Download Digital Subtraction Angiography

Digital Image Processing
and
Digital
Subtraction
Angiography
วัตถุประสงค์
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อธิบายขบวนการประมวลผลภาพดิจิตอลได้
อธิบายวิธีการปรับคอนทราสของภาพดิจิตอลได้
อธิบายการทางานและควบคุม window ของภาพรังสี ดิจิตอลได้
อธิบายวิธีการทา Subtraction ภาพด้วยวิธีต่างๆ ได้
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LUT Curve
Selection of
Curve
Enhancing Visibility of Detail
Digital
Subtraction
Angiography
DSA
Computed radiography
The need for subtraction
Subtraction for
improvement
in conspicuity
Mask image
Live image
(original)
(original + contrast media)
Mask-Live
Mask image
Live image
Live-Mask
Image processing with Java
ให้นกั ศึกษาใช้ โปรแกรมนี้ในการทา
Digital Subtraction
ftp://rsbweb.nih.gov/pub/image-j/win32/
Subtraction methods
1. Depth
2. Energy
3. Time
1. Temporal subtraction
(Time-dependent)
Temporal
subtraction
1. Pre-contrast images (mask images)
2. Post-contrast images (live images)
3. Subtraction of mask from live images
2. Energy subtraction
Energy dependence of x-ray attenuation of difference tissue
Dual energy subtraction
Dual energy
subtraction
Compton/Photoelectric
decomposition
Advantage / Disadvantage
1. Provide selective
cancellation
2. Fast , in millisecond,
minimized motion
interference
1. More complex
2. More sensitive to
scatter radiation
3.Impossible to
remove soft-tissue
and bone
simultaneously
Dual energy subtraction images
Soft-tissue removed
Bone removed
3. Hybrid subtraction
Temporal subtraction + Energy subtraction
Image processing
1. Spatial filtering
2. Pixel shifting operation
3. Temporal filtering
4. Intensity transformations
5. Window/Level techniques
6. Parametric imaging
1. Spatial filtering
Spatial filtering is a method of selectively
enhancing or diminishing specific spatial
frequency components in an image
Diagram of two-dimensional digital spatial filtering
Digital
filtering(Convolution)
Each pixel in the processed images is
derived from a set of pixels in the
original image as determined by the
mask.
• Methods
Low-pass filtering
High-pass filtering
Median filtering
Low-pass digital spatial
filtering(Smoothing)
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High-pass digital spatial
filtering (Edge enhancement)
Filtered images
Original
Low-pass
High-pass
Smoothing
(Edge enhancement)
Median filtering
Mask = Median value of the
appropriate 9 pixels in the
original image
Median filtering images
Digital chest radiograph with
unwanted dot artifacts
After application of 3x1 medial
filter to remove dots
2. Pixel shifting operation
• Rotation
• Translation
• Magnification
• Minification
Pixel registration to reduce motion artifacts
3. Temporal filtering
1. Time interval
difference(TID)
2. Integration
3. Blurred mask temporal
subtraction
Generalized temporal
filtering diagram
4. Recursive filtering (real
time methods)
3.1. Time -interval difference
subtraction
3.2. Integration
Pre-contrast and post-contrast images are
summated(integrated) to reduce noise
Image integration
Single pre-contrast image
Single post-contrast images
8 pre-contrast image
8 post-contrast image
3.3. Blurred mask
temporal subtraction
For cardiac study : increase s/n for mask image and
the edge of cardiac will blurred
3.4. Recursive filtering
(real time methods)
1. Reduce radiation dose
2. Reduce motion artifacts
4. Intensity transformation
Use of image processing to correct the non linearity of film
Gamma correction curve
Gamma correction curves can be use to enhance or reduce contrast
Contrast
enhancement
Contrast
reduction
Original image
Histogram equalization
Original arterial DSA image
of the kidney
After histogram equalization
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Display
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Histogram equalization
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255
Display
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5. Windows / Level Techniques
Gray scale
display
1024
White
Window
width W
Window
center C
Black
0
Windowing
Double windows techniques
1024
Gray scale
display
White
Window 1
Black
Window 2
0
Windowing
6. Parametric imaging
• The algorithms for image processing that provide a
final displayed image in which the value of each
pixel is related to the attenuation or attenuation
change at the particular point in the patient
Parametric(functional) imaging
Acute Tubular Necrosis
Example of parametric imaging
1. Time to peak enhancement
2. Mean transit time
3. Maximum pixel attenuation
4. Integrated attenuation change
5. Local volume distribution
6.Quantitative imaging :
Temporal processing
An idealized contrast enhancement curve or
Indicator dilution curve
Quantitative imaging
Example of calculation
1. Peak or Maximum
enhancement
2. Time to maximum
enhancement
3.Time to half maximum
enhancement
4. Integrated
enhancement(area under
the curve)
5. Mean transit time
6. etc
Application
A. Cardiac output
B. Regional blood flow
C. Cardiac ventricular
ejection fraction
D. Quantitation of left to
right shut
E. etc
Gamma variate parameters of typical timeconcentration curve
A comparison between cardiac output estimations using DR
and standard thermodilution methods
DSA quantitation of vessel stenosis
DSA of right coronary
artery stenosis
Identifies the region of
stenosis, and normal portion,
then calculate the degree of
narrowing
Boundary detection
1.After location
of aortic valve
plane and apex,
the
computer
constructs a ray
passing through
the center(x) of
the LV
2. A s e r i e s o f
rays emanating
from the center
are drawn by the
c o m p u t e r
3. The density of
pixel values is
measured , the
e d g e
i s
d e te r m in e d a t
50% o f t h e
maximum values
End-diastolic
ED contours
are shown for
different
thresholds
values(50% and
75%)
End-systolic
ES contours
are shown for
different
thresholds
values(50% and
75%)
The ejection
fraction is
computed using
the 50% thresholds
silhouettes
EDV  ESV
EF 
EDV
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บรรณานุกรม
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Image processing program with Java http://rsb.info.nih.gov/ij/
Digital Subtraction Angiography. USA,