Transcript What do PET clinicians/researchers want? What can the PET

The Forefront in Image Processing:
PET/Molecular Approaches
Joel Karp
University of Pennsylvania
Sixth Annual NCI-Industry Forum
Quantitative Oncologic Imaging
April 7-8, 2005
Issues of Performance, Image
Processing, Quantification
• Performance of current-generation PET scanners
Global effects - data correction
Local effects - image reconstruction
Statistical and count-rate effects
• Self-consistency: instrument performs same day-to-day
• Cross-consistency: all instruments produce same result
• Comparing images (PET and CT) from different patients,
different instruments, and different institutes
What is Measured with PET
b
Random coincidence
True coincidence
(~2t . singles2)
a
Scattered coincidence
Coincidence?
Yab = Nab(AabTab + Sab + Rab)
What is measured
Normalization Attenuation
Record event
Trues Scatter Randoms
Reconstruct image from line-ofresponse (LOR) projection data
If there are N counts in
the image,
SNR ≠ N / (N)1/2
Signals from Different Voxels are Coupled
 Statistical Noise Does Not Obey Counting Statistics
Iterative reconstruction
Data
Ax = y
y
Differ
ence
Back-projection
c(k)
d(k)
Correction for
Attenuation,
Scatter,
Randoms
Update
^(k)
y
x (j k 1)  x (j k )
Forward projection
yi


  k x (kj ) 
1
yˆ
ai
 i

x(k)
k
k
k
,j
Image
Start
here
x(0)
Data Flow
DETECTOR
Philips Allegro: 616 x 29 crystals
DIGITIZER
POSITION
CALCULATOR
BINNER
COMPUTER
reconstruction
PACS archive
RAWVIEW
(52 bytes/event)
For A,B side of event
26 PMT energies/zone (26 bytes)
100M events = 5200 Mbytes
LISTVIEW
(8 bytes/event)
For A,B side of event
2D position (3 bytes)
timestamp
Energy
(1 byte)
TOF (1 byte)
100M events = 800 Mbytes
SINOGRAM
(80 Mbytes/frame)
R, Phi
(295x161x2 = 95 Kbytes)
Slice
(29^2 = 841)
100M events = 560 Mbytes (7 frames)
IMAGE
X,Y
(128x128 = 16 Kbytes)
100M events = 4 Mbytes (250 slices)
2D (septa) vs. 3D (no septa)
2D Imaging
S
3D Imaging
T
R
Low geometric sensitivity
Low Scatter and Randoms
High geometric sensitivity
High Scatter and Randoms
True
Scatter
Scatter decreases with high
energy threshold - depends
on energy resolution
Out-of-field activity increases randoms in 3D
Problem increases as bore size increases
-> less shielding
Singles
FOV 3D
mode
14
Random s relative
to 435 keV
12
10
8
6
4
2
0
200
Randoms ~ 2t
.
Singles2
250
300
350
400
450
Lower Energy threshold (keV)
• decreases with narrow timing window (2t)
• decreases with high energy threshold
• estimated (and subtracted) with 2nd (delayed) timing window
500
550
Count-rate Performance
70-cm long x 20-cm diameter
10 mCi dose
NEMA 2001 (body)
Noise Equivalent
Count-rate
NEC = T/(1+S/T+R/T)
NEC ~ SNR2
Philips Allegro
PET Imaging Performance
• Spatial resolution -> partial volume effect
intrinsic:
4-6 mm
reconstructed:
>10 mm
• Scatter fraction -> noise and bias (after correction)
2D:
10-20% SF
3D:
30-60% SF
• Sensitivity and count-rate capability -> statistical quality
25 - 100 kcps
or 5 M - 20 Mevents per 3 min frame
A
Scatter Correction
B
Before
Scatter
correction
After
Scatter
correction
Single Scatter - Model based correction
Calculate the contribution for an arbitrary scatter
point using the Klein-Nishina equation
Attenuation correction
with radioisotope
transmission scan
20 mCi 137Cs source - 662 keV
A = 1 / e -md
d = length of chord through tissue
m = attenuation coefficient
Attenuation correction for PET
Types of transmission images
Coincident photon Ge68/Ga-68
(511 keV)
Single photon Cs-137
(662 keV)
X-ray
(~30-140kVp)
high noise
15-30 min scan time
low bias
low contrast
lower noise
5-10 min scan time
some bias
lower contrast
no noise
1 min scan time
potential for bias
high contrast
Attenuation/Scatter correction
No AC or Scatter Corr
University of Pennsylvania PET Center
AC and Scatter Corr
Philips Allegro
Fore-FBP
Fully 3D Iterative
Reconstruction
improves image
quality
How about
quantification?
3D Ramla
NEMA NU2-2001 Image Quality Phantom
37 mm
28 mm
10 mm
foam
13 mm
22 mm 17 mm
Out-of-field Activity
Partial Volume Effect
Ideal Contrast
1
0.8
Contrast
0.6
3D-10 mm
Ctr-10mm
Max-10mm
3D-13mm
Ctr-13mm
Max-13mm
3D-17mm
Ctr-17mm
Max-17mm
3D-22mm
Ctr-22mm
Max-22mm
3D-28mm
Ctr-28mm
Max-28mm
0.4
0.2
0
0
2
4
6
FWHM (mm)
8
10
NEMA IEC Phantom
LOR RAMLA reconstruction
10-mm Hot Sphere - 4mm
13-mm Hot Sphere - 4mm
0.35
0.5
0.3
4-mm (non-LOR)
4-mm (LOR)
4-mm (LUT)
0.4
Contrast
Contrast
0.25
0.2
0.15
0.3
4-mm (non-LOR)
4-mm (LOR)
4-mm (LUT)
0.2
0.1
0.1
0.05
0
0
5
10
15
20
25
0
30
0
Background Variabili ty (%)
5
10
15
20
25
Background Variabili ty (%)
17-mm Hot Sphere - 4mm
22-mm Hot Sphere - 4mm
0.5
0.6
0.5
0.4
Vary relaxation parameter  from
0.2
0.00025 (top left) to 0.075
(bottom right)
4-mm (non-LOR)
4-mm (LOR)
4-mm (LUT)
Contrast
Contrast
0.4
0.3
0.3
0.2
4-mm (non-LOR)
4-mm (LOR)
30
Contrast vs. Noise
Iterative - Ramla
Filtered Backprojection (FBP)
1.7 cm hot sphere
2.8 cm cold sphere
Image processing
Filters for restoring the spatial frequency components
Low (left) Maximum gain = 2.5
Medium(middle)Maximum gain = 3.5
High gain (right) Maximum gain = 4.5
WF(f) = 1/MTF(f)
for f<fcut
WF(f) = 1/MTF(fcut) exp-kf 2
for f>fcut
k - parameter describing the Gaussian
roll-off
fcut - cutoff frequency
K, fcut -were bracketed from an analysis
of phantom data
Lesion contrast improves with filtering
no
low
med
Profile through the lesion
high
1 Mcts
no TOF
300 ps TOF
Time-of-Flight :
list-mode iterative
reconstruction
10 Mcts
5 Mcts
5Mcts TOF
1Mcts TOF
5Mcts
1Mcts
Challenges in comparing images
• Spatial resolution differences
partial volume - simple (approximate) correction
spatial recovery in reconstruction model adds noise
• Reconstruction algorithm
local convergence depends on algorithm and activity
• Accuracy of corrections - randoms, scatter, attenuation
depends on patient size and activity distribution
• Imaging protocol
scan acquisition time and delay post-injection
• Quantification - typically based on simple cylinder
QC - monitor and correct daily drifts
Activity calibration - counts/voxel/min -> nCi/ml
Count-rate corrections - dead-time
Challenges in comparing images
• Instrumentation in PET is constantly evolving
performance of new scanner >> older scanner
• Image data size is large - data transfer and archiving
PET: 4 Mbyte (with 4 mm3 voxels)
CT: 64 Mbyte (with ~1 mm3 voxels)
• DICOM
quantification (SUV) requires PT format (not NM)
manufacturers workstations still most practical
• Data analysis tools must be standardized and validated
region-of-interest
• Image processing
behavior must be understood - difficult to standardize