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
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
Image Reconstruction
detector
X
Object
being
imaged
Selected
point in the
object
Slides are not to be reproduced without the permission of the author
3-D
coordinate
system
Z
Y
Image Reconstruction
Known:
• Angle of projection
• Source-detector
distance
• Count value at point
•
Slides are not to be reproduced without the permission of the author
X
Y
A photon
from the
point is
detected
Image Reconstruction
Find:
• Contribution of each
point along the line, to
the projection
•
X
Slides are not to be reproduced without the permission of the author
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
•
Slides are not to be reproduced without the permission of the author
FBP Method of Tomographic
Reconstruction
Acquire Data
(projection)
Final Images
Back-Projection
Slides are not to be reproduced without the permission of the author
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
•
Slides are not to be reproduced without the permission of the author
Iterative Method of Tomographic
Reconstruction
Acquire Data
(projection)
Final Images
Initial guess
Guess image
Fails
criteria
Meets
criteria
Compare to original
projections
Slides are not to be reproduced without the permission of the author
ForwardProjection
Iterative Reconstruction Image Quality
•
•
•
Initial guess image
Number of iterations
Filtering
Iterations:
1
5
Blurry
10
20
100
Noise, artifacts
Image Filtering
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
High-pass filter
Image Filtering
•
Low-pass filter
High-pass filter
•
Band-pass filter
Restoration filter
Slides are not to be reproduced without the permission of the author
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)
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
Image Re-Orientation
Slides are not to be reproduced without the permission of the author
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)
Slides are not to be reproduced without the permission of the author
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
•
Slides are not to be reproduced without the permission of the author
Data Handling: Re-Orientation
•
•
Think of the task, not the tool
Consistency is critical
ECG-Gating
Slides are not to be reproduced without the permission of the author
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)
Slides are not to be reproduced without the permission of the author
In e.soft
Counts vs. Projection #
for Each Gate
Normal case
Slides are not to be reproduced without the permission of the author
Transient tachycardia
ECG-Gating Errors: Software tools
•
AI to interpret curves (at processing time)
In ECToolbox
Calculation of Ejection Fraction
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
Patient Motion
Slides are not to be reproduced without the permission of the author
Patient Motion
•
•
•
Vertical: correctable
Horizontal: partially correctable
Rotational: not correctable
Vertical
Horizontal
Rotational
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
Identifying Patient Motion
Slides are not to be reproduced without the permission of the author
Rows from all
projections
Columns from all
projections
Identifying Patient Motion
Cyclogram
Marked point on
transverse image
Slides are not to be reproduced without the permission of the author
Columns containing marked point,
from all projections
Automatic Image Processing
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
The End
Slides are not to be reproduced without the permission of the author
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
Slides are not to be reproduced without the permission of the author
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)
Slides are not to be reproduced without the permission of the author