Transcript DICOM`s_New_MR_Objects_by_C_Parisot

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DICOM Supplement 49
Extended MR DICOM Objects
Korean PACS Conference
Charles Parisot
May 5, 2002
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New MR Image Objects
…… Why ?
To address with full interoperability, add
acquisition techniques such as :
•Diffusion Imaging,
•Perfusion Imaging,
•Angio Imaging,
•fMRI Imaging,
•Cardiac Imaging and
•Spectroscopy for MR
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3 New MR Image Objects
DICOM Supplement 49
• Enhanced MR Image Object
• MR Spectroscopy Object
• Raw Image Data Object
•
•
standardized enough to allow network, archive
otherwise vendor dependent
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A new Generation of Image Objects
…… Why ?
• Performance and ease to manage
the Exploding number of images
in an MR acquisition
• More complex inter-relation
between these images
• Real-Time Imaging increasingly
used
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A new Generation of Image Objects
…… Why ?
• Performance and ease to manage
the Exploding number of images
in an MR acquisition
• More complex inter-relation between
these images
• Real-Time Imaging increasingly
used
Solution:
Concatenation of
Multiframe Image Objects
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MR Multiframe High Level Requirements
•Provide a way of efficiently organizing large
groups of images (1,000 to 10,000 Images)
•Provide a way of organizing any group of images
(cine loop, Peripheral Vascular stations/phases)
• Allow for organizing of image sets associated
with a single complex multiphase application
(Peripheral Vascular localizers, stations/phases)
Dynamic Images
with up to 100 dimensions !
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Multiframe Organization
Flexible separation of Static vs Dynamic Attributes
Always Static Attributes - They never change per frame
in a multiframe:
– Will never need to change within an Image Object
=> changes in these require the start of a new
Image Object
– Goal is to reduce complexity in receiving
application
– Facilitates use of current toolkit technology.
Examples:
• Pixel (# bits, matrix size, etc.)
• Pulse Sequences
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Multiframe Organization
Dynamic Attribute Groupings
Dynamic Attributes may change per frame in a
multiframe. They are separated in Functional
Groups that often change together:
– To reduce the number of changeable entities
within a multiframe object
– to allow for modality independent reuse – modality
independent vs dependent
– to convey semantics to the receiving application
e.g., MIP program may not accept a MF object that has an
orientation group that changes
– Special care has been taken to "balance" the size
of groupings.
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MR Multiframe Organization – 24 Dynamic Functional Groups
Pixel Measures
MR Image Frame Type
Frame Content
MR Timing & Related Parameters
Plane Position
MR FOV/Geometry
Plane Orientation
MR Echo
Referenced Image
MR Modifier
Derivation Image
MR Image Modifier
Cardiac Trigger
MR Receive Coil
Frame Anatomy
MR Transmit Coil
Pixel value Transformation
Frame VOI LUT
Real World Value Mapping
MR Diffusion
MR Averages
MR Spatial Saturation
MR Metabolite Map
MR Velocity Encoding
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MR Multiframe Organization – 24 Dynamic Functional Groups
For a Specific MR Image Instance :
• some Functional Groups are
shared across all frames,
• some vary per frame
MR Image Instance
Shared Functional
Groups
Attributes
For all frames
Pixel Measures
MR Image Frame Type
Frame Content
MR Timing & Related Parameters
Plane Position
MR FOV/Geometry
MR Echo
MR Modifier
MR Image Modifier
MR Receive Coil
MR Transmit Coil
MR Diffusion
MR Averages
MR Spatial Saturation
MR Metabolite Map
MR Velocity Encoding
Plane Orientation
Referenced Image
Derivation Image
Cardiac Trigger
Frame Anatomy
Pixel value Transformation
Frame VOI LUT
Real World Value Mapping
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Per Frame Functional
Group Sequence
Frame 1
attributes
Frame 2
attributes
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A new Generation of Image Objects
…… Why ?
• Performance and ease to manage
the Exploding number of images
in an MR acquisition
• More complex inter-relation between
these images
• Real-Time Imaging increasingly
used
Solution:
Concatenation of
Multiframe Image Objects
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Reasons to break up
MultiFrame Objects
• File systems file, partitions, or storage media size
limits
• To provide pseudo real-time streaming (fMRI
from scanner to workstation for real-time
monitoring and processing)
• To provide for retransmission in chunks in the
case of network transmission failures
• Standard “Forced Breakup” due to dynamic
attribute being defined as a static attribute
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Concatenations
An object may be split up into two or more SOP Instances
e.g. After frame-numbers 2000, 4000 and 4200
1
1
Image attributes
Shared Dimension Module
attributes
2
3
4
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n
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Pixel data for frames of set n
Concatenation Frame Offset
Number (e.g.1, 2001, 4001 and 4201)
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Concatenation of MF Objects
• Examples: total body scan stations, fMRI broken into
time segments
• Concatenation UID is used to group image objects
belonging to the same concatenation
• All concatenated objects must have the same:
–
–
–
–
Instance Number
Frame of reference
Series number & UID
Dimension modules
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A new Generation of Image Objects
…… Why ?
•
•
•
•
Most attributes are made mandatory for greater interoperability
Many old attributes not used removed
Anatomy specification required to facilitate PACS handling
Image Relationship & Referencing – generalized and added
coded reasons for reference
• New image pipeline (LUT and Color Palette)
• Image Types
One sacrifice:
Create a New Enhanced MR Image Object, different
and incompatible with the existing MR image Object
For A Higher Level
of Compatibility
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MR Object Relationships

Ref. Image Sequence if planned on
prior image
Prior
Spectroscopy
0-n
Prior
Image


0-n
0-n
Source Image
Sequence for derived
images
Ref. Image Sequence
if planned on prior
images
0-n
Ref. Image Sequence
Only if image type is
METABOLITE MAP
Image

0-n
0 -n

Ref. Raw Data
Sequence
Source Image Sequence
for derived spectroscopy
data
Ref. Image Sequence if
planned on prior
spectroscopy data
Spectroscopy
0-n
Ref. Image
Sequence
Raw
Data
Ref. Raw Data
Sequence
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LUTs
Stored
Values
Real
Value
LUT
Real World
Value LUT or
Data
(0040,9212)
P
LUT
VOI
LUT
Modality
LUT
Display
Real world
value
Value
Real World
Value Intercept
and Slope
attributes
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Unit
Measurement
Units Code
Sequence
(0040,08EA)
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Real World Value LUT
• Sometimes the integer pixel values and what the user
wants to see for pixel values based on real world
units (such as blood flow velocity at a pixel location)
are different.
• The Real World Value LUT maps the pixel data to the
units the user wants to see (cm/sec or mm/sec…)
• Multiple overlapping regions of pixel values can be
mapped to multiple LUTs including regions that are
not mapped at all (e.g. functional data versus
physiological data).
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Palette Color Pipeline
Range of
Stored
Values to be
mapped to
grayscale
Modality
LUT
Largest
Monochrome
Pixel Value
VOI
LUT
G
B
+
Color
Display
Palette Color
Number of
entries
R
PLUT
Mapped to gray level
RGB values by display
device
Range of
Stored
Values to be
mapped to
color
....
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Palette Color LUT
• Palette Color LUT is used to map
Monochrome2 pixels to color
• Mixed Monochrome2 grayscale pixels and
palette color pixels can be shown in the same
image (e.g. to show functional data in color
on top of physiological data).
• Only a single grayscale and single color
range of pixel values can be represented.
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Image Type Attributes
MR Image Description Macro
• MR has a large, rich set of image types
• Applications need a way to determine if an image set is
compatible with its processing
• Supplement 49 proposes a reasonably orthogonal set of
attributes for image type useful to reading applications:
• Image Type (0008,0008) values:
– 1: Original/Derived – redefined
– 2: Primary/Secondary – Only Primary valid for MR
– 3: Image Flavor – the overall most important characteristic
of this Image – e.g. flow encoded, max-IP, Perfussion,
Stress, T1, T2, etc.
– 4: Derived Contrast – Diffusion aniso, Subtraction, Velocity,
None – generally an indication of post processing performed
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Image Type Attributes – 2
MR Image Description Macro
• Other Image Types are separate attributes:
– Pixel Presentation – (Palette) Color/Monochrome (color
supported or not)
– Volumetric Properties – Volume, Sampled, Distorted (used
by Grx, 3D to determine image compatibility with the
application)
– Volume Based Calculation Technique – MAX_IP, MPR,
Curved-MPR… (used by Grx, 3D to determine image
compatibility)
– Complex Image Component - Magnitude, Phase, Real,
Imaginary (standard MR transformations of the raw data)
– Acquisition Contrast - T1, T2, Perfusion, Combination…
(MR acquisition contrast types)
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New MR Image Objects
…… Why ?
To address with full interoperability add
acquisition techniques such as :
•Diffusion Imaging,
•Perfusion Imaging,
•Angio Imaging,
•fMRI Imaging,
•Cardiac Imaging and
•Spectroscopy for MR
Each Application has specific viewing characteristics…..
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Order in Viewing ?
• Many parameters can change from frame to frame.
• For the most important, those that define certain
relations between slices, specific tags have been
defined to indicate and order the relation.
• A “dimension” will consist of “number of...” tags with
the highest ordinal number of every dimension.
• Sorting images according to those ordinal
numbers, and repeating that for another dimension,
will enhance receiving applications and
interoperability
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Dimension Attributes
Examples
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Stations
Stacks
Positions
In-stack slice Position (slice # relative to stack)
Orientations
Trigger delay times
Temporal positions
Diffusion B values
Metabolite maps
Echoes
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Dimensions – Use of Indexes
• Examples can be given in many area’s, but in general
the mechanism uncouples the actual value of a
certain attribute: actual position vs. position number
• In some cases the increase of attribute values will be
related to that of the index number:
e.g. “Trigger delay time” (in ms) increases with the
Trigger delay time index: 1....6
• In other cases these are completely uncoupled:
Orientation(patient) and Orientation Index
• In some cases the index looks very much like the
attribute value.
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Examples of properties that may change,
cardiac phase
b-value
orientation
time
position
volume
time
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Cardiac Example: 1 station, 1 stack, n trigger delay times
Trigger delay time index
1
6
Instack
Index
5
2
6
4
3
2
1
Frame number 1-6
5
3
6
4
3
2
1
Frame number 7-12
5
4
3
2
1
Frame number 13-18
time
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Diffusion Example: 1 station, 1 stack , 3 b-values
B-value Index
3
6
Instack
Index
5
2
6
4
3
2
1
Frame number 1-6
5
1
6
4
3
2
1
Frame number 7-12
5
4
3
2
1
Frame number 13-18
time
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Example: 1 Station, 3 stacks
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Multi Stack example (parallel and non-parallel, 2D and 3D)
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Instack
Index
6
Instack
Index
5
5
4
4
3
3
6
Instack
Index
Stack 3
2
2
5
Frame number 13-18
1
Stack 2
1
Frame number 7-12
4
Stack 1
3
2
Frame number 1-6
1
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A new Generation of MR Image Objects
Multiframe
Functional Groups
of Dynamic Attributes
Concatenation
Most attributes
mandatory
New
Enhanced
MR Image
Anatomy specification
required
MR Object Relationship
and Referencing
LUT for Real Values
and Color Maps
MR
Spectro
Dimensions
and Indexes
Advanced MR Applications
Raw
Higher Efficiency
Images
Higher Compatibility
And soon Enhanced CT…..
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DICOM Web Site
http:// medical . nema . org
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