Radiation Protection in Digital Radiology
Digital Radiographic Image Processing
L05
IAEA
International Atomic Energy Agency
Educational Objectives
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List three main purposes of digital image
processing
Explain the term “greyscale histogram”
Show how radiographic technique factors
affect the greyscale histogram
Suggest how errors in digital image
processing can contribute to unnecessary
radiation exposure to patients
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The quality of any monochrome image
can be described in conventional terms.
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Density (darkness)
Contrast
Sharpness
Noise
Artefacts
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Advantages of DR images vs. analogue
images
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Density can be modified.
Contrast can be modified.
Sharpness can be modified.
Noise can be modified.
Result of 20 years of development!
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Advantages of analogue images vs. DR
images
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Density function inherently nice.
Contrast inherently higher.
Sharpness inherently higher.
Noise inherently lower.
Result of 110 years of development!
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DR density is adjustable and arbitrary
• Acquisition is independent from display
• Code values in the raw DR image can be
translated to any display level
• This allows DR to compensate for over- and
under-exposure, producing a consistent
appearance
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Incorrect exposure factor selection in
screen-film radiography
Overexposed
Underexposed
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DR compensates for incorrect exposure
factor selection
Overexposed
Underexposed
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The raw DR image has low contrast
• DR has an extremely wide latitude, which implies
low contrast for an imaging system that is “display
limited” (limited by the latitude of the display).
• DR code values can be remapped to generate high
contrast for “values of interest” (VOI), while
sacrificing contrast for other values.
• This is the primary purpose of DR image
processing
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Idealised Greyscale Histogram
• Goal is to represent
anatomy, A, with good
contrast
# pixels
• B is air
• C is scatter contribution only outside collimators
• D is scatter contribution only image of anatomy
outside collimated area,
barium, or lead
A
D
B
C
Exposure (or greyscale)
Region of clinical interest
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Increased mAs shifts the Greyscale
Histogram
# pixels
A
D
B
C
Exposure (or greyscale)
Region of clinical interest
• Changing mAs does not affect subject contrast,
as long as the dynamic range is not exceeded
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Increased kVp squeezes the Greyscale
Histogram
# pixels
D
C
A
B
Exposure (or greyscale)
Region of clinical interest
• Higher kVp => less subject contrast
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Primary job of image processing: identify values
of interest and maximize their contrast.
• Detection of collimator boundaries or
anatomy, “exposure recognition”
• Window width and window level are adjusted
relative to greyscale histogram
• Density is thus also adjusted
• This is “acquisition processing”
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Did we skip a step?
• How did we assure that the response of the
detector was uniform over the entire field of
view?
• “Pre-acquisition processing”, or
“preprocessing”, corrects for detector
imperfections and variable response.
• Some include auto-ranging in preprocessing.
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Detector characteristic function for CR
3
10000
1000
2
1.5
100
1
10
Intensity (rel)
Density (OD)
2.5
Film/screen
Histogram
w/contrast
PSL
Adjust WW
0.5
0
0.1
1
10
100
1
1000
Air KERMA (µGy)
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Raw data
Raw, ranged data
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“Matched latitude” is another feature of
histogram re-scaling
3
10000
1000
2
1.5
100
1
10
Intensity (rel)
Density (OD)
2.5
Histogram 1
Histogram 2
Adjust WW 1
Adjust WW 2
0.5
0
0.1
1
10
100
1
1000
Air KERMA (µGy)
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Code values can be remapped in more
complex ways to modify contrast
• Modifying contrast is the secondary role for image
processing
• Contrast is compromised for some values of
interest in order to enhance contrast in others
• “post-acquisition processing”
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There are many brand-names for postacquisition processing
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Look-up-table (LUT, Gradation Processing)
Unsharp Mask (Frequency Processing)
Multi-frequency Processing (Musica®)
Multi-Objective Frequency Processing
Dynamic Range Control
Tomographic Artifact Suppression
Energy Subtraction (Dual Energy)
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Code values can be remapped to a nonlinear function
• This function might have lower contrast for lighter
and darker features with higher contrast for values
in the middle range, to achieve a film-like
appearance.
• Code values within the values of interest are
translated by means of a Look-up Table (LUT).
• This is “Gradation Processing”,
“Sensitometry”, and “grey-scale rendition”
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Raw, ranged data
Gradation-processed data
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The DR image has limited sharpness
• Sharpness is limited by pixel dimensions
• The smallest feature that can be resolved by CR is
a “line pair” represented by one “dark” pixel next to
a “light” pixel.
• Maximum spatial resolution is the sampling rate
(pixels per mm) divided by 2 (pixels per line pair)
• This is also called the “Nyquist Frequency” or
“Nyquist Limit”
• A large format cassette with 2000 pixels along the
35 cm dimension would have about 6 pixels per
mm and a maximum spatial resolution of 3 lp/mm
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Practical resolution is less than the Nyquist
frequency
• Factors besides sampling
compromise sharpness
• X-ray focal spot dimensions
• Blur in Indirect DR and CR
• Optical and mechanical
imprecision in IDR and CR
• Afterglow in fast-scan dimension
in CR
• Limit of resolution is where
Modulation Transfer Function
(MTF) has decreased to 10%
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Enhancing sharpness: a secondary
purpose of image processing
• If one can selectively increase the contrast
of features in the image that represent large
changes in code value over a few pixels,
one can increase sharpness.
• Two methods
• Unsharp Mask (Frequency Processing)
• Musica® Edge Contrast
• “edge restoration”
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Unsharp mask process
• Start with original image
• Create a blurred version of the original image by averaging
all pixels within a small region called a “kernel”.
• A large kernel blurs large features
• A small kernel blurs small features
• Subtract the blurred image from the original image to make
a difference image or mask
• The mask contains features that were NOT blurred
• Add the mask back to the original image
• Resulting image has enhanced features that were NOT
blurred
• Enhancement controlled by a “boost” factor
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Unsharp mask process
blurred
Original image
Blurred image
Orig + Diff
Difference image
Sharpened image
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Musica® Edge Contrast
• Raw image is decomposed into sub-bands, each
representing an octave of spatial frequencies.
• Adding all sub-bands together would reconstitute
the original image
• The contrast of features in each sub-band is modified
according to a function.
• The degree of enhancement is controlled by the
value of a single parameter.
• Differential enhancement is controlled by a second
parameter value.
• The modified sub-bands are added back together to
create a modified image.
• Extra enhancement of high frequency sub-bands
emphasizes edges
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Raw, ranged data
Gradation-processed data
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Musica® Processed data
Gradation-processed data
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Dynamic
Range
Control
blurred
Original image
Blurred image
(actually a form
of “contrast
enhancement”)
Orig + f(blurred)
DRC image
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Dual Energy Subtraction Imaging: Uses low
energy image and high energy image …
• Two images are acquired
• Weighted combination and subtraction of these
images produces “bone only” and “soft tissue
only “ images
• Quality of images depends on energy separation
Bone only image
Soft tissue only image
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Conventional vs. DES images
Where is the lesion? Is it calcified?
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Sufficient x-rays must reach the detector
to produce the radiographic image.
• At the same dose, the smaller the pixel size, the
fewer x-rays in each pixel, and the worse the
noise.
• Larger the pixel size, worse the sharpness.
Air KERMA
Photons
Noise
(µGy)
/100m X100m
(%)
0.9
133
8.6
0.09
13
27.4
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9 µGy
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L05 Digital Radiographic Image Processing
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3 µGy
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Post-acquisition processing can reduce
noise. (generic term is “noise reduction”)
• Because noise is considered as high frequency
variation, attenuating high spatial frequencies can
reduce noise.
• This is effectively a “high pass filter”
• Unsharp Mask can do this
• Musica® Noise Reduction can do this
• This also attenuates small clinical features!
• Corollary is that, enhancing small features also
enhances noise!
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Optimization in DR imaging cannot
ignore patient dose!
• In order to make a diagnostic radiographic image,
a sufficient number of x-rays must reach the
detector.
• Unfortunately, the x-rays must pass through the
patient to reach the detector.
• The ALARA Principle dictates that the examination
should be performed with the lowest reasonable
dose to the patient.
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Acquisition processing involves
assumptions:
• Radiographic technique
• Composition of anatomic region imaged
• Use of collimation
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Images with very different greyscale histograms
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Post-acquisition processing is controlled
by exam-specific parameters
• There are literally thousands of permutations
of allowable parameter settings.
• Extreme values can have dramatic effects
on the image.
• There is no general agreement on the
optimum values for the parameters.
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Auxiliary purpose of image processing:
improve usability
• Imprint demographic
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overlays
Add annotations
Apply borders or shadow
masks
Flip and rotate
Increase magnification
Conjoin images
• Scoliosis
• Full leg
• Modify sequence of views
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Conjoined images: early vs. modern
Note:
different
contrast
Better
contrast
matching
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Inappropriate roles for image processing
• Compensate for
inappropriate radiographic
technique
• Compensate for poor
calibration of acquisition
and display
• Deletion of non-diagnostic
images
Recovery of non-diagnostic
images to prevent reexposure is last resort, not
routine activity!
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Bad practice still translates into bad images.
Automation has not been
invented to correct for
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patient motion
poor inspiration
bad positioning
improper collimation
incorrect alignment of x-ray beam
and grid
• wrong exam performed
• wrong patient examined
• double-exposure.
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Radiographers need to recognize image artefacts ...
• … and take appropriate
action when artefacts
occur.
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Conclusions:
• DR image quality can be described in conventional
terms.
• DR image processing has three purposes:
• Identify values of interest and maximize their contrast
• Modify contrast within values of interest
• Improve usability of the image
• Image quality cannot be optimized without
considering patient dose.
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Answer True or False
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Acquired images from DR system are
independent of display
DR has wide latitude
Spatial resolution of DR images are limited
by pixel dimensions
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Answer True or False
• True. Acquisition is independent from display, code
values in the raw DR image can be translated to
any display level
• True. DR has extremely wide latitude, ie., it has
low contrast and is limited by the latitude of the
display
• True. The factors involved are focal spot thickness,
blur in indirect DR (IDR) and CR, after glow and
optical and mechanical imprecision in IDR and CR.
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References
• Flynn MJ Processing digital radiographs of specific body parts.
Advances in Digital Radiography: RSNA Categorical Course in
Diagnostic Radiology Physics. Samei E and Flynn MJ eds (2003) 71-
78.
• Seibert JA Digital radiographic image presentation: preprocessing
methods. Advances in Digital Radiography: RSNA Categorical Course
in Diagnostic Radiology Physics. Samei E and Flynn MJ eds (2003),6370.
• Chotas HG, Ravin CE. Digital radiography with photostimulable storage
phosphor: control of detector latitude in chest imaging. Invest Radiol 27
(1992),822-828.
• Huda W, Slone RM, Arreola M, Hoyle BA, Jing Z. Significance of
exposure data recognizer modes in computed radiography. SPIE 2708
(1996),609-616.
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References (continued)
• Freedman M, Pe E, Mun SK, Lo SCB, Nelson M. The potential for
unnecessary patient exposure from the use of
storage
phosphor imaging systems. SPIE 1897(1993) 472-479.
• Gur D, Fuhman CR, Feist JH, Slifko R, Peace B. Natural migration
to a higher dose in CR imaging. Proc Eighth European Congress of
Radiology. Vienna Sep 12-17(1993)154.
• Huda W, Slone RM, Belden CJ, Williams JL, Cumming WA, Palmer
CK. Mottle on computed radiographs of the chest in pediatric
patients. Radiology 199 (1996) 249-252.
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