Outline of presentation
Digital Fluoroscopic Imaging:
Acquisition, Processing & Display
• Introduction to digital fluoroscopy
AAPM
46th
Annual Meeting
July 2004
Pittsburgh, Pennsylvania
• Digital fluoroscopy components
• Analog and digital image characteristics
• Image digitization (quantization/sampling)
J. Anthony Seibert, Ph.D.
University of California Davis
Medical Center
Sacramento, California
• Image processing
• Summary
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
History of digital fluoroscopic imaging
History of digital fluoroscopic imaging
• …….
……. mid 1970’
1970’s
• …….
……. 1990’
1990’s
– Modified II/TV system with “fast”
fast” ADC
– Temporal and energy subtraction methods
–
–
–
–
–
• …….
……. 1980’
1980’s
–
–
–
–
Clinical DSA angiography systems
Qualitative and quantitative improvements
Image processing advances
Temporal and recursive filtering
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Quantitative correction of image data
Rotational fluoroscopic imaging
MicroMicro-fluoroscopic imaging capabilities
CT fluoroscopy (using fanfan-beam scanners)
ConeCone-beam CT reconstructions
• …….
……. 2000 - present
– Introduction of realreal-time flatflat-panel detectors
3
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Why digital fluoroscopy / fluorography?
• Low dose fluoroscopic imaging
(digital averaging, last frame hold)
•
•
•
•
•
•
• Pulsed fluoroscopy and variable frame rate
• DSA and nonnon-subtraction acquisition and display
• Digital image processing and quantitation
Introduction to digital fluoroscopy
Digital fluoroscopy components
Analog and digital image characteristics
Image digitization (quantization/sampling)
Image processing
Summary
• Image distribution and archiving, PACS
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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1
Fluoroscopic Acquisition Components
Image Intensifier - TV subsystem
TV Camera
Input phosphor
Housing
Photocathode (- )
Side View: C arm System
Aperture
(Iris)
Focusing
electrodes
TV camera
Evacuated
Insert
e-
C-Arm
Apparatus
Image
Intensifier
Lens optics
and mirror
assembly
Anode (+)
TV Monitor
e-
X-rays in
Cine Camera
Photospot Camera
Spot Film Device
Digital Photospot
DSA System
Collimator
~25,000 Volts
acceleration
Grid
Light out → Recorder
-
e- - eee
-
ZnCdS:Ag
output phosphor
e- e- eee
-
CsI input
phosphor
X-ray Tube
SbCs3
photocathode
X-rays → Light → Electrons
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Video or CCD
camera to ADC
to Digital Image
Output
phosphor
Peripherals
Electrons → Light
~5000 X amplification
7
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TV camera readout and output video
Structured Phosphor: Cesium Iodide (CsI)
Crystals grow in long columns that act as light pipes
CsI
Light Pipe (Optical
Fiber)
LSF
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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TV camera specifications
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IIII-TV digital systems
• Low resolution:
• Two decades+ of availability
– 525 line, interlaced, 30 Hz (RS(RS-170)
• Video signal is convenient for digitization
• High resolution:
• Low noise performance of II’
II’s: ↑SNR
– 1023 - 1049 line, interlaced, 30 Hz (RS(RS-343)
• WellWell-developed capabilities
• Highest resolution
– IA, DSA, digital photospot
– 2048 line systems
– Rotational CT
• CCD camera implementations
• Progressive scan a must for short pulsepulse-width
digital applications
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
• II is Big and bulky; image distortions prevalent
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2
Photodetector: a - Si TFT active matrix array
FlatFlat-panel Fluoroscopy / Fluorography
Photodiode:
Light to electronic signal
Scintillator
• Based upon TFT charge storage and
readout technology
• ThinThin-FilmFilm-Transistor arrays
– Proven with radiography applications
– Now available in fluoroscopy
• CsI scintillator systems (indirect conversion)
• a-Se systems (direct conversion)
X-rays to light
Amplifiers – Signal out
TFT: Storage and readout
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Amorphous Silicon
Amorphous Silicon
TFT active matrix array
TFT active matrix array
Gate
switches
Amplifiers – Signal out
G1
Active
Area
Dead
Zone
13
G2
Amplifiers – Signal out
G1
ThinThin-Film
Transistor
D1
CR1
D2
CR2
D3
Store the charge
G3
Active Readout
Activate gates
Amplify charge
Convert to Digital
Charge
Collector
Electrode
CR3
Charge
Amplifiers
Data lines
Analog to
Digital
Converters
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Cross section of detector:
a-Se / TFT array
X-ray
Incident xx-rays
High
voltage
Structured XX-ray
phosphor (CsI)
Top electrode
+
-
Source
Gate
Drain
S
G
- - -
-
+
-
+
+
+++
Charge
Photodiode
+
+
Adjacent gate line
TFT
-
+
+
D
16
Direct detector crosscross-section:
a-Si TFT/ CsI phosphor
Light
Expose to xx-rays
G2
Storage
Fill Factor = Active area ÷ (Active area + Dead Zone) Capacitor
G3
Large pixels: ~ 70%
Small pixels: ~ 30 %
14
Storage capacitor
+
-
Selenium photoconductor
Charge collection electrode
(pixel size)
ThinThin-FilmFilm-Transistor
Storage capacitor
Glass substrate
Stored charge
X-rays to light to electrons to electronic signal:
Indirect xx-ray conversion
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
X-rays to electrons to electronic signal
Direct xx-ray conversion
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3
Flat panel vs. Image Intensifier
Output
phosphor
image
Total over-framing
Maximum horizontal framing
Digital
sampling
matrix
Flat
panel
Maximum vertical framing
II
Field coverage / size advantage to flat panel
Image distortion advantage to flat panel
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Framing of digital matrix:
framing
FOV
spatial resolution
% recorded area
23 cm nominal
input diameter
512 × 480 matrix
(% digital area used)
Maximum vertical
framing
22 cm
0.46 mm
100 %
1.09 lp / mm
(41%)
Maximum horizontal
framing
Maximum
overframing*
overframing*
19 cm
15 cm
0.43 mm
74%
1.16 lp / mm
(78%)
0.33 mm
61%
1.5 lp / mm
(100%)
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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FlatFlat-panel fluoro detector:
FOV vs. spatial resolution vs. xx-ray utilization
4:3 aspect ratio
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
efficient use of xx-ray detector / xx-ray field
21
Flat panel vs. Image Intensifier
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Flat panel vs. Image Intensifier
• Electronic noise limits flatflat-panel amplification
gain at fluoro levels (1(1-5 µr/frame)
• Pixel binning (2x2, 3x3) offers improvements
• Low noise TFT’
TFT’s are slowly being produced;
variable gain technologies on the horizon
II conversion gain: ~5000:1
-- Electron acceleration flux gain
-- Minification gain
• II’
II’s will likely go the way of the CRT……
CRT……..
FOV variability (mag
(mag mode) and sampling advantage to II
Gain / noise advantage to II
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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4
Interventional system digital hardware architecture
X-ray
system
Analog
signal
Arithmetic
Logic Unit
Array
Processor
MicroProcessor
Peripheral
equipment
Patient
monitor
•
•
•
•
•
•
ADC
DAC
Display
Processor
Video
memory:
64 MB to
512 MB
System information (kV, mA, etc)
Digital
Disk Array
DICOM
Interface
Images
(XA objects)
Image Workstation
Modality Interface
Local Image
Cache
HL-7
Interface
PACS
Introduction to digital fluoroscopy
Digital fluoroscopy components
Analog and digital image characteristics
Image digitization (quantization/sampling)
Image processing
Summary
Modality Worklist
Patient / Images
reconciliation
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Conventional raster scan: RSRS-170
Fluoroscopic Analog Image
4:3 aspect ratio, 525 lines, 483 active
• Continuous brightness variation
corresponding to differential xx-ray
transmission of the object
700 mV
voltage
image
height:
3
Uniformly irradiated
II with lead disk
0 mV
39 µsec
-300 mV
sync signals determine
image location
image width: 4
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
33 msec
Single horizontal video line
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Digital Image Matrix
Digital Image Requirements
700 mV
• Contrast resolution
voltage
– Ability to differentiate subtle differences in
x-ray attenuation (integer numbers)
0 mV
39 µsec
-300 mV
• Spatial Resolution
Rows and columns define
useful matrix size across
active field of view. For
RS-170 standard, this
corresponds to
~480 x 480.
– Ability to discriminate and detect small objects
(typically of high attenuation)
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Single horizontal video line
23 68 145 190 238 244 249 150 38 31 30 35 43 159 232 241 239 182 131 33
Digitized video signal corresponding to horizontal line
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5
Digital Acquisition Process
Digital Image Characteristics
• Advantages
• Conversion of continuous, analog signal
into discrete digital signal
– Separation of acquisition and display
– Image processing applications
– Electronic display, distribution, archive
• Digitization
• Disadvantages: noise and data loss
– Sampling (temporal / spatial)
– Quantization (conversion to integer value)
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
– Quantization
– Sampling
– Electronic (shot)
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Consequences of digitization
• Negative:
– Loss of spatial resolution
•
•
•
•
•
•
– Loss of contrast fidelity
– Aliasing of high frequency signals
• Positive:
– Image processing and manipulation
– Electronic distribution, display and archive
Introduction to digital fluoroscopy
Digital fluoroscopy components
Analog and digital image characteristics
Image digitization (quantization/sampling)
Image processing
Summary
– Quantitative data analysis
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Acquisition
Processing
Peripheral
components
ADC
Analog to
digital
conversion
34
• Sampling:
Sampling: measuring the analog signal at
discrete time intervals
Softcopy
CRT or
DAC
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Analog to Digital Conversion:
Digitization
Display
Computer
hardware
and
software
algorithms
Fluoro unit
33
FlatPanel
– @ 2x frequency of video bandwidth
Digital to
analog
conversion
• Quantization:
Quantization: converting the amplitude of
the sampled signal into a digital number
RAIDRAID-5
online
– Determined by the number of ADC bits
Storage / Archive
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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6
Sampling: discrete spatial measurement
Sampling
infinite bits, 3 samples / line
• Signal averaging within detector element (del)
area = ∆x × ∆y
•
• Cutoff sampling frequency = 1 / ∆x
Input
Sampling aperture
• Nyquist frequency = 1 / 2∆
2∆x
•
• Minimum resolvable object size (mm)
= 1 / (2 × Nyquist frequency)
relative error
Sampling points
infinite bits, 7 samples / line
Input
relative error
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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in phase
200 µm
500 µm
38
Input signal equal to Nyquist frequency
MTF of pixel (sampling) aperture
1000 µm
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Phase Effects
Resolution and digital sampling
Detector
Element,
“DEL”
DEL”
Sampling points
Sampling aperture
180°
180° phase shift
1
Bar pattern
Modulation
0.8
0.6
pixel matrix
0.4
0.2
0
0
1
2
3
4
5
6
Frequency (lp/mm)
Cutoff frequency = 1 / ∆x
Sampling
pitch
Sampling
aperture
good signal modulation
no signal modulation
MTF of sampling aperture
Nyquist frequency = 1/2∆
1/2∆x, when pitch = aperture
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
sampled output signal
39
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Aliasing effects:
Aliasing: Insufficient sampling
Input signal frequency, f > Nyquist frequency, fN
input f = 1.5 fN
Pixel Sampling
input f = 2.0 fN
Low frequency
> 2 samples/ cycle
High frequency
Assigned (aliased) frequency
< 2 samples/ cycle
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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output f = 0.5 fN
output f = 1.0 fN
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7
Aliasing
How important is aliasing?
• Most objects have relatively low contrast
Input signal frequency spectrum, fin
Input signal BW
• High frequency noise lowers DQE(f) in the clinically
useful frequency range
amplitude
Sampling BW
• Clinical impact is probably minimal, except with
stationary antianti-scatter grids
-fN
fN
0
fS
• However, image size reduction can cause aliasing
2fS
– Subsampling retains high frequencies, violating Nyquist limit
Frequency
Higher frequency overlapping sidebands
reflect about f to lower spatial frequencies
N
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Resolution and image blur
44
FOV and digital sampling
12 cm
24 cm
– Light spread in phosphor
– Geometric blurring: magnification / focal spot
1k x 1k: 120 µm
~4 lp/mm
• Goal: match detector element with
anticipated spread to optimize sampling
process
24 cm
12 cm
• Sources of blur
1k x 1k : 240 µm
~2 lp/mm
2 k x 2k: 120 µm
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
~4 lp/mm
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Quantization: conversion to digital number
Sampling and spatial resolution
2 bits (4 discrete levels) and infinite sampling
3
2
1
0
input signal ramp
1000 samples
500 samples
250 samples
125 samples
quantized output
relative error
3 bits (8 discrete levels) and infinite sampling
7
6
5
4
3
2
1
0
input signal ramp
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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quantized output
relative error
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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8
3 bit Analog to
Digital Converter
Reference
voltage, V
Video
input
Quantization
Comparators
• Threshold to next level is ½ step size
R
+
-
7V
8
R
+
-
6V
8
Successive
fractional
voltage at each
comparator
+
-
5V
8
R
+
-
4V
8
R
MSB
• Quantization noise causes “contouring”
contouring”
Priority
Encoder
Logic
+
-
3V
8
• Larger # bits provide better accuracy
Digital
Output
R
8 discrete output values
• Typical bit depths:
LSB
– Fluoroscopy: 8 bits
– Angiography: 10 – 12 bits
– CR / DR:
10 – 14 bits
R
+
-
2V
8
R
+
-
V
8
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Quantization Effects
50
Dynamic range considerations
• Maximum usable signal determined by:
– Saturation of detector (TV camera)
– Light aperture (determine entrance exposure)
– Analog to digital converter (ADC)
• Minimum usable signal determined by:
8 bits
4 bits
3 bits
–
–
–
–
2 bits
Number of bits in ADC
Quantum noise
System noise
Electronics
“Contouring”
Contouring” is a problem in areas slowly varying in contrast.
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Resolution and Image Size
512
x
graylevels
256
1024
4096
16384
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Digital Image Display
• 2 bytes / pixel uncompressed for digital fluoro
•
bits
8
10
12
14
• Digital to Analog Converter (DAC)
512 matrix (1/2 MB/image, 15 MB/s*)
• Estimate of original analog signal amplitude
• 1024 x 1024 matrix ( 1 MB/image, 30 MB/s*)
• 2048 x 2048 matrix (4 MB/image, 120 MB/s*)
• Image fidelity determined by
– *At 30 frame/s acquisition rate
– Frequency response (bandwidth)
– Number of converter bits (usually 8 or 10 bits)
– Image refresh rate (# updates / sec)
• Overall storage requirement / Interventional
Aangiography study:
– 200 to 1000 MB
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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9
MSB
Digital to Analog Converter: DAC
0
0
Reference voltage
0
0
0
MSB
Ref / 2
0
1
0
Ref / 4
0
0
0
0
0
0
0
0
0
0
0
Voltage
adder
Ref / 8
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Image bit
planes
Ref / 16
Digital
input
0
0
000 0
0 1 1
0 1 1 1
0 1 1 1 1
1 1 1 1 1
0 0 0 1 1 1 1 1 1
0 0 0 1 1 1 1 1 1 1
0 0 1 1 1 1 1 1 1 1
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 0 1 1 1 1 1 1 1 1 1
0
0 1 1 1 1 1 1 1 1 1
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Bit depth
0
1
LSB
Numerical
representation
y
x
Linear DAC
Ref / 32
1
Voltage
out
Ref / 64
1
Ref / 128
0
video
synchronization
electronics
Ref / 256
LSB
0
source gate
drain
Image
representation
Transistor (switch)
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
digital number
appearance:
55
0
dark
255
bright
56
Display of digital data
Display adjustments
Look-up-table
(LUT)
• LUT: Look up table
– Dynamic conversion of digital data through a
translation table
Logarithmic
transform
8 bit output
255
255
Linear
transform
WL
– NonNon-destructive variation of image brightness and
contrast
WW
Exponential
transform
– Reduced display dynamic range requires
compression of image range data
0
12 bit input
57
Grayscale Processing
0
0
4095
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
8 bit output
display range
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Window Width / Window Level
• LookLook-upup-table Transformation
– Window (contrast) and level (brightness)
Iout (x,y) = c × Iin (x,y) + d
• Histogram equalization
– Redistribution of grayscale frequencies over
the full output range
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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10
Contrast Resolution
Low Contrast Resolution
• Fluoroscopic Speed
– Dependent on lightlight-limiting aperture
– variable for digital flatflat-panel detectors
– ? secondary quantum sink at higher frequencies
Temporal
Averaging
4 frames
No Temporal
Averaging
• Electronic noise
– shot noise, dark noise, fixed pattern noise
• Structured noise
– Anatomy, overlying objects
• “Useful”
Useful” dynamic range
– minimum detectable contrast with additive noise
1 mR
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0.1 mR
Image subtraction low contrast phantom
0.01 mR
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Noise Sources
•
Digital acquisition: SNRSNR-limited detection
–
–
–
–
–
quantum mottle and secondary quantum sink
fixed pattern (equipment) structured noise
electronic and shot noise
digitization: sampling and quantization noise
anatomic (patient) noise
•
•
•
•
•
•
• Imaging system should always function in
x-ray quantumquantum-limited range
Introduction to digital fluoroscopy
Digital fluoroscopy components
Analog and digital image characteristics
Image digitization (quantization/sampling)
Image processing
Summary
– With II/TV, gain is sufficient
– With flatflat-panel, electronic noise is limiting factor
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Image Processing
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Image Processing Operations
• Enhance conspicuity of clinical information
• Point
– Pixel to pixel manipulation
• Optimize image display on softcopy monitors
• Local
• Optimize image display on hardcopy film
– Small pixel area to pixel manipulation
• Reduce radiation dose through image averaging
• Global
– Large pixel area to pixel manipulation
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Temporal Averaging
Image Subtraction (DSA)
Iout(x,y)
(x,y) = N Σ Ii(x,y)
• Pixel by pixel operation:
• Reduces noise
fluctuations by N 0.5
• Increases SNR
• Decreases temporal
resolution
Iout(
(x,y) – Ii(x,y) + c
out(i) (x,y) = Im(x,y)
• Time dependent log difference signal
• Contrast enhancement via windowing and
leveling
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Logarithmic amplification
Linear to Log LUT
10 bit to 8 bit
• Linearizes exponential xx-ray attenuation
Output Digital Number
250
• Difference signal is independent of incident xx-ray flux
Mask image:
I m = N 0e
Contrast image:
I c = N 0e
Subtracted image:
68
− µ bg t bg
− µ vessel t vessel − µ bg t bg
I s = ln( I m ) − ln( I c ) = µ vessel tvessel
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
200
150
100
50
0
0
200
400
600
800
Input Digital Number
69
1,000
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Digital
Fluoro
Digital Subtraction Angiography
• Temporal subtraction sequence
Mask
– First implemented mid 1970’
1970’s
Contrast Image
Subtraction
Image
Contrast agent
• Eliminate static anatomy
– Increase conspicuity
• Isolate and enhance contrast
– Lower contrast “load”
load”
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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12
TimeTime-dependent subtraction (DSA)
DSA examples
Subtracted
images
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DSA image manipulation / quantitation
74
Matched Filtration
• Pixel shifting
(correct for
misregistration)
Cmax
C(t)
Cavg
time
• Add anatomy
(visualize
landmarks)
Average ROI signal in image i.
ki = C(t) - Cavg
+
• Measurements /
densitometry
time
Image sequence and ROI
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Image weighting coefficients, ki
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Matched Filtration
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Image comparisons
k6 × I6(x,y)
Contrast
Image
k5 × I5(x,y)
Mask subtract
Image
k4 × I4(x,y)
k3 × I3(x,y)
+
k2 × I2(x,y)
k1 × I1(x,y)
Matched filter
Image
Single averaged output image
Selective dye
Image
High SNR at ROI position
Scaling factor ki
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Recursive filtration
Image Processing Operations
• Digital image buffer adds a fraction, k, of the incoming
image to the previous output image; temporal averaging
with exponentially decreasing signal
• Point
– Pixel to pixel manipulation
Iout(n)
(n) = k Iin(n)
(n) + (1(1-k) Iin(n(n-1) + (1(1-k)2 k Iin(n(n-2) +…
+….
×k
Iin(x,y)
(x,y)
• Local
+
– Small pixel area to pixel manipulation
Iout(x,y)
(x,y)
× (1(1-k)
• Global
feedback
– Large pixel area to pixel manipulation
Image
Memory
Buffer
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Spatial Filtration
80
Convolution
• Pixel by pixel multiplication and addition
of filter kernel with image:
• Low pass (smoothing)
• High pass (edges)
( N −1)/ 2
I out ( x ) =
∑ g( i ) I
in
( x + i)
i =− ( N −1)/ 2
• Bandpass (edge enhancement)
I out ( x ) = g ( −1) × I in ( x − 1) + g ( 0) × I in ( x ) + g(1) × I in ( x + 1)
• “RealReal-time”
time” filtration uses special hardware
and filter kernels of small spatial extent
I out ( x ) = g ( x ) * I in ( x )
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
Point sampling aperture:
Finite sampling aperture:
frequency response
frequency response
MTF
MTF
LSF
width: ∆ x ~ 0
1
1
Single element LSF
width: ∆x
0.6
0.4
0.2
height:
1/ ∆x
0
-0.2
0
0.5
1
1.5
2
Frequency
(units of 1/ ∆x)
2.5
3
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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sinc (x)
0.8
Modulation
Modulation
0.8
height:
1/ ∆x
82
0.6
0.4
0.2
0
-0.2
0
0.5
1
fN
fS
1.5
2
2.5
Frequency
(units of 1/ ∆x)
3
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Filter kernels
Low pass filtration – smoothing
Frequency response
1 and 3 element equal weight kernel
MTF
1
1 element
0.8
Single element LSF
width: ∆x
Modulation
height:
1/ ∆x
Three element LSF
width: 3 ∆x
• Convolve “normalized”
normalized” filter kernel with image
• Reduces high frequency signals
0.6
0.4
0.2
• Reduces noise variations
3 element
0
• Reduces resolution
-0.2
height:
1/(3∆
1/(3∆x)
0
0.2 0.4 0.6 0.8 1
Frequency
Units of 1/ ∆x
1.2
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2D Low pass filter kernel
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Variable weight lowlow-pass filter kernel
•Convolve “normalized”
normalized” filter kernel with image
Variable weight kernel
width:
height:
∆x
Output
1
1
1
10 10 10
1
1
4
7
10 10
1
1
1
1
1
1
10 10 10
1
1
4
7
10 10
1
1
1
1
1
1
10 10 10
1
1
4
7
10 10
1
1
1
1
1
1
10 10 10
1
1
4
7
10 10
1
1
1
10 10 10
1
1
4
7
10 10
1
1
1
10 10 10
1
1
4
7
10 10
**
÷9
Profile before
Frequency response
variable weight kernel
1
0.6 / ∆x
0.2 / ∆x
Break into parts:
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
0.6
0.4
0.2
0
-0.2
0 0.2 0.4 0.6 0.8 1 1.2
Frequency
Units of 1/∆
1/∆x
+
Profile after
Combined response
0.8
Modulation
Input
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
High pass filtration
88
HighHigh-pass filter kernel
Single kernel LSF
• Low pass filtered signal subtracted from
original signal
Frequency response
highhigh-pass filter
1
• High frequencies (edges) remain in image
-
Modulation
Highpass LSF
+
-
• Increased noise is apparent
Lowpass LSF
-1 -1 -1
-1
9
-1
-1 -1 -1
0.8
Difference
0.6
0.4
0.2
0
-0.2
0 0.2 0.4 0.6 0.8 1 1.2
Frequency
Units of 1/∆
1/∆x
Normalized 3x3 edge enhance kernel, Gain = 1
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2D high pass filter kernel
Example filtered images
•Convolve “normalized”
normalized” filter kernel with image
Input
Output
1
1
1
10 10 10
1
-1
-1
-1
1
1
1
10 10 10
1
1 -26 35 10 10
-1
9
-1
1
1
1
10 10 10
1
1 -26 35 10 10
-1
-1
-1
1
1
1
10 10 10
1
1 -26 35 10 10
1
1
1
10 10 10
1
1 -26 35 10 10
1
1
1
10 10 10
1
1 -26 35 10 10
**
1 -26 35 10 10
Unfiltered
Profile before
Edge enhanced
Smoothed
Profile after
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Image Processing Operations
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Global Image Processing
• Frequency domain processing
• Point
– Convolution becomes multiplication
– More efficient with kernels > 9x9
– Pixel to pixel manipulation
• Inverse filtering (deconvolution)
• Local
– e.g., veiling glare, scatter corrections
– Small pixel area to pixel manipulation
• Image translation, rotation and warping
• Global
– Correction of misregistration artifacts, pincushion
distortion, vignetting, nonnon-uniform detector response
– Large pixel area to pixel manipulation
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Inverse filtering
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Quantitative Algorithms
• 2D – FT methods:
–
–
–
–
• Stenosis sizing: length, area, densitometry
Measure PSF
Generate inverse filter
Multiply by 2D2D-FT of image
ReRe-inverse transform
• Distance measurements
• Density – time curve analysis
X-ray scatter PSF and inverse filter:
• Perfusion – functional studies
• Relative flow and volumetric assessment
• Vessel tracking
• CT with conecone-beam reconstruction
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Limits to Quantitation
Summary
• NonNon-linear / nonnon-stationary degradations
–
–
–
–
• Digital imaging is an essential part of
fluoroscopic and angiographic systems
Beam Hardening
Scatter
Veiling Glare
NonNon-uniform bolus / diffusion
• Limitations and advantages of fluoro digital
acquisition and processing must be
understood for maximum utilization
• Geometric effects
–
–
–
Pincushion distortion
Vignetting
Rotational accuracy (CT)
• DICOM standards are a must for the
integration of digital fluoroscopy in the clinical
environment and PACS
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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Summary
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
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References / further information
• Seibert JA. Digital Image Processing Basics, in A
Categorical Course in Physics: Physical and
Technical Aspects of Interventional Radiology, Balter
S and Shope T, Eds,
Eds, RSNA Publications, 1995
• Fluoroscopic / Fluorographic
image processing can provide
• Bushberg et.al. Essential physics of Medical
Imaging, Lippincott,
Lippincott, Williams & Wilkens,
Wilkens, Philadelphia,
2002
– Significant improvement of image quality
– Reduced dose (radiation and contrast)
– Enhanced image details
– DSA, roadmapping,
roadmapping, quantitative densitometry
– functional imaging, fluoro conecone-beam CT
2004 AAPM Pittsburgh, JA Seibert, Ph.D.
• Balter S, Chan R, Shope T. Intravascular
Brachytherapy / Fluoroscopically Guided
Interventions, Medical Physics Monograph #28,
Medical Physics Publishing, Madison, WI, 2002.
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