1. No category

Myocardial Perfusion
Image Reconstruction and Processing
(What is Your Computer Doing?)
Russell Folks, BS, CNMT, RT N
Emory University School of Medicine
Disclosure:
The speaker receives royalties from sale of computer software:
ECToolbox, ExSPECT-II
Topics
•
•
•
•
•
•
Image Reconstruction
Image Filtering
Image Re-Orientation
ECG-Gating and EF Calculation
Motion Correction
Process Automation
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Image Reconstruction
Definition:
• Generation of 3-D count distribution
from 2-D input data acquired over at
least 180 degrees
Practical:
• planar projections used to produce
transaxial (= transverse) slices
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Image Reconstruction
detector
X
Object
being
imaged
Selected
point in the
object
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3-D
coordinate
system
Z
Y
Image Reconstruction
Known:
• Angle of projection
• Source-detector
distance
• Count value at point
•
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X
Y
A photon
from the
point is
detected
Image Reconstruction
Find:
• Contribution of each
point along the line, to
the projection
•
X
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Y
photons in
front and in
back also
detected
Image Reconstruction Methods
•
Analytic methods
– Filtered back-projection (FBP, commercially
implemented)
– Other algorithms
Efficient
• Faster
• FBP: Subject to streaks and interference
between adjacent pixels of different activity
•
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FBP Method of Tomographic
Reconstruction
Acquire Data
(projection)
Final Images
Back-Projection
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Sinogram,
Fourier transform
Filtering
Image Reconstruction Methods
•
Iterative methods
– Maximum Likelihood-Expectation
Maximization (MLEM)
– Ordered Subsets-Expectation Maximization
(OSEM)
Can better account for attenuation, scatter,
detector response
• Sensitive to initial estimate
•
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Iterative Method of Tomographic
Reconstruction
Acquire Data
(projection)
Final Images
Initial guess
Guess image
Fails
criteria
Meets
criteria
Compare to original
projections
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ForwardProjection
Iterative Reconstruction Image Quality
•
•
•
Initial guess image
Number of iterations
Filtering
Iterations:
1
5
Blurry
10
20
100
Noise, artifacts
Image Filtering
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Filtering Basics: Frequency
Low frequency
High frequency
Image courtesy of
James Galt, Ph.D.
Filtering Basics: Frequency
Low frequency
Signal
Low-pass filter
Preserve low frequency
High-pass filter
Preserve high frequency
Filtering Basics: Frequency
Input image
diagnostic info
Preserve with:
Low-pass filter
Contrast
Edges, detail, noise
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High-pass filter
Image Filtering
•
Low-pass filter
High-pass filter
•
Band-pass filter
Restoration filter
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Filter Types Commonly Implemented
•
Ramp
– linear
•
Hanning
– Cutoff Frequency: point at which the filter is
defined to be zero
•
Butterworth
– Critical Frequency: point at which the filter
starts to roll-off toward zero
– Power Factor: steepness of the filter roll-off
(NOTE: power factor = 2 × order)
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Butterworth Filters
critical frequency = 0.4 1/cm
for all filters
1
Butterworth Filter
0.8
Power Factor:
0.6
20
5
2
0.4
0.2
0
0
0.2
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0.4
0.6
Frequency (1/cm)
0.8
1
Image courtesy of
David Cooke, MSEE.
Butterworth Filters
1
power factor = 10 for all filters
Butterworth Filter
0.8
0.6
0.2 1/cm
0.4 1/cm
0.6 1/cm
0.4
0.2
0
0
0.2
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0.4
0.6
Frequency (1/cm)
0.8
1
Image courtesy of
David Cooke, MSEE.
Image Filtering
Power = 5
C.F.
0.22
C. F. = .52
Power
20
C.F.
0.52
Power
5
C.F.
0.82
Power
2.5
Filtering in Myocardial SPECT
•
Before reconstruction
– Applied to planar projections
During reconstruction
• After reconstruction
•
– Applied to transaxial images
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Image Re-Orientation
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Image Re-Orientation
Definition:
• Extraction of counts from transaxial data,
along oblique-angle planes
Practical:
• Transaxials used to produce sagittal /
coronal (body) or VLA / HLA / SA
(myocardial)
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Image Re-Orientation Requirements
Accurate definition of planes
• Consistent definition of planes (stress and
rest planes should match (usually))
• Result: enough slices and slice width to
characterize the heart
•
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Data Handling: Re-Orientation
•
•
Think of the task, not the tool
Consistency is critical
ECG-Gating
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Beat Acceptance Window
Avg R-R interval found
20% acceptance
window (use with
extra bin)
100% acceptance
window
No acceptance window
(accept all beats)
Handling Arrhythmic Beats
Avg R-R interval found
Each incoming beat is
compared to the average RR interval
Early beat: no counts are
added to later bins
Late beat: temporal blurring in
later bins due to mixed
counts
ECG-Gating Errors: Software tools
•
Plot of heart rate variation (at recon time)
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In e.soft
Counts vs. Projection #
for Each Gate
Normal case
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Transient tachycardia
ECG-Gating Errors: Software tools
•
AI to interpret curves (at processing time)
In ECToolbox
Calculation of Ejection Fraction
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Myocardial Boundary
Definition
•
Sampled
pixels
Max count is sampled
around the myocardium
LV
chamber
Left Ventricular
Short axis slice
LV
wall
Myocardial Boundary
Definition
Max count is sampled around
the myocardium
• Location is used to define
endocardial and epicardial
edges
Epicardial
Boundary
•
Endocardial
boundary
LV
chamber
Left Ventricular
Short axis slice
LV
wall
Endocardial and Epicardial Surfaces
in 3-D
•
•
•
Define boundaries for
each gate
Determine ED and ES
frames
Track change in volume
across cardiac cycle
Image courtesy of: University of
Michigan Health System
Volume Curve
•
•
•
Shape is general indication
of cardiac cycle
Fourier curve fit can
improve ES identification
8/16 frames not enough for
diastolic function analysis
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Patient Motion
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Patient Motion
•
•
•
Vertical: correctable
Horizontal: partially correctable
Rotational: not correctable
Vertical
Horizontal
Rotational
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Motion Artifacts
stress
•
Horizontal motion
rest
stress
•
Vertical motion
rest
Motion Correction at Acquisition
(Prevention)
•
•
•
Multiple acquisitions, use only those with
no motion
Limit scan time
Make the patient as comfortable as
possible
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Motion Correction Methods
•
Look at differences between adjacent projections
– correlation function
– Phase-only matched filtering
•
Track the heart, frame-to-frame
– Marked center point (diverging squares method)
– Circle ROI (2-D fit method)
•
Manual frame shift
– Visual frame-to-frame comparison
– Color overlays
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Identifying Patient Motion
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Rows from all
projections
Columns from all
projections
Identifying Patient Motion
Cyclogram
Marked point on
transverse image
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Columns containing marked point,
from all projections
Automatic Image Processing
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Automatic Image Processing
Automate common tasks (batch process)
• Interpret the task as a computer
algorithm
•
– agree on a standard path and methods
•
Systematize relevant knowledge
– normal database, expert system
•
Recognize a pattern in the data
– feature extraction, neural network
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Automatic Image Processing
Even automatic software assumes the user
is knowledgeable
• Understand when to intervene
•
– To change parameters such as filters
– With image corrections
– With additional QC tests
– With service calls
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The End
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Hanning Filters
1
Hanning Filter
0.8
0.6
0.4 1/cm
0.4
0.6 1/cm
0.822 1/cm
0.2
0
0
0.2
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0.4
0.6
Frequency (1/cm)
0.8
1
Image courtesy of
David Cooke, MSEE.
Filtering Terminology
•
Nyquist Frequency
– The highest possible frequency that may be
faithfully displayed in a digital image
•
Sampling Theorem
– The Nyquist frequency is always 0.5
cycles/pixel (2 pixels/cycle)
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