Quantitative measures of novel tracers

Will Hallett

Some controversial issues

Study design / Number of subjects / Radiation doseSubject / Production / ResourcesImage noiseWhat activity should we inject?Go/no go?Reconstruction parameters?

Study design

Typically we are not looking for potential lesions (not an observer problem)

Statistical test on an outcome measure from group(s) of subjects under more than one condition (e.g. on and off a drug)

If the states are the same (close) test-retest difference mean variance:

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- Ideally small compared to the size of the effect to detect

Reproducibility only useful if useful information retained (Reliability)

Study design

Typically a measured outcome will depend on

Calibration (scanner etc) Biology (underlying uptake – volume of distribution – SUV)Image noise (detection efficiency, dose)Other factors – subject motion, metabolite analysis etc

If these are all uncorrelated (not always)

Variance of the outcome mean will be the sum of the variances from these factors and decrease with the number of subjects

Image noise

Tomographic Image noise variance �����.

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Depends also on scanner efficiency, reconstruction

Voxel size dependence worse than ‘cut and count’

TOF partially recovers this – localisation of events

1170MBq injected, 2.8-1.5 kBq/ml

170MBq injected (F18)

Typical brain scan around 2kBq/ml

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NEMA NECR for different scanners

NEMA NECR for different scanners

Effect of filtering (‘smoothing’)

All pass

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1300 pixels in striatum VOI all pass mean

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5mm sd

5mm Gaussian

Note effect of subject movement in last few frames

Effect of reducing the dose – simulation from real data

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10101001000101001110101001010100111001101101011010100010101010010101010010101010101101010101000101010101101010100011010101010100

10101001000101001110101001010100111001101101011010100010101010010101010010101010101101010101000101010101101010100011010101010100

10101001000101001110101001010100111001101101011010100010101010010101010010101010101101010101000101010101101010100011010101010100

10101001000101001110101001010100111001101101011010100010101010010101010010101010101101010101000101010101101010100011010101010100

211 MBq scan

Sim. 106 MBq x 2 Sim. 52 MBq x 4 Sim. 26 MBq x 8 Sim. 13 MBq x 16

Subdivide list mode data into 2, 4, 8 and 16 ‘low dose’ data sets

Effect of reducing the dose

211MBq

13MBq

The Relationship Between Radioactive Dose and Precision of Outcome Parameters in Quantitative PET Imaging of the Human Brain Y.H. Nai, M.L. Cunneen, R.S. Dimber, W.A. Hallett, G.E. Searle BrainPET 2011 meeting

211MBq

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Effect of reducing the dose on outcome measure – simulation from real data

211MBq

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13MBq

Effect of reducing the dose on outcome measure – simulation from real data

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Injected activity (MBq)

BP

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Effect of reducing the dose on fitting error– simulation from real data

Effect of the dose – phantom studies

Instrumentation factors affecting variance and bias of quantifying tracer uptake with PET/CT R. K. Doot, J. S.

Scheuermann, P. E. Christian,J. S. Karp, P. E. Kinahan 6035 Med. Phys. 37, p 6035 2010

Purpose: …repeatability and reproducibility of serial PET measures of activity as a function of object size, acquisition,

reconstruction, and analysis method on one scanner and at three PET centers using a single protocol with long half-

life phantoms.

Methods: The authors assessed standard deviations SDs and mean biases of consecutive measures

of PET activity concentrations in a uniform phantom and a NEMA NU-2 image quality IQ

Phantom... Each experimental set consisted of 20 consecutive PET scans…

An equation was derived to estimate the SD of a new PET measure from a known SD based on the ratios of available

coincident counts between the two PET measures.

Results: For stationary uniform phantom scans, the SDs of maximum RCs were three to five times

less than predicted for uncorrelated pixels within circular regions of interest ROIs with diameters

ranging from 1 to 15 cm. For stationary IQ phantom scans from 1 cm diameter ROIs, the average

Reproducibility of GE DSTE, Philips Gemini TF, and Siemens Biograph Hi-REZ PET/CT scans of the same IQ phantom,

with similar acquisition, reconstruction, and repositioning among 20 scans, were, in general, similar mean and

maximum RC SD range 2.5% to 4.8%.

Conclusions: Short-term scanner variability is low compared to other sources of error. There are

tradeoffs in noise and bias depending on acquisition, processing, and analysis methods. The SD of

a new PET measure can be estimated from a known SD if the ratios of available coincident counts

between the two PET scanner acquisitions are known and both employ the same ROI definition.

Results suggest it is feasible to use PET/CTs from different vendors and sites in clinical trials if they

are properly cross-calibrated.

For same conditions Standard Error ~1/sqrt(counts)

Doot et al Med Phys 37 2010

Effect of dose - contribution to variance (SE) of BP

Data taken from BrainPET 2011 Dose and Precision of Outcome Parameters in Quantitative PET Imaging of the Human Brain Y.H. Nai, M.L. Cunneen, R.S. Dimber, W.A. Hallett, G.E. Searle

FBP PSF

FBP vs. PSF real data

FBP vs PSF phantom data

Images analysed according to NEMA NU (2007) Hot and cold sphere contrast values calculated

FBP Image PSF Image ROI Image

PSF reconstruction hot sphere images

FBP vs PSF hot sphere contrast – NEMA image quality phantom

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FBP

Note: 2mm gauss filter was used for these and subsequent phantom comparisons

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FBP vs PSF cold sphere contrast – NEMA image quality phantom

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Background Variability

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FBP vs PSF Background variability – NEMA image quality phantom

FBP vs. TrueX Contrast and acquisition times – NEMA image quality phantom

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Hot Sphere Contrast Versus Acquisition Time (PSF 6i,16s and FBP)

PSF 10minPSF 1minPSF 15sPSF 5sPSF 2sFBP 10 minFBP 1minFBP 15sFBP 5sFBP 2s

FBP vs. PSF Time Activity Curves

Reference Region - Cerebellum Putamen

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FBP

FBP and PSF for

different ‘doses’

FBP vs. PSF Binding potential

Caudate - FBP Caudate - PSF