided by the first
metal mask. That is, the location of the metal mask in both the
first and the second channels are the same although the values in
each may differ.
[0069] At 620, method 600 may include performing a forward
projection on the first metal mask and the second metal mask to
respectively generate a first metal trace and a second metal trace.
Specifically, the metal voxels, which may be indicated by non-zero
values in the metal masks, are projected onto the detector for a
given source position. Those detector pixels or dexels that have a
non-zero contribution from the forward projection are labeled as
metal detector pixels or metal dexels. Method 600 may then
return.
[0070] FIG. 7 is a high-level flow chart illustrating an example
method 700 for guided prior image generation according to an
embodiment of the invention. In particular, method 700 relates to
generating a first prior image, and using the first prior image to
guide the generation of a second prior image. Method 700 may
comprise a subroutine of the method 500 depicted in FIG. 5. For
example, method 700 may comprise the action 545 of the method 500.
Method 700 may be described with reference to the system and
components shown in FIGS. 1-4 and the method shown in FIG. 5,
however the method may be applied to other systems and methods
without departing from the scope of the present disclosure.
[0071] Method 700 may begin at 705. At 705, method 700 may include
performing projection completion to generate a first and a second
first-pass corrected projections based on respective metal traces
and projection datasets. Projection completion comprises the
removal of metal from the projection dataset using the metal trace.
In some examples, projection completion may comprise an
interpolation in the original projection dataset at the location of
the metal trace.
[0072] After projection completion, method 700 may continue to 710.
At 710, method 700 may include performing FFOV backprojections to
generate a first and a second first-pass corrected images based on
the first and second first-pass corrected projections.
[0073] At 715, method 700 may include applying edge-preserving
smoothing to the first and second uncorrected images guided by the
first metal mask. For example, a Gaussian may be applied to the
uncorrected images.
[0074] At 720, method 700 may include performing image blending to
generate first and second blended images based on the first and
second first-pass corrected images and the first and second
smoothed uncorrected images guided by the first metal mask. In one
example, the first-pass corrected images and the smoothed
uncorrected images may be combined in the image domain. For
example, the first first-pass corrected image and the first
smoothed uncorrected image may be separately passed through one or
more banks of filters. The filtered first first-pass corrected
image may then be combined with the filtered first smoothed
uncorrected image using weights generated based on the first metal
mask. Specifically, these weights may be spatially varying as a
function of the distance from the metal, where the distance from
the metal is based on the first metal mask. The second first-pass
corrected image and the second smoothed uncorrected image may be
similarly filtered and combined using the same weights generated
based on the first metal mask. The multi-band filtering and the
blending of the first-pass corrected images and the smoothed
uncorrected images may be carried out in frequency space. To that
end, in some examples, performing image blending may include
transforming the images to the frequency space, applying the
multi-band filtering and the weighted combination, and then
transforming the frequency-space images back to the image
domain.
[0075] At 725, method 700 may include generating a first prior
image using segmentation based on the first blended image and the
first first-pass corrected image. First, simple thresholding may be
applied to segment air and soft tissue in the first blended image.
It should be appreciated that the use of air and soft tissue is
exemplary, and in some examples, the first blended image may be
segmented based on materials other than air and soft tissue.
[0076] After thresholding the first blended image, additional
corrections may be applied to the segmented first blended image in
order to generate the first prior image. These additional
corrections may also reduce the presence of metal artifacts. For
example, the first blended image includes information from the
first uncorrected image. In cases where the metal artifacts are
very high, such artifacts can propagate through from the
uncorrected image and show up in the first blended image and the
segmented first blended image. At least some of these artifacts can
be removed based on the first first-pass corrected image and a set
of selection rules. For example, if a voxel is segmented as air in
the first blended image but not in the first first-pass corrected
image, this is possibly due to high metal artifacts propagating
into the first blended image from the original image (i.e., the
first uncorrected image). Hence, in such cases, it is better to
trust the segmentation of the first first-pass corrected image.
Segmented tissue regions may be similarly compared between the
segmented first blended image and the first first-pass corrected
image. Furthermore, for all other voxels, the prior image is equal
to the segmentation result of the first blended image. In this way,
the first prior image may be generated.
[0077] At 730, method 700 may include generating a second prior
image based on the second blended image and the first prior image
segmentation. For example, the segmentation of the first prior
image may be applied to the second blended image. In some examples,
the segmentation of the first prior image may include the
corrections determined based on the first first-pass corrected
image, as described herein above. Pseudocode for generating the
second prior image is depicted at 732. For each i.sup.th voxel in
the image, if the first prior image at the voxel includes an air
value (i.e., if prior.sub.first(i)=AirVal), then the second prior
image at that voxel is set to the air value at that voxel (i.e.,
prior.sub.second(i)=AirVal(i)). Otherwise, if the first prior image
at the i.sup.th voxel includes a tissue value (i.e., if
prior.sub.first(i)==TissueVal), then the second prior image at that
voxel is set to the tissue value at that voxel (i.e.,
prior.sub.second(i)=TissueVal(i)). Otherwise, the second prior
image at the i.sup.th voxel may be set to the second blended image
at that voxel (i.e.,
prior.sub.second(i)=blended.sub.second(i)).
[0078] After generating the second prior image, method 700 may then
return. Specifically, method 700 may return the first and the
second prior images to the method 500 described herein above with
regard to FIG. 5.
[0079] FIG. 8 is a high-level flow chart illustrating an example
method 800 for guided ANMAR according to an embodiment of the
invention. Method 800 may comprise a subroutine of method 500. For
example, method 800 may comprise the action 555 of the method 500
depicted in FIG. 5. Method 800 may be described with reference to
the system and components shown in FIGS. 1-4, however the method
may be applied to other systems without departing from the scope of
the present disclosure.
[0080] Method 800 may begin at 805. At 805, method 800 may include
calculating a first difference of the first projection dataset and
the first metal trace. The first difference comprises the first
projection dataset minus the first metal trace. Continuing at 810,
method 800 may include calculating a second difference of the
second projection dataset and the second metal trace. The second
difference comprises the second projection dataset minus the second
metal trace.
[0081] At 815, method 800 may include calculating a weighting
function based on the first projection dataset and the first metal
trace. The weighting function may comprise a piecewise function
which selects a synthetic view (e.g., the first NMAR projection
dataset) in regions where an x-ray passed purely through metal or
purely through non-metal, blends the synthetic view and the
original view in transition regions between the purely metal and
purely non-metal regions. In this way, the resolution in the
vicinity of the metal may be improved. In one example, blending the
synthetic views and the original views may comprise combining the
views based, for example, on a polynomial function.
[0082] In some examples, the weighting function may be calculated
based on the first metal trace. For example, an upper threshold and
a lower threshold may be determined based on the intensity range of
the first metal trace, such that regions in the first metal trace
with intensity values above the upper threshold include metal, and
regions with intensity values below the lower threshold do not
include metal. The weighting function may then blend original views
with the synthetic views in regions between the upper threshold and
the lower threshold, select the synthetic views in regions above
the upper threshold, and select original views in regions below the
lower threshold.
[0083] In another example, the weighting function may be calculated
based on the first difference. In this way, regions containing
metal may be identified by the absence of a view in the first
difference, transition regions may be identified by reduced views
in the first difference compared to the original view, and regions
free of metal may be identified by zero difference between the
first difference and the original view.
[0084] Continuing at 820, method 800 may include generating a first
ANMAR projection dataset based on the first NMAR projection
dataset, the weighting function, and the first difference. In
particular, generating the first ANMAR projection dataset may
comprise inputting both the first NMAR projection dataset and the
first difference into the weighting function, which outputs the
first ANMAR projection dataset. The first ANMAR projection dataset
thus comprises the first NMAR projection dataset with an improved
resolution in the vicinity of metal regions, where views in the
first NMAR projection dataset are blended with views in the first
difference.
[0085] At 825, method 800 may include generating a second ANMAR
projection dataset based on the second NMAR projection dataset, the
weighting function, and the second difference. In particular,
generating the second ANMAR projection dataset may comprise
inputting both the second NMAR projection dataset and the second
difference into the weighting function, which outputs the second
ANMAR projection dataset. The second ANMAR projection dataset thus
comprises the second NMAR projection dataset with an improved
resolution in the vicinity of metal regions, where views in the
second NMAR projection dataset are blended with views in the second
difference. Method 800 may then return.
[0086] FIG. 9 is a high-level flow chart illustrating an example
method 900 for guided metal image generation according to an
embodiment of the invention. Method 900 may comprise a subroutine
of method 500. For example, method 900 may comprise the action 530
of the method 500 of FIG. 5. Method 900 may be described with
reference to the system and components shown in FIGS. 1-4 as well
as the method shown in FIG. 5, however the method may be applied to
other systems and may be used in conjunction with other methods
without departing from the scope of the present disclosure.
[0087] Method 900 may begin at 905. At 905, method 900 may include
generating a mono-energetic image based on the first non-MAR
material basis image and the second non-MAR material basis image.
Generating the mono-energetic image may comprise summing the first
non-MAR material basis image and the second non-MAR material basis
image.
[0088] At 910, method 900 may include generating a third metal mask
by thresholding the mono-energetic image with the first metal
threshold. In particular, the first metal threshold may comprise
the same first metal threshold used to generate the first metal
mask at action 540 of FIG. 5.
[0089] At 915, method 900 may include generating a binary metal
mask by converting the third metal mask to a binary format. For
example, the value of all pixels away from the metal in the third
metal mask may be set to zero while the value of all pixels
including metal may be set to unity.
[0090] At 920, method 900 may include generating a first metal
image by multiplying the binary mask and the first material basis
image. Given the binary format of the binary mask, multiplying the
binary mask and the first non-MAR material basis image sets the
value of all pixels away from the metal in the non-MAR material
basis image to zero, while the value of all pixels including metal
in the non-MAR material basis image remain the same since the value
of the binary mask at those locations is equal to one. Therefore
the first metal image comprises an image that only includes
metal.
[0091] At 925, method 900 may include generating a second metal
image by multiplying the binary mask and the second material basis
image. In particular, the binary mask generated at 915 may be
multiplied with the second non-MAR material basis image to generate
the second metal image as described above at 920.
[0092] At 930, method 900 may include applying image processing to
the first metal image and the second metal image. For example,
image processing operations such as dilation, erosion, smoothing,
and so on may be applied to the metal images to create the most
realistic appearance. Method 900 may then return.
[0093] Although first and second projection datasets are described
herein above with regard to FIGS. 5-9, where the first and second
projection datasets correspond to a higher energy projection
dataset and a lower energy projection dataset, respectively, in
some embodiments, more than two projection datasets may be acquired
at different energies. It should be appreciated that the methods
described herein may be applied to any number of projection
datasets (greater than or equal to two). In examples where more
than two projection datasets are acquired, the first projection
dataset as described herein above may correspond to the highest
energy projection dataset, while the actions applied to the second
projection dataset as described herein above may be applied to each
lower energy projection dataset. In this way, metal artifact
reduction applied to the highest energy projection dataset may be
used to guide metal artifact reduction applied to the lower energy
projection datasets.
[0094] Referring now to FIG. 10, a package/baggage inspection
system 1000 is shown that can use the image acquisition and
reconstruction techniques according to embodiments enclosed and
which includes a rotatable gantry 1002 having an opening 1004
therein through which packages or pieces of baggage may pass. The
rotatable gantry 1002 houses one or more x-ray energy sources 1006
as well as a detector assembly 1008 having scintillator arrays
comprising scintillator cells. A conveyor system 1010 is also
provided and includes a conveyer belt 1012 supported by structure
1014 to automatically and continuously pass packages or baggage
pieces 1016 through opening 1004 to be scanned. Objects 1016 are
passed through opening 1004 by conveyor belt 1012, imaging data is
then acquired, and the conveyor belt 1012 removes the packages 1016
from opening 1004 in a controlled and continuous manner. As a
result, postal inspectors, baggage handlers, and other security
personnel may non-invasively inspect the contents of packages 1016
for explosives, knives, guns, contraband, and so on.
[0095] An implementation of system 10 and/or 1000 in an example
comprises a plurality of components such as one or more of
electronic components, hardware components, and/or computer
software components. A number of such components can be combined or
divided in an implementation of the system 10 and/or 1000. An
exemplary component of an implementation of the system 10 and/or
1000 employs and/or comprises a set and/or series of computer
instructions written in or implemented with any number of
programming languages, as will be appreciated by those skilled in
the art. An implementation of system 10 and/or 1000 in an example
comprises any (e.g., horizontal, oblique, or vertical) orientation,
with the description and figures herein illustrating an exemplary
orientation of an implementation of the system 10 and/or 1000, for
explanatory purposes.
[0096] The technical effect of the disclosure may include the
reduction of metal artifacts in reconstructed images. Another
technical effect of the disclosure may include the reconstruction
of an image from a plurality of projection datasets. Yet another
technical effect of the disclosure may include the display of an
image reconstructed from a plurality of projection datasets,
wherein metal artifacts in the plurality of projection datasets are
reduced and wherein one of the plurality of projection datasets is
used to guide the reduction of metal artifacts in the other
projection datasets. Another technical effect of the disclosure may
include the generation of a metal mask for a lower energy
projection dataset based on a metal mask generated for a higher
energy projection dataset.
[0097] In one embodiment, a method comprises acquiring a first
projection dataset and a second projection dataset, detecting a
location of metal in the first projection dataset, applying
corrections to the first and second projection datasets based on
the location of the metal, and displaying an image reconstructed
from the corrected first and second projection datasets.
[0098] In one example, detecting the location of the metal in the
first projection dataset comprises generating a first metal mask
comprising voxels above a metal threshold in a first backprojection
of the first projection dataset. The method further comprises
generating a second metal mask comprising voxels in a second
backprojection of the second projection dataset at a same location
of the voxels in the first metal mask.
[0099] As another example, the method further comprises generating
a first prior image based on the first projection dataset and the
location of the metal, and generating a second prior image based on
the second projection dataset and the first prior image.
[0100] In another example, the method further comprises segmenting
the first prior image into at least two regions based on selected
threshold values. In such an example, generating the second prior
image based on the first prior image comprises generating the
second prior image based on the segmentation of the first prior
image.
[0101] As another example, generating the second prior image based
on the first prior image comprises transforming pixel values of the
first prior image based on an acquisition energy of the second
projection dataset.
[0102] In one example, applying the corrections comprises
generating a first interpolated projection dataset based on a
forward projection of the first prior image, and generating a
second interpolated projection dataset based on a forward
projection of the second prior image. In yet another example,
applying the corrections further comprises calculating a weighting
function based on the first interpolated projection dataset and the
location of the metal, blending the first interpolated projection
dataset with the first projection dataset based on the weighting
function, and blending the second interpolated projection dataset
with the second projection dataset based on the weighting
function.
[0103] In some examples, the method further comprises generating a
first metal image and a second metal image based on the metal
threshold.
[0104] In one example, the first projection dataset comprises a
higher energy projection dataset and the second projection dataset
comprises a lower energy projection dataset.
[0105] In another embodiment, a method comprises: acquiring a first
projection dataset and a second projection dataset; generating a
first metal-reduced projection dataset based on the first
projection dataset and a second metal-reduced projection dataset
based on the second projection dataset, wherein generating the
second metal-reduced projection dataset is further based on the
first projection dataset; decomposing the first and the second
metal-reduced projection datasets into a first metal-reduced
material basis and a second metal-reduced material basis, and the
first and the second projection datasets into a first material
basis and a second material basis; reconstructing a first
metal-reduced material image based on the first metal-reduced
material basis, a second metal-reduced material image based on the
second metal-reduced material basis, a first material image based
on the first material basis, and a second material image based on
the second material basis; generating a first metal image based on
the first material image and a second metal image based on the
second material image; generating a first metal-corrected image by
combining the first metal image and the first metal-reduced
material image; generating a second metal-corrected image by
combining the second metal image and the second metal-reduced
material image; and outputting at least one of the first
metal-corrected image, the second metal-corrected image, and a
mono-energetic image comprising a combination of the first
metal-corrected image and the second metal-corrected image.
[0106] In one example, generating the first metal-reduced
projection dataset and the second metal-reduced projection dataset
comprises: generating a first metal mask based on the first
projection dataset, and a second metal mask based on the first
metal mask and the second projection dataset; generating a first
prior image based on the first projection dataset and the first
metal mask, and a second prior image based on the first prior
image; interpolating the first projection dataset based on the
first metal mask and the first prior image, and the second
projection dataset based on the second metal mask and the second
prior image; and blending the first interpolated projection dataset
with the first projection dataset to generate the first
metal-reduced projection dataset, and the second interpolated
projection dataset with the second projection dataset to generate
the second metal-reduced projection dataset.
[0107] In one example, generating the second metal mask based on
the first metal mask and the second projection dataset comprises
selecting values of the second projection dataset based on the
first metal mask.
[0108] As another example, generating the second prior image based
on at least the first prior image comprises transforming the values
of the first prior image to form the second prior image.
[0109] In yet another example, the first interpolated projection
dataset and the first projection dataset are blended using a
weighting function, the weighting function calculated based on the
first projection dataset and the first metal mask, and wherein the
second interpolated projection dataset and the second projection
dataset are blended using the weighting function.
[0110] In one example, generating the first metal image and the
second metal image based on the first material image and the second
material image comprises: generating a mono-energetic image based
on the first material image and the second material image;
generating a binary metal mask by thresholding the mono-energetic
image with a metal threshold calculated based on the first
projection dataset; and generating the first metal image by
multiplying the first material image with the binary metal mask,
and the second metal image by multiplying the second material image
with the binary metal mask.
[0111] In some examples, the first projection dataset comprises a
higher energy projection dataset and the second projection dataset
comprises a lower energy projection dataset.
[0112] In yet another embodiment, an imaging system comprises: an
x-ray source that emits a beam of x-rays toward an object to be
imaged, the x-ray source configured to emit x-rays with a high
energy and a low energy; a detector that receives the x-rays
attenuated by the object; and a data acquisition system (DAS)
operably connected to the detector. The imaging system further
comprises a computer operably connected to the DAS and programmed
with instructions in non-transitory memory that when executed cause
the computer to acquire, via the DAS, a first projection dataset
and a second projection dataset, detect a location of metal in the
first projection dataset, generate a first metal-corrected
projection dataset based on the first projection dataset and the
location of the metal, and generate a second metal-corrected
projection dataset based on the second projection dataset and the
location of the metal.
[0113] In one example, the system further comprises a display, and
the computer is further programmed with instructions in the
non-transitory memory that when executed cause the computer to
output an image to the display, the image reconstructed based on
the first metal-corrected projection dataset and the second
metal-corrected projection dataset.
[0114] Note that the example control routines included herein can
be used with various imaging system configurations. The routines
and methods disclosed herein may be stored as executable
instructions in non-transitory memory and may be carried out by the
imaging system including the computer in combination with the
various system components, such as the detector assembly, the
operator console, and so on.
[0115] As used herein, an element or step recited in the singular
and proceeded with the word "a" or "an" should be understood as not
excluding plural of said elements or steps, unless such exclusion
is explicitly stated. Furthermore, references to "one embodiment"
of the present invention are not intended to be interpreted as
excluding the existence of additional embodiments that also
incorporate the recited features. Moreover, unless explicitly
stated to the contrary, embodiments "comprising," "including," or
"having" an element or a plurality of elements having a particular
property may include additional such elements not having that
property. The terms "including" and "in which" are used as the
plain-language equivalents of the respective terms "comprising" and
"wherein." Moreover, the terms "first," "second," and "third," etc.
are used merely as labels, and are not intended to impose numerical
requirements or a particular positional order on their objects.
[0116] This written description uses examples to disclose the
invention, including the best mode, and also to enable a person of
ordinary skill in the relevant art to practice the invention,
including making and using any devices or systems and performing
any incorporated methods. The patentable scope of the invention is
defined by the claims, and may include other examples that occur to
those of ordinary skill in the art. Such other examples are
intended to be within the scope of the claims if they have
structural elements that do not differ from the literal language of
the claims, or if they include equivalent structural elements with
insubstantial differences from the literal languages of the
claims.
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