The Forefront in Image Processing: PET/Molecular Approaches

Joel KarpUniversity of Pennsylvania

Sixth Annual NCI-Industry ForumQuantitative Oncologic ImagingApril 7-8, 2005

Issues of Performance, Image Processing, Quantification

• Performance of current-generation PET scannersGlobal effects - data correctionLocal effects - image reconstructionStatistical 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

Yab = Nab(AabTab + Sab + Rab)

What is Measured with PET

a

b

What is measured Trues

True coincidence

Scattered coincidence

Random coincidence

(~2 . singles2)

Coincidence?

Record event

Normalization Attenuation RandomsScatter

Signals from Different Voxels are Coupled Statistical Noise Does Not Obey Counting Statistics

If there are N counts in the image,

SNR ≠ N / (N)1/2

Reconstruct image from line-of-response (LOR) projection data

Iterative reconstruction

x(0)

Correction for Attenuation,

Scatter, Randoms

yData

A x = y

y(k) x(k)

Image

Forward projection

(k)

Difference c(k)

Update

Back-projection

^

Start here

xj(k+1) =xj

(k) +λkxj(k) yik

ˆ y ik−1

⎛ ⎝ ⎜ ⎜

⎞ ⎠ ⎟ ⎟ aik , j

DETECTOR

DIGITIZER

POSITION CALCULATOR

BINNER

COMPUTER

RAWVIEW (52 bytes/event) For A,B side of event26 PMT energies/zone (26 bytes)100M events = 5200 Mbytes

LISTVIEW (8 bytes/event) For A,B side of event2D 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)

Data Flow

Philips Allegro: 616 x 29 crystals

IMAGE X,Y (128x128 = 16 Kbytes)100M events = 4 Mbytes (250 slices)

reconstructionPACS archive

2D (septa) vs. 3D (no septa)

2D Imaging 3D Imaging

Low Scatter and Randoms High Scatter and RandomsLow geometric sensitivity High geometric sensitivity

0

5000

10000

100 300 500Energy (keV)

TrueScatter

S T R

Scatter decreases with high energy threshold - depends on energy resolution

Out-of-field activity increases randoms in 3D

Singles FOV 3D

mode

0

2

4

6

8

10

12

14

200 250 300 350 400 450 500 550

Lower Energy threshold (keV)

Randoms relative

to 435 keV

Randoms ~ 2 . Singles2

• decreases with narrow timing window (2)• decreases with high energy threshold• estimated (and subtracted) with 2nd (delayed) timing window

Problem increases as bore size increases-> less shielding

Count-rate Performance

0

50

100

150

200

0.0 0.1 0.2 0.3 0.4 0.5Activity concentration (uCi/cc)

Randoms

Trues

NEC

Scatter

3.7 7.4 11.1 14.8 (kBq/ml)

Noise Equivalent Count-rate

NEC = T/(1+S/T+R/T)

NEC ~ SNR2

Philips Allegro

10 mCi dose70-cm long x 20-cm diameter

NEMA 2001 (body)

PET Imaging Performance• Spatial resolution -> partial volume effect

intrinsic: 4-6 mmreconstructed: >10 mm

• Scatter fraction -> noise and bias (after correction)2D: 10-20% SF3D: 30-60% SF

• Sensitivity and count-rate capability -> statistical quality25 - 100 kcps or 5 M - 20 Mevents per 3 min frame

Scatter CorrectionAA

BB

Single Scatter - Model based correctionCalculate the contribution for an arbitrary scatter point using the Klein-Nishina equation

BeforeScattercorrection

AfterScattercorrection

Attenuation correction with radioisotope transmission scan

20 mCi 137Cs source - 662 keV

A = 1 / e -d

d = length of chord through tissue = attenuation coefficient

Attenuation correction for PETTypes of transmission images

Coincident photon Ge-68/Ga-68

(511 keV)

high noise15-30 min scan

timelow bias

low contrast

Single photon Cs-137

(662 keV)

lower noise5-10 min scan

timesome bias

lower contrast

X-ray(~30-140kVp)

no noise1 min scan timepotential for bias

high contrast

Attenuation/Scatter correction

University of Pennsylvania PET Center

No AC or Scatter Corr AC and Scatter Corr

Philips Allegro

Fully 3D Iterative Reconstruction improves image

quality

Fore-FBP

3D Ramla

How about quantification?

NEMA NU2-2001 Image Quality Phantom

Out-of-field Activity

13 mm

10 mm

17 mm

28 mm22 mm

37 mm foam

Partial Volume Effect

0

0.2

0.4

0.6

0.8

1

0 2 4 6 8 10

Ideal Contrast

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

Contrast

FWHM (mm)

NEMA IEC Phantom

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0 5 10 15 20 25 30

10-mm Hot Sphere - 4mm

4-mm (non-LOR)4-mm (LOR)4-mm (LUT)

Contrast

Background Variability (%)

0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30

13-mm Hot Sphere - 4mm

4-mm (non-LOR)4-mm (LOR)4-mm (LUT)Contrast

Background Variability (%)

0

0.1

0.2

0.3

0.4

0.5

0 5 10 15 20 25 30

17-mm Hot Sphere - 4mm

4-mm (non-LOR)4-mm (LOR)4-mm (LUT)

Contrast

Background Variability (%)

0

0.1

0.2

0.3

0.4

0.5

0.6

0 5 10 15 20 25 30

22-mm Hot Sphere - 4mm

4-mm (non-LOR)4-mm (LOR)4-mm (LUT)

Contrast

Background Variability (%)

LOR RAMLA reconstruction

Vary relaxation parameter from 0.00025 (top left) to 0.075 (bottom right)

Contrast vs. Noise

1.7 cm hot sphere 2.8 cm cold sphere

Iterative - RamlaFiltered Backprojection (FBP)

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

Profile through the lesion

Lesion contrast improves with filtering

no

low

med

high

Time-of-Flight : list-mode iterative

reconstruction

5Mcts1Mcts1Mcts TOF

5Mcts TOF

no TOF 300 ps TOF1

Mct

s5

Mct

s10

Mct

s

Challenges in comparing images• Spatial resolution differences

partial volume - simple (approximate) correctionspatial recovery in reconstruction model adds noise

• Reconstruction algorithm local convergence depends on algorithm and activity

• Accuracy of corrections - randoms, scatter, attenuationdepends on patient size and activity distribution

• Imaging protocolscan acquisition time and delay post-injection

• Quantification - typically based on simple cylinderQC - monitor and correct daily driftsActivity calibration - counts/voxel/min -> nCi/mlCount-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)

• DICOMquantification (SUV) requires PT format (not NM)manufacturers workstations still most practical

• Data analysis tools must be standardized and validatedregion-of-interest

• Image processingbehavior must be understood - difficult to standardize