Overview of DICOM WG21 Multi-Energy Imaging Supplement
DICOM WG21
MULTI-ENERGY IMAGING
SUPPLEMENT OVERVIEW
16
-June-2015
Agenda
•
Definitions, Use Cases, Objectives
•
Aspects of Multi-Energy (ME) technologies
•
New types of ME images
•
Proposed Approach
•
Risk and Concerns
ME-Definition
Imaging techniques, including scanning, reconstruction,
processing, when the scanner utilizes multiple energies
from the X-Ray beam spectrum, as opposed to the
conventional CT imaging, when a single (accumulated) X-
Ray spectrum is used.
The existing CT and Enhanced CT (eCT) IODs do not
adequately describe the new CT multi-energy imaging.
Although different vendors apply different scanning and
detection techniques to achieve multi-energy images, there
is large commonality in the generated diagnostic images.
Examples of Multi-Energy CT
Physics background
Clinical Use Cases
The primary potential applications this supplement intends
to focus on include:
•
Allowing better differentiation of materials that look similar
on conventional CT images, e.g., to differentiate Iodine
and Calcium in vascular structures
•
Eliminating acquisitions such as non-contrast acquisition,
when the “virtual/artificial non-contrast” image is
generated from the contrast image
Objectives
When defining this supplement, the following objectives /
goals have been considered:
1.
Making multi-energy information (acquisition,
reconstruction and processing attributes) available to
rendering or processing applications
2.
Facilitating fast and easy adoption of this supplement
across the imaging community, both modalities and
PACS/Displays.
3.
Eliminating (or at least minimizing) the risk of mis-
interpretation when the ME images are displayed by a
non-compliant display, including incorrect
measurements
New aspects of ME technologies
Different vendors apply different technologies for:
•
Scanning
•
Detection
•
Reconstruction
•
Processing
•
Material decomposition
This results in a variety of image types calling for
s
tandardization of parameters and definitions
Virtual Mono-
chromatic
Image (VMI)
Material-
Specific
Image
Material-
Subtracted
Image
Color
Overlay
Image
Color
Blending
Image
Discrete
Labeling
Image
Proportiona
l Map
Image
Iodine Map;
Bone Density
Virt. Non-C;
Virt. Non-Ca
Effective AN
(Z) Image
Electron
Density
Image
Probability
Map Image
Color Map
Image
Multi-Energy Imaging
Material
Quantification
(Decomposition)
Material
Classification
(Labeling)
Material
Visualization
(Color)
Material-
Modified
Image
Highlighted;
Partially-
Suppressed
Color Eff. AN
Gout crystals
on top of CT
image
4 categories
many
flavors
Proposed approach
•
For VMI and Quantification we reuse basic and enhanced CT
IOD‘s for ME images
•
For Classification we consider CT IOD, Segmentation IOD or
Parametric Maps IOD
•
For Visualization we consider reusing SC, Presentation States,
Blending and may be Parametric map or adding RGB palette
information to CT IOD
•
Include the new attributes (add macros) in the existing IOD‘s
•
Add new definitions to the existing standard (like Image Type,
defined terms)
•
Adapt some descriptions to multi-energy related cases
Close to current implementations. Better chance to be
widely/fast adopted
Virtual Mono-chromatic Image (VMI)
40 keV
167 keV
Essentially analogous to a CT image that would be generated
by a monochromatic (of a specific keV value) X-Ray beam
Material-Specific Image
Iodine Map
Image presenting a
physical scale of
specific material. Pixel
values can be in HU or
in equivalent material
concentration (e.g.,
mg/ml).
Material-Subtracted Image
Virtual Non-Contrast Image
Image with one or
more materials
subtracted. Pixel
values may have been
corrected for
displacement of one
material by another
material.
Material-Modified Image
Image where pixel values have been
modified to highlight a certain target
material (either by partially
suppressing the background or by
enhancing the target material), or to
partially suppress the target material.
Unlike Material-Specific and Material-
Subtracted images (that can allow
accurate measurements), the
Material-Modified image is primary
used for better visualization of the
target materials
Bone Marrow Image
Electron Density Image
An image where each
pixel represents a
number of electrons
per unit volume.
Widely used in
radiotherapy.
Effective AN (Z) Image
An image where each
pixel represents
Effective Atomic
Number (aka “Effective
Z”) of that pixel.
Probability Map Image
An image where pixels
describe the probability
that this pixel is
classified as one or
more of the multiple
defined materials
Gout Material Map
Material Visualization (Color)
Users are asking to
visualize ME-content in
certain ways: color maps,
color overlays, blending, etc.
How we intend to extend
•
C.8.2.1 CT Image Module
•
CT Additional X-Ray Source Sequence (0018,9360) (modified)
•
Table X-1 “Optional CT Multi-Energy Macro Attributes" (added)
•
C
T
M
u
l
t
i
E
n
e
r
g
y
A
c
q
u
i
s
i
t
i
o
n
S
e
q
u
e
n
c
e
(
1
C
)
•
>Table X-2 “CT Multi-Energy Acquisition Macro Attributes”
•
C
T
M
u
l
t
i
E
n
e
r
g
y
M
a
t
e
r
i
a
l
D
e
c
o
m
p
o
s
i
t
i
o
n
S
e
q
u
e
n
c
e
(
1
C
)
•
>Table X-3 “CT Multi-Energy Material Decomposition Macro Attributes”
•
C
T
M
u
l
t
i
E
n
e
r
g
y
I
m
a
g
e
S
e
q
u
e
n
c
e
(
1
C
)
•
>Table X-4 “CT Multi-Energy Image Macro Attributes”
•
C.8.15.3.9 CT X-Ray Details Macro (for Enhanced CT
IOD only)
•
CT X-Ray Details Sequence
Risk and Concerns
•
E
n
h
a
n
c
e
m
e
n
t
s
c
a
n
b
e
m
i
s
-
i
n
t
e
r
p
r
e
t
e
d
i
n
k
e
V
i
m
a
g
e
n
e
e
d
f
o
r
c
o
r
r
e
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t
d
i
s
p
l
a
y
l
a
b
e
l
i
n
c
l
u
d
i
n
g
k
e
V
i
m
a
g
e
t
y
p
e
a
n
d
k
e
V
v
a
l
u
e
100 kV (conventional)
50keV (virtual monochomatic)
Risk and Concerns
•
High keV (>150) images can be mis-interpreted that no
contrast was applied (missing contrast information)
•
Potential risk for VNC images (e.g. changing size of small
structures) – currently not broadly validated that VNC is
truly equivalent to TrueNonContrast
•
Quantitative measurements are different in non-contrast
vs. contrast use case (e.g. renal mass and renal cyst)
•
Missing labeling can cause confusion which can slow
down the workflow
delay the diagnosis
Questions
To guarantee that the created ME images contain the necessary
information, a number of new attributes will be defined. These
attributes will be added to the ME images, either as an extension
to the existing IODs, or as part of new, ME-specific, IODs.
However, we cannot mandate using any new specific attribute
for existing IOD. Is there any workaround:
•
Can we say that specific attribute is Type 1 if the system supports ME
imaging?
Yes only for new attributes
;
•
What about standard attributes, like Image Type? Ok to extend with
new values and make them mandatory if the image is ME?
•
Can we say that specific attribute is Type 1C if the units for pixels are
not in HU?
Not for existing attributes; ok for new
•
Shall we rather define an optional SQ with mandatory ME attributes?
•
Image Type of VMI Images. It is defined use ORIGINAL unless there is
a specific case requiring it to be DERIVED.
WG6 recommends leaving
it to the vendor to decide if the image is ORIGINAL or DERIVED.
Questions
To add Real-World Value Mapping to CT IOD to accurately
describe the non-HU values. There are several concerns
with this approach:
•
RWVM is not specified today for CT images or widely implemented
in the field (is this correct?). As a result, the units can be
misinterpreted by the display application
•
Rescale Slope and Rescale Intercept are Type-1 for CT image (if it
is ORIGINAL); there is potential conflict between rescale attributes
and RWVM. If we define image type as DERIVED, we avoid Type-1
requirement for Rescale Slope/Intercept, so they can be omitted.
•
As a consequence, Rescale attributes shall be used if possible; for
specific cases (to be identified) RWVM may be considered.
Questions
•
To describe the need and recommendations for good
labeling in the informative section (e.g., to display keV
value for VMI images; to display material + concentration
for measurements, etc.). DICOM alone cannot enforce
PACS/Workstation to present specific attributes therefore
there is little chance new important attributes we introduce
here will be presented to the users. Should we work with
IHE?
•
Can we “orchestrate” the object in such a way that naïve
display will either present the image adequately or fail to
present anything? For instance, we can put rescale
attributes to zero for non-HU ME images
END OF PRESENTATION
Virtual
Mono-chromatic Image (VMI)
43 HU (160 keV)
307 HU (50 keV)
31 HU (≈ 120kV)
-
=
Contrast VMI Images
12 HU
206
HU
Renal Mass use case
Non-Contrast Image
DETAILS OF
IMPLEMENTATION
ME Acquisition Techniques
•
X-Ray Sources
•
SINGLE_SOURCE
•
MULTI_SOURCE
•
KV Switching
•
NONE, FAST, SLOW
•
Multi-Energy Acquisition
•
SINGLE_SCAN
•
MULTI_SCAN
•
Multi-Energy Detection
•
CONVENTIONAL
•
MULTILAYER
•
PHOTON_COUNTING
•
Technique-Specific Parameters:
•
What do we want to record for Dual-Layer and PhC?
ME Material Decomposition
•
Decomposition Method
•
SINOGRAM_BASED
•
IMAGE_BASED
•
Decomposition Base Materials (sequence)
•
Decomposition Description
•
Vendor-specific label/description
•
Material Attenuation Curves (opt)
•
Decomposition Parameters
•
e.g., dual-energy ratio
Pixel Value Units
•
Method 1: using Rescale
•
Rescale Slope
•
Rescale Intercept
•
Rescale Type
•
Measurement Units as standard Coded Values
•
e.g., UCUM: "mg/cm^3“
•
Method 2 – using Real World Value Mapping
Sequence
•
First/Last Values Mapped
•
Value Slope/Intercept
•
LUT (optional)
•
Measurement Units (Coded Value)
Material Classification (Labeling)
Spectral Imaging Challenges
•
Monochromatic Images
•
Capture keV value
•
How to differentiate from “legacy” CT?
•
Incl. query, display annotations
•
Material Density Images
•
Non-HU values: how to avoid confusion?
•
Several alternatives for solution
•
Segmentation IOD
•
Real-World Mapping
•
PTE-like new IOD
•
Rely on specific Rescale Intercept/Slope/Type
•
Effective Atomic Number Images
•
Similar challenges as for Material Density
•
Using Color mapping prohibits measurements and analysis
Open Issues (some)
•
Assess risks ME images been misinterpreted as the
conventional ones on a PACS/Workstation
•
AI: work through Mark Armstrong (ACR) to get radiologist to
enumerate the risks
•
How shall we model KV Switching? (including duration
and gaps, proportion of High/Low KV)
•
Alternative 1: Describe as two different sources
•
Alternative 2: Single dynamic source KV-Switching specific
parameters
•
How shall we “model” Photon Counting detector?
•
Shall we better describe the full “Data Path”?
•
Too complicated and vendor-specific?
•
Each vendor to provide a list of potential public attributes
specific for the each ME Acquisition/Recon techniques
The DICOM WG21 Multi-Energy Imaging Supplement aims to address the challenges and opportunities in multi-energy imaging technologies, providing a comprehensive overview of imaging techniques, use cases, objectives, and potential clinical applications. The supplement discusses the definition of multi-energy imaging, examples of multi-energy CT, physics background, clinical use cases, objectives, and new aspects of multi-energy technologies. It emphasizes the importance of standardization for different vendors' technologies to achieve consistent and accurate diagnostic images.
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Presentation Transcript
DICOM WG21 MULTI-ENERGY IMAGING SUPPLEMENT OVERVIEW 16-June-2015
Agenda Definitions, Use Cases, Objectives Aspects of Multi-Energy (ME) technologies New types of ME images Proposed Approach Risk and Concerns
ME-Definition Imaging techniques, including scanning, reconstruction, processing, when the scanner utilizes multiple energies from the X-Ray beam spectrum, as opposed to the conventional CT imaging, when a single (accumulated) X- Ray spectrum is used. The existing CT and Enhanced CT (eCT) IODs do not adequately describe the new CT multi-energy imaging. Although different vendors apply different scanning and detection techniques to achieve multi-energy images, there is large commonality in the generated diagnostic images.
Clinical Use Cases The primary potential applications this supplement intends to focus on include: Allowing better differentiation of materials that look similar on conventional CT images, e.g., to differentiate Iodine and Calcium in vascular structures Eliminating acquisitions such as non-contrast acquisition, when the virtual/artificial non-contrast image is generated from the contrast image
Objectives When defining this supplement, the following objectives / goals have been considered: 1. Making multi-energy information (acquisition, reconstruction and processing attributes) available to rendering or processing applications 2. Facilitating fast and easy adoption of this supplement across the imaging community, both modalities and PACS/Displays. 3. Eliminating (or at least minimizing) the risk of mis- interpretation when the ME images are displayed by a non-compliant display, including incorrect measurements
New aspects of ME technologies Different vendors apply different technologies for: Scanning Detection Reconstruction Processing Material decomposition This results in a variety of image types calling for standardization of parameters and definitions
Multi-Energy Imaging 4 categories Virtual Mono- chromatic Image (VMI) Material Quantification (Decomposition) Material Classification (Labeling) Material Visualization (Color) many flavors Material- Specific Image Effective AN (Z) Image Discrete Labeling Image Color Overlay Image Gout crystals on top of CT image Iodine Map; Bone Density Material- Subtracted Image Electron Density Image Proportiona l Map Image Color Blending Image Virt. Non-C; Virt. Non-Ca Material- Modified Image Probability Map Image Color Map Image Highlighted; Partially- Suppressed Color Eff. AN
Proposed approach For VMI and Quantification we reuse basic and enhanced CT IOD s for ME images For Classification we consider CT IOD, Segmentation IOD or Parametric Maps IOD For Visualization we consider reusing SC, Presentation States, Blending and may be Parametric map or adding RGB palette information to CT IOD Include the new attributes (add macros) in the existing IOD s Add new definitions to the existing standard (like Image Type, defined terms) Adapt some descriptions to multi-energy related cases Close to current implementations. Better chance to be widely/fast adopted
Virtual Mono-chromatic Image (VMI) Essentially analogous to a CT image that would be generated by a monochromatic (of a specific keV value) X-Ray beam 40 keV 167 keV
Material-Specific Image Iodine Map Image presenting a physical scale of specific material. Pixel values can be in HU or in equivalent material concentration (e.g., mg/ml).
Material-Subtracted Image Virtual Non-Contrast Image Image with one or more materials subtracted. Pixel values may have been corrected for displacement of one material by another material.
Material-Modified Image Bone Marrow Image Image where pixel values have been modified to highlight a certain target material (either by partially suppressing the background or by enhancing the target material), or to partially suppress the target material. Unlike Material-Specific and Material- Subtracted images (that can allow accurate measurements), the Material-Modified image is primary used for better visualization of the target materials
Electron Density Image An image where each pixel represents a number of electrons per unit volume. Widely used in radiotherapy.
Effective AN (Z) Image An image where each pixel represents Effective Atomic Number (aka Effective Z ) of that pixel.
Probability Map Image Gout Material Map An image where pixels describe the probability that this pixel is classified as one or more of the multiple defined materials
Material Visualization (Color) Users are asking to visualize ME-content in certain ways: color maps, color overlays, blending, etc.
How we intend to extend C.8.2.1 CT Image Module CT Additional X-Ray Source Sequence (0018,9360) (modified) Table X-1 Optional CT Multi-Energy Macro Attributes" (added) CT Multi Energy Acquisition Sequence (1C) >Table X-2 CT Multi-Energy Acquisition Macro Attributes CT Multi Energy Material Decomposition Sequence (1C) >Table X-3 CT Multi-Energy Material Decomposition Macro Attributes CT Multi Energy Image Sequence (1C) >Table X-4 CT Multi-Energy Image Macro Attributes C.8.15.3.9 CT X-Ray Details Macro (for Enhanced CT IOD only) CT X-Ray Details Sequence
Risk and Concerns Enhancements can be mis-interpreted in keV image need for correct display label including keV image type and keV value 50keV (virtual monochomatic) 100 kV (conventional)
Risk and Concerns High keV (>150) images can be mis-interpreted that no contrast was applied (missing contrast information) Potential risk for VNC images (e.g. changing size of small structures) currently not broadly validated that VNC is truly equivalent to TrueNonContrast Quantitative measurements are different in non-contrast vs. contrast use case (e.g. renal mass and renal cyst) Missing labeling can cause confusion which can slow down the workflow delay the diagnosis
Questions To guarantee that the created ME images contain the necessary information, a number of new attributes will be defined. These attributes will be added to the ME images, either as an extension to the existing IODs, or as part of new, ME-specific, IODs. However, we cannot mandate using any new specific attribute for existing IOD. Is there any workaround: Can we say that specific attribute is Type 1 if the system supports ME imaging? Yes only for new attributes; What about standard attributes, like Image Type? Ok to extend with new values and make them mandatory if the image is ME? Can we say that specific attribute is Type 1C if the units for pixels are not in HU? Not for existing attributes; ok for new Shall we rather define an optional SQ with mandatory ME attributes? Image Type of VMI Images. It is defined use ORIGINAL unless there is a specific case requiring it to be DERIVED. WG6 recommends leaving it to the vendor to decide if the image is ORIGINAL or DERIVED.
Questions To add Real-World Value Mapping to CT IOD to accurately describe the non-HU values. There are several concerns with this approach: RWVM is not specified today for CT images or widely implemented in the field (is this correct?). As a result, the units can be misinterpreted by the display application Rescale Slope and Rescale Intercept are Type-1 for CT image (if it is ORIGINAL); there is potential conflict between rescale attributes and RWVM. If we define image type as DERIVED, we avoid Type-1 requirement for Rescale Slope/Intercept, so they can be omitted. As a consequence, Rescale attributes shall be used if possible; for specific cases (to be identified) RWVM may be considered.
Questions To describe the need and recommendations for good labeling in the informative section (e.g., to display keV value for VMI images; to display material + concentration for measurements, etc.). DICOM alone cannot enforce PACS/Workstation to present specific attributes therefore there is little chance new important attributes we introduce here will be presented to the users. Should we work with IHE? Can we orchestrate the object in such a way that na ve display will either present the image adequately or fail to present anything? For instance, we can put rescale attributes to zero for non-HU ME images
Virtual Mono-chromatic Image (VMI) Renal Mass use case Contrast VMI Images 12 HU Non-Contrast Image - = 43 HU (160 keV) 206 HU 31 HU ( 120kV) 307 HU (50 keV)
DETAILS OF IMPLEMENTATION
ME Acquisition Techniques X-Ray Sources SINGLE_SOURCE MULTI_SOURCE KV Switching NONE, FAST, SLOW Multi-Energy Acquisition SINGLE_SCAN MULTI_SCAN Multi-Energy Detection CONVENTIONAL MULTILAYER PHOTON_COUNTING Technique-Specific Parameters: What do we want to record for Dual-Layer and PhC?
ME Material Decomposition Decomposition Method SINOGRAM_BASED IMAGE_BASED Decomposition Base Materials (sequence) Decomposition Description Vendor-specific label/description Material Attenuation Curves (opt) Decomposition Parameters e.g., dual-energy ratio
Pixel Value Units Method 1: using Rescale Rescale Slope Rescale Intercept Rescale Type Measurement Units as standard Coded Values e.g., UCUM: "mg/cm^3 Method 2 using Real World Value Mapping Sequence First/Last Values Mapped Value Slope/Intercept LUT (optional) Measurement Units (Coded Value)
Material Classification (Labeling) Proportional/Density Map: Discrete Labeling (most-probable material): ABCD 2 1.5 0: Unknown 1: Material A % or mg/ml 2: Material B 3: Material C Probability/Confidence Map: 0.2
Spectral Imaging Challenges Monochromatic Images Capture keV value How to differentiate from legacy CT? Incl. query, display annotations Material Density Images Non-HU values: how to avoid confusion? Several alternatives for solution Segmentation IOD Real-World Mapping PTE-like new IOD Rely on specific Rescale Intercept/Slope/Type Effective Atomic Number Images Similar challenges as for Material Density Using Color mapping prohibits measurements and analysis
Open Issues (some) Assess risks ME images been misinterpreted as the conventional ones on a PACS/Workstation AI: work through Mark Armstrong (ACR) to get radiologist to enumerate the risks How shall we model KV Switching? (including duration and gaps, proportion of High/Low KV) Alternative 1: Describe as two different sources Alternative 2: Single dynamic source KV-Switching specific parameters How shall we model Photon Counting detector? Shall we better describe the full Data Path ? Too complicated and vendor-specific? Each vendor to provide a list of potential public attributes specific for the each ME Acquisition/Recon techniques
