Download (T)CT ECT CT PET

Rigshospitalet, Copenhagen
PET
(and PET/CT, and SPECT/CT)
Medical Imaging Systems
DTU, October 2010
PET
Søren Holm
Entrance 39
Ansv. fysiker, lic. scient
PET- and Cyclotron Unit, Rigshospitalet
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Photo: Flying Professor Andreas Kjær
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Overview (0):
Thanks to:
GE Medical Systems
CPS Innovation (Bernard Bendriem)
Siemens Medical Solutions
Philips Medico
Different kinds of tomography – PET in particular
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
Impact (www.impactscan.org)
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(T)CT
ECT
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SPECT
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CT
PET
PET
Computer(ized)
X-ray
ON
from:
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attenuation of photons
distribution of tracer
attenuation of photons
distribution of tracer
structure / anatomy
function / physiology
structure / anatomy
function / physiology
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Radiology
Nuclear Medicine
”Noise” in images depend on counts
5M
5k
Ken Krowlton, Domino Portraits, 1982
#dots recorded determine what details can be seen in image
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And for the ignorants: this is Groucho Marx
CT
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distribution of tracer
structure / anatomy
function / physiology
Map of Nuclides
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Positron
#protoner
for
8
PET
attenuation of photons
P
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PET is really ART:
Annihilation Radiation Tomography
α
511 keV
1955
β+
β+
positrondecay
e-
stable nuclides
β−
511 keV
electrondecay
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#neutroner
E = mc2
1905
11
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Result: Two photons on an almost straight line
that nearly contains the point of decay
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PET radiation detection
Positron Emission Tomography (?) or ART
We never detect the positrons directly, only the annihilation photons
Annihilation
A PET scanner counts coincidences
Radiation
Tomography
detector
d1
Coincidence
window
ee+
Tube or Line Of
Response (LOR)
13
yes
∆t ~4-12 ns
t2
detector
d2
coincidence
∆t
time
1
Coincidences
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|t1-t2|<∆t?
d1
d2
Inhalation of radioactive β +
gas
1991
t1
2
3
4
5
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Map of Nuclides
Time of Flight (TOF) PET
– more than just a coincidence...
tried and abandoned in ancient PET times (~1985)
idea commercially relaunched in 2006 by Philips
measure time difference between arrival at the 2 detectors
∆t = 600 ps = 0.6 ns
Annihilation point can be determined with an accuracy of
0.6 ns × 30 cm/ns / 2 ⇒ 9 cm (why “/2” ?)
det1
ee+
det2
Hence, more information available for image formation
Hypothesis: better image quality, or shorter scanning time, or
less injected activity
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Positron Emitting Isotopes
…magnified
Isotope
Half-Life
Production
Carbon-11
20.5 min
14N(p,α
α)11C
Nitrogen-13
10.0 min
16O(p,α)
α)13N
Oxygen-15
2.1 min
14N(d,n)15O
Fluorine-18
110 min
18O(p,n)18F
Gallium-68
68 min
Daughter of Ge-68 (271days)
Rubidium-82
1.3 min
Daughter of Sr-82 (25days)
α
α)
(F-), 20Ne(d,α
α)18F (F2)
• Small elements (C,N,O,F) allow “real” biochemistry
• Short half-lives make tracer production an integral part of PET
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(small) Cyclotron
Cyclotrons
Ion Source
-
Production of H
RF + magnetic field
Ion acceleration
Carbon foil
Electron stripping
-
H ⇒ H+ (protons!)
protons do irradiate a
Scanditronix 32 MeV
GE Minitrace 11 MeV
Target
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Production of 18F
Nuclear Reactions
Z
17F
18O
(p,n) 18F
14N (d,n) 15O
14N (p,α
α) 11C
16O (p,α
α) 13N
18F
64.49s
β+
109.77m
18O(p,n)18F
19F
20.3 ms
post FDG synthesis ≈ 20 GBq / 20 µA / 2h irr
15O
16O
17O
18O
122.24s
99.762
0.038
0.200
product
H218O (150 euro/g)
18F-
17O
18O
99.64
0.36
0.038
0.200
12C
13C
14C
98.89
1.11
5736 a
also used:
20Ne(d,α
α)18F
N
β+
≈ 0.7 GBq/µA sat
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target material
product
Ne (+ F2) gas (1 euro/liter)
18F-F
2
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Overview (1):
19F
20.3 ms
16O
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18F
109.77
96.9 m
% β+
99.762
β+
99.8 %
target material
15N
14N
9.96 m
20.3 m
YIELD ≈ 2 GBq / µA sat
96.9 % β+
99.9 % β+
13N
11C
20Ne
90.48
Cheap, but...
low specific activity
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Principle of image formation!
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
Projection
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Backprojection
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Eksempel på FBP
Filtered Back Projection af røntgendata (CT)
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Iteration principle
image space
image
estimate
old
forward
projection
model !!!
Iterative methods
image space
projection space
Iteration 1
projection space
estimated
projections
projection
measured
projections
Estimated
projection
new
UPDATE
COMPARE
image space
error
projection space
error
Measured
projection
Current
estimate
Compare
(e.g. – or / )
Update
Error
image
backprojection
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backprojection
Error
projection
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5
Iterative methods
image space
projection
Current
estimate
Iterative methods
Iteration 2
projection space
image space
projection
Estimated
Estimated
projection
projection
Measured
projection
Current
estimate
Compare
(e.g. - or / )
Update
projection space
Iteration N
Estimated
Estimated
Estimated
Estimated
projection
projection
projection
projection
Measured
projection
Compare
(e.g. - or / )
Update
Error
image
backprojection
Error
projection
Error
image
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Error
projection
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Iterative Recon versus FBP
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Overview (2):
FBP Methods
Iterative Methods
Implementation:
Reconstruction speed:
Imbedded corrections:
Simple
Very fast
None
Complex
Slow with ML-EM, fast with OSEM
anything you can model
Low-count image:
Reconstruction artifacts:
Noisy
Streak/spill artifacts
Less noise
None, regular activity distribution
FBP reconstructed
image
backprojection
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
OSEM reconstructed
image
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(IAEA meeting 1959)
Positron scanner (not PET)
(Brain tumor scanner at MGH 1955- )
Positrocephalogram (PCG)
of patient with meningioma
PCG:
A ”routine”
examination!
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Anger camera (Rev.Sci.Instr. 1957)
Gamma camera for
positron imaging:
NOT a recent
invention
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Gamma camera principle
position sensitive photon-detector
Principles
proposed by
Hal Anger 1959
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PARALLEL HOLE COLLIMATOR
used for planar imaging or tomography (SPECT)
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PET 1966 -
1968 PET
Positome I 1966
32 detectors
Advance 1993
12096 detectors
HRRT 2002
119,808 detectors
Therascan 1982
256 detectors
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$2002
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7
Special Brain scanner (HRRT)
Overview (3):
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
119.808
Detector
elements
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Coincidence Projections
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Data-Sorting Sinogram
Counts
Projection
Projection
+90°
Sinogram
Angle
Detector ring
0°
Position
-90°
Coincidence events determine sampling
paths (Lines Of Response)
Parallel Lines Of Response are sorted into a
row of count numbers (Projection),
representing the number of 511 keV photon
pairs detected.
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Each projection is entered as a row into a sinogram. A sinogram is an array which
stores the number of coincidence events for each detector position and each angle.
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Geometric correction
Sinogram
Interpolation needed before reconstruction
the center of the field of view.
The sampled LORs
are not equidistant,
but become closer
towards the edges.
r
y
Real
Space
φ
x
Project
Radon
transform
The projections
must be resampled
prior to or during
reconstruction
Point in center: straight line
Off-center: sine curve, amplitude proportional to radius
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Image Reconstruction
The Collimator
in ring PET
Image slice
Sinogram
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Static Frame Acquisition
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Dynamic Frame Acquisition
Set of slices in a single FOV
No patient table motion
Brain scan, tumor spot, heart examinations (myocardial
perfusion, metabolism and viability)
Set of slices over time in a single FOV (like a movie)
No patient table motion
Start data acquisition as the tracer is administered
Quantitative flow study (blood or other fluid): brain or heart
Cerebral dynamic PET scan
Tracer is 18F-labeled D2 dopaminergic receptor ligand
Finding: caudate and putamen normal tracer uptake
Adjacent brain PET static
slices
Gated Acquisition
time
Gated Acquisition
Set of slices over time in a single FOV (like a movie)
No patient table motion
Heart imaging without cardiac motion blurring
Myocardial perfusion, metabolism and viability
R-wave
R-wave
ECG
Heart cycle
Electrocardiogram
Gated slices of a cardiac PET scan
time
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List Mode data (LM)
Whole-Body Acquisition
List mode is an alternative way to store data during acquisition
In the sinogram, events are counted in each matrix element. In List Mode,
the two addresses (the detector numbers) of the two detectors involved are
written to a continuous datastream.
Every millisecond a timestamp is added to the stream
Signals from cardiac or respiratory monitoring may be added
Set of slices in multiple FOVs
Step-by-step tabletop motion during acquisition
Metastatic spread of cancer
After the acquisition, the LM-file can be replayed and sorted into sinograms
This allows dynamic and gated studies to be acquired without assumptions
about time frames and number of gating bins
In some cases it may even save raw data storage capacity
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Whole-body
scan
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Body Tomographic Planes
Tomographic Planes for the Heart
Frontal
or Coronal
Short Axis (SA)
Transverse,
Transaxial
Transverse
or Transaxial
Sagittal
Horizontal
Long Axis (HLA)
Frontal,
Coronal
Vertical
Long Axis (VLA)
Sagittal
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Comparison of
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Overview (4):
PET and SPECT:
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PARALLEL HOLE COLLIMATOR
+ PET
all directions
simultaneously
1: Higher sensitivity, with
”electronic collimation”
one direction at a time
+ SPECT
2: Fast dynamic scans
1: Dual isotopes possible
3: ”Biological” tracers with C11, N-13, O-15, F-18
2: Tc-99m generator + kits !
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Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
3: Less expensive to run
4: Quantitative technique,
with attenuation correction
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2D Imaging
Michelogram – direct and cross slices
The diagram shows all
possible combinations of
two detector rings.
The ones ”allowed” are
marked with a ”∗.
Combinations that add to
one sinogram are
connected with a line
between the points
High Resolution Mode
• Direct ∆Z = 0
• Cross ∆Z = ±1
• Limited Sensitivity
Shown examples are the
simple direct slices
(diagonal in diagram) and
High Sensitivity Mode
• Direct ∆Z = 0, ±2
• Cross ∆Z = ±1, ±3
• Increased Sensitivity
simple cross slices
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Direct
Cross
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2D / 3D
Michelogram – 2D ”span 7”
The number of
combinations
involved in
direct and cross
slices is known
as the ”SPAN”
-in this case 7
Note how the
sensitivity
decreases
towards the
edge of the axial
Field-of-View
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2D ACQUISITION MODE
septa employed
low acceptance solid angle
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Michelogram – 3D, RD11 and RD15
2D/3D Volume Acquisition
Detector ring
3D ACQUISITION MODE
no septa
High(er) acceptance solid angle
Detector ring
RD = Ring Difference
In 3D, where larger angles (ring differences) are allowed, the michelogram becomes
populated further away from the diagonal – eventually completely filled.
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2D acquisition
(Septa installed)
3D acquisition
(Septa removed)
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Axial sensitivity profile
”Overlap” in whole-body scanning
The overlapping
scans add up to
give a constant
sensitivity and
noise level
(except for top
and bottom)
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How to organize 3D data?
3D data – span 7
In 2D, data is a stack
of sinograms.
Event (X1,Z1,X2,Z2)-->
Also for 3D, points in the
michelogram may be
joined, reducing the
number of sinograms
stored.
z
r
Element r,θ of sinogram Z
θ
In 3D, data may be organized as a
set of sinograms (Z1,Z2) or as a
set of projection planes.
Event (X1,Z1,X2,Z2)-->
z
v
y
θ φ
u
x
Element u,v of plane θ , φ.
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u
Projection Planes
(u,v,θ,φ)
or
Z2
φ
Z1
v
u
φ
The image is no longer separable into a stack of 2D
slices, but must be reconstructed as a volume.
θ
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3-D PET: Reconstruction Problem
3D Data Organization
Collection of sinograms
(u,φ,Z1,Z2)
Note: 3D u = 2D r, 3D v = 2D z, and 3D φ = 2D θ.
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Problem: the cross-plane views are incomplete: they do
not view entire image volume.
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3-D PET: The Reprojection Solution
Forward Projected Data
θ=0
A two-pass algorithm:
θ= +/-1
θ= +/-2
θ= +/-3
θ= +/-4
Reconstruct in-plane data.
Forward project to complete cross-plane views.
3D reconstruction of completed projection planes.
(Kinahan and Rogers, IEEE TNS, 1988)
θ= +/-5
θ= +/-6
As θ increases, the
amount of forward
projection required to
complete data increases.
θ = +/-5 chosen to
balance data loss and
amount of data
completion.
θ= +/-7
θ= +/-8
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SSR = Single Slice Rebinning
FORE = FOurier REbinning
Uses the Frequency-Distance Principle, which states that the signal
at (ω,k) in the 2D Fourier Transform of a sinogram arises
primarily from activity at a distance r ∝ -k/ω from the center of
the field of view.
Simply assign data for each sinogram to the
”average position” – the transverse plane
containing the intersection with the axial
centerline.
Good approximation near scanner center
Large axial blurring towards the edge.
Not acceptable for high resolution imaging.
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the center of the field of view.
ω
r
y
φ
x
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k
2D-FT
Project
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3D-PET: The FORE Solution
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FORE Algorithm Steps
1. Apply corrections
2. For each sinogram…
– take 2D FFT.
– rebin sinogram according to
Frequency Distance
Principle
With r determined by the Frequency-Distance Principle, the axial
displacement of the data on a cross-plane sinogram at oblique
angle θ is calculated by ∆z = r tanθ.
3. Normalize and take 2D IFFT of
2D sinograms.
z
F
FT
sinogram
sinogram
image
4. 2D reconstruction of images
θ
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r (x,y)
2D Recon
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rebinned
sinogram
F -1
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Overview (5):
Image Reconstruction
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
Filtered Back-Projection methods:
• 2D Filtered Back-Projection (2D FBP)
• 3D Filtered Back-Projection (3D FBP)
Iterative Reconstruction methods:
• Maximum Likelihood Expectation Maximization (ML-EM)
• Ordered Subsets Expectation Maximization (OSEM)
• Full 3D iteration
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FAQ #1:
Spatial Resolution
Sources
Ability
to detect two objects
close together.
measured by phantoms
What is the smallest [something]
you can see in a PET scanner?
Images
Poor
resolution
Good
resolution
Line profiles of a point source
Wrong question /No simple answer
• Full Width at Half Maximum (FWHM) is
the measure of resolution (unit: mm).
Depends on the activity concentration/contrast
• Typical PET resolution: FWHM ≈ 5 mm.
FWHM
1/2
• Limitations are related to PET physics or
scanner technology and design.
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Radial Vs Tangential Resolution
Filtering is
a balance
between
noise and
resolution
(FWHM)
Radial resolution
FWHM
smaller ring diameter
cause more degradation
Tangential resolution
Distance from center of FOV
Plot of resolution versus radial
position
Smearing of point sources
within the FOV
20
4 mm
mm
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• Radial resolution degrades toward the edge of the field-of-view.
• Tangential resolution is almost constant within the field-of-view.
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Radial Vs Tangential Resolution
Results of modeling PSF in reconstruction
(example: Siemens “High definition” recon)
Crystal
block
Line of Response
parallel to crystals
axis
Detector ring
Line of Response
oblique to crystals
axis
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Mouse Heart PET/CT: 28g Mouse
Sanchez-Crespo A et al.
Image courtesy of David Stout
Crump Institute for Molecular Imaging, Los Angeles CA
Eur J Nucl Med Mol Imaging (2004)
A similar device has been installed at the Panum Institute
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Overview (6):
Scintillation detector
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
crystal
γ
light
guide
Dynodes with HV differences
∆V
out
(∆Ε)
in
photocathode
0 100
ns
200
γ - photon absorbed - converted to light – starts cascade af electrons
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Block Concept
Evolution of PET-detectors
Eγ
X = A / (A + B)
X1 X1 = 100 / (100 + 0) = 1.0
X2 X2 = 55 / (55 + 45) = 0.55
Therascan
4096+
1981 (89)
2005
4*4 block
20*35 mm
13*13 block
6*12 mm
total # 256
B
A
Biograph Hirez
1991
single detector
4*4 mm
total # 4096
total # 24336
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PET Detector Components
Flood Histogram / Position Map
Anger Logic
Crystal elements
and photomultiplier
tubes
A
X=
A+B
Detector
ring
Y
Detector block
Y=
C
C+D
Module
array
X
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Detector blocks
and front-end
electronics
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PET
Normalisation correction
(example: GE Advance)
– Compensates systematic efficiency variations
(system geometry, crystal efficiency)
PMT-gain
=
Detector module
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Energywindows
336 *
Uniformity
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Every pair of blocks is tuned for coincidence timing
Sinogram before
normalisation correction
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Sinogram after
normalisation correction
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Block contribution to sinogram
All the contributions from a single detector will be on a
line, angled as shown. Due to sensitivity differences a
certain ”rhombic” structure will be visible in the
sinogram.
i
cry
sta
l
Siemens Biograph uses 68Ge cylinder
One defective block
will show up as a
black, empty area in
the sinogram
i
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Effect of Normalisation
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Uniform phantom – very sensitive to
NORM
without
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with
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Normalisation
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Overview (7):
(digression on the origin of ring artefacts)
Red LOR’s connect block edges
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
(but first some theory of photon interactions with matter)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
Lines of Respons (LOR’s)
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Compton scattering
Two processes stop photons in tissue:
θ
Simple,
Absorption:
e-
γ
Compton
scattering:
dθ
γ’
no parameters
Energy of outcoming photon
can be calculated from the
scattering angle
Parameters
e-
γ
E
E’ and θ
θ
γ’
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γ
E’ (<E)
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Scatter depends on:
Klein-Nishina Cross sections
Photon energy
Next Slide
Scatter angle θ
Compton scattering is completely described by the
Klein-Nishina cross sections:
!
(α is incoming photon energy – in units of m0 = 511 keV)
Wild
physicists
ahead
(


1
E ' (θ ) = E * 

1 + α (1 − cos θ ) 
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10 keV
100 keV
)
2
2

 
α 2 1 − cos θ
d σ r02 
1
=
* 1 + cos 2 θ +



d Ω 2 1 + α (1 − cos θ )  
1 + α (1 − cos θ ) 
1MeV
Physics: definition of attenuation
absorbed
no interaction
Monoenergetic, parallel
beam of photons
scattered
Attenuation = Absorption + Scatter
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Linear attenuation coefficient: µ
Linear attenuation coefficient: µ
I0
I(x) = I0* e-µµx -
I0* e-µµx
x
absorbed
x in cm, µ in cm-1
HVL = ln(2)/µ
µ ∼ 0.7 / µ
detected
Water at 511 keV:
µ ∼ 0.10 cm-1
1 cm ~ 10%
scattered
HVL ~ 7 cm
Attenuation = Absorption + Scatter
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HVL is small compared to body dimensions
µtotal = µabs + µscatter
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Mass attenuation coefficient µm
Mass attenuation coefficient µm
(density: ρ g/cm3 )
The linear attenuation coefficient is proportional to density
ρ.
µm,,abs
Dividing by ρ gives a number independent of density.
and to
µm for ice, fluid water, and steam has the same value
To get
proprotional to
µm,,scatter:
µ from a µm – curve or table: multiply by ρ
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1/E3 (energy)
Z3
(atomic number)
slowly decreasing with E
independent of Z
(1/E)
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Mass attenuation coefficients
Trues Coincidence
in water (soft tissue) and lead
Scatter Coincidence
Vary linearly with
injected dose
Tc-99m
H2O
Pb
Tc-99m
PET
PET
Not all
counts are
created
equal...
Random Coincidence
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Vary quadratically with injected dose
there are
the good,
the bad,
and
the ugly
counts 114
and liniearly with coincidence window
19
The ideal scintillation detector has
High element no (Z)
High density (ρ)
Short decay of light
High ligth output
Low price
Good absorption
⇒
crystal
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PET Detector Scintillator Materials
light
guide
Crystal
material
High sensitivity
⇒
Less Deadtime, fast counting
⇒
Narrow coincidence window
⇒ less Randoms
⇒
Good energy resolution
⇒ Scatter rejection
NaI:Tl
BGO
LSO:Ce
LYSO
GSO:Ce
BaF2
max
Z
effective
Z
53
83
71
71
64
55
51
73
66
54
59
54
density
g/cm3
output
decay time
photons/keV
ns
3.7
7.1
7.4
5.4
6.7
4.1
230
300
40
53
56
0.8
40
8
28
28
7.5
2
dynodes
115
photocathode
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Scintillator Light Output
116
Noise Equivalent Counts (NEC)
When correcting for randoms
and scatter, counts are
subtracted
But the noise (uncertaincy) is
added!
Correction
The result is of less value than
if the same #counts were
measured directly
NEC is the number of GOOD
counts to which the result is
equivalent.
Measured
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Corrected
NEC
117
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119
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Formula for NEC
2
NEC =
[Trues * (1 − Scatterfraction)]
[Trues + 2 f * Randoms]
NB: ”Trues” in this formula contains scatter
Scatterfraction: from 8% (2D) to 50% (3D)
f:
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fraction of FOV, covered by object
20
NEC reflects the noise!
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Overview (8):
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
no interaction
absorbed
scattered
||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
||
Attenuation : Too few photons
Scatter: too many, but misplaced photons
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PET is a quantitative technique
124
Attenuation in a homogenous medium
(although most people don’t care these days...)
without
attenuation
IF all the necessary corrections are applied:
Deadtime
Geometry, Normalization
Randoms subtraction
Attenuation and scatter
Decay
…
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with
attenuation
(or with perfect
correction)
C oun ts
12
8
Without attenuation
Attenuation, µ=0.16cm -1
4
0
-8
-4
0
4
8
Position (cm)
125
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21
Anthropomorphic phantom
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BEFORE Correction
128
P1 = exp(-µx)
Attenuation
Correction
AFTER Correction
(r,θ)
µ
x
Sinogram
Angle
+90°
L-x
L(r,θ)
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Conclusion:
Correction may cure the patients…
129
P2 = exp(-µ(L-x))
0°
Position
-90°
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Probability of a coincidence:
Probability of a coincidence:
P(r,θ) = P1 * P2
P(r,θ) = P1 * P2
= exp(-µx) * exp(-µ(L-x)) = exp(-µL) [independent of x !]
= exp(-µx) * exp(-µ(L-x)) = exp(-µL) [independent of x !]
- and therefore easily measured with external (rotating) pin/point source
- and corrected: each line of response multiplied by its own exp(µ
µL)
P1= exp(-µx)
P1= exp(-µx)
Sinogram
Sinogram
x
Angle
µ
+90°
Angle
+90°
µ
L-x
0°
0°
L(r,θ)
L(r,θ)
P2= exp(-µ(L-x))
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-90°
Position
131
P2= exp(-µ(L-x))
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-90°
Position
132
22
GE uses 68Ge Pin sources
If µ is not a constant (and it IS NOT):
x
L− x
0
x
P1 * P 2 = exp( − ∫ µ ( x)dx) * exp( −
∫
L
µ ( x)dx) = exp(− ∫ µ ( x)dx)
0
still independent of the source position
x
P1 = exp( − ∫ µ ( x)dx)
GE Advance and GE Discovery LS
0
Transmission Sinogram
GE 4096 (by hand)
(3 pins, automated complex robotic arm)
+90°
Angle
µ
0°
L(r,θ)
GE Discovery ST etc.
(1 pin in trunk)
L
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-90°
x
Position
133
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CT scans are µ-maps (in HU)
Conversion of CT Numbers to µ
They can be used for attenuation correction – essentially noise free
But:
µ(511 keV) ≠ µ(80 keV) , therefore a scaling is required
Soft tissue and bone (and contrast) scale differently
Not one simple scaling factor, but a lookup table for µ(HU; kVp)
100
Mass attenuation cm2/g
Water, scatter
Water, photo
10
134
Water, total
Bone, scatter
Bone, photo
Bone, total
For CT values < 100 ,
materials are assumed
to have an energy
dependence similar to
water
For CT values > 100,
material is assumed to
have an energy
dependence similar to a
mixture of bone and
water
0.200
Attenuation at 511keV
P 2 = exp( − ∫ µ ( x)dx)
0.150
0.100
Water/air
Bone/Water
0.050
1
10
100
1000
0,1
The green line shows
the effect of using water
scaling for all materials
0.000
-1000
-500
0
500
1000
CT number measured at 140kVp
0,01
Energy in keV
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Scatter in
Attenuation Correction Benefits
•
•
•
•
Non-corrected
PET image
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SPECT
Provides PET images closer to real radiotracer distribution
Corrects attenuation-introduced geometric artifacts
Improves lesion detection in deep organs
Allows absolute quantitative PET studies
appears ”inside” the object
PET
may appear outside object
Attenuation-corrected
PET image
Better Image Quality
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23
Scatter korrektions teknikker
Scatter Correction by Function Fitting
Metz / Wiener filtering
Asymmetrical energy window
Dual energy window
Dual photopeak window
Weighing of detected events (WAM)
Channel ratio method
Photopeak energy distribution analysis
Position dependent scatter correction
Stationary / Non-stationary deconvolution
Iterative reconstruction techniques
Neural network
Pixel by pixel spectral analysis
Regularized deconvolution-fitting method
Scatter-free imaging (CFI)
Holospectral imaging
Factor analysis of medical image sequences
Fit data to projection tails
This requires tails!
More elaborate methods model the scatter from detailed knowledge of the
object
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Scatter: The Problem in 3D
Overview (9):
2D: 20 kcps (18 true, 2 scatter)
3D: 200 kcps (135 true, 65 scatter)
3D: 26 kcps (0 true, 26 scatter)
Reconstruction from projections
Positron imaging history
Sinograms – various types of acquisitions
2D/3D, FBP vs. iterative recon methods, image presentation
Resolution (how small ... can you see)
Scanner physics (how to optimize a PET scanner)
Good counts, bad counts (!) and noise equivalent counts (NEC)
Attenuation and scatter correction of PET-data
PET/CT scanners (PET/MR, and SPECT/CT)
Sources outside FOV very significant.
Deconvolution or modeling methods cannot see this
activity.
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Good reasons to combine
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Functional Images
PET and CT:
PET Images
0:
1:
2:
3:
“abnormal activity”
Because it is possible (MR more difficult)
Improved attenuation correction in PET
Complementary information
structure + function
anatomy + physiologi/biochemistry
Exact localization (”free” coregistration)
Weather Patterns
“weather activity”
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24
Structural Images
Fusion Images
CT Images
Discovery PET/CT Images
“precise body’s anatomy”
“abnormal activity and precise body’s
anatomy”
Geographic Map
USA Weather Map
“precise outlines of the states”
“intense weather and precise
outlines of the states”
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PET/CT scanner:
146
PET/CT (or SPECT/CT) scanner:
168 cm
First “physicist’s” PET-CT was constructed by Townsend, Beyer,
Kinahan et al in Pittsburgh 1994-
200 cm
We (Rigshospitalet) got a prototype from GE in 2001
Today commercial devices exist from:
182 cm
(Townsend et al 2004)
GE
Siemens-CTI
Philips
and a few others
- with more than 2000 installations worldwide
Anatomical localisation of tracer – Attenuation correction
Since 2005, almost NO stand-alone PETs have been installed
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combines two modalities in one gantry, common axis, common bed
allows precise fusion of images
provides
structure + function in the same image
can use CT for attenuation correction of PET /SPECT)
147
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PET/CT: GE Discovery LS
148
Inside Discovery LS
Advance NXI + Lightspeed plus
Detector
material
BGO
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25
PET/CT
PET –CT alignment /registration
RH (4)
Bispebjerg (2)
Næstved
Herlev (2)
Not likely to
change
spontaneously.
Århus PET (2)
Hillerød
Århus NUK
Gentofte
Herning?
Vejle (2)
For GE and
Siemens: Only
opened by
service. Must
be measured
after ”invasive
procedures”
Glostrup?
Philips Gemini (KAS Herlev)
(Allegro + MX8000 D)
Siemens Biograph 16 (RH)
Odense (3)
Philips Gemini
designed to
scan in the
separated
position also.
Ålborg
Køge
(Hamlet)
Århus Skejby
Hvidovre?
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GE Discovery ST
GE
(Milano San Raffaele)
151
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Siemens PET – CT alignment
PET – CT alignment
GE Discovery aligns
CT with PET transmission
using the built-in Ge-68 line
sources.
Siemens Biographs
have no transmission
sources.
No (other) activity is required
The phantom contains 5 glass
spheres
Instead 2 Ge-68 line
sources mounted in
a black box are used.
Newest GE-scanners use
similar phantom with Ge-68
spheres
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They are visible on both
PET and CT, and an
automated algorithm
provides the correction
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Philips PET – CT alignment
154
Small Animal PET-CT
Special service tool holding 6 Na-22 point sources
Registration performed at 1 year interval
CT resolution down to 15 µ
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PET resolution < 2 mm
156
26
Potential PET-MR configurations
PET-MR: the ”cheap” solution
Based on: Eur J Nucl Med Mol Imag (2009) Suppl 1 S86
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(real) PET-MR is coming
158
(real) PET-MR is coming
Siemens has a prototype combination:
3 T MR scanner with brain coil
PET ”insert” between coil and magnet
Requires detector insensitive to magnetic field
Attenuation correction is a challenge: MR does not
describe density, and coil is not measured
Whole Body model is now being tested
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Presented at
EANM 2008 by
B. Pichler
159
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DOI measurement with APD
PSAPD
(Depth of Interaction – to avoid parallax-errors)
Position Sensitive Avalanche Photo Diode
Presented at
EANM 2008 by
A. Del Guerra
From: Dokhale et al. Phys Med Biol 49 (2004); 4293-4304
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27
Basic PET Detector SSPMs based
Solid-state photo-multipliers
(SSPMs) can be fabricated from
small silicon sub-pixels to replace
PMTs making them attractive for
PET+MR and TOF-PET:
• fast, low-jitter time response
• magnetic field immunity
• small form-factor
Coincidence
timing res.
• Readout circuits/ASIC
development
Magnetic
Sensitivity
• Multiplexing options
Noise
• Handling multi-crystal events
Technology
Maturity
• MR compatible architecture
25%
50%
50%
550ps
350ps
1000ps
106
105-106
102
100 mm
2 mm
2 mm
High
Low
Low
Gain
Profile
Technical challenges:
SSPM
Detection
efficiency
Next years fashion?
APD
PMT
Low
Low
High
40 yrs
4 yrs
8 yrs
Low
High
High
From:
Cost
MIC2009
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SPECT/CT (GE Hawkeye)
SPECT/CT
164
X-Ray Tube is mounted on the same
gantry as the gamma cameras
serves the same dual purpose of
A ‘CT scan’ takes 15 s / cm
With 1 inch crystals, “PET” is possible
Anatomical localisation of tracer and
Attenuation correction
AC is not as simple or ”exact” as in PET
- because we have only one photon
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BrightView XCT SPECT/CT
SPECT/CT
Siemens
Symbia
166
Philips
Precedence =
Skylight SPECT + Brillance CT
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28
PET / SPECT / CT
PET / SPECT / CT
for small animals
Mediso
Budapest
Exhibition at
EANM 2008
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29