Image quality of a gamma camera

David S. Graff PhD

Why should you care about image quality?

• Image quality of nuclear medicine cameras can degrade

• You will be assessing image quality daily/weekly

• Poor image quality can hurt patients

Noise Blur Collimators Observer Performance

Noise Blur Collimators Observer Performance

Where does image noise come from?

• A Patient is injected with 100 Bq of 99Tc.

• How many decays will there be in 1 second?

Why does this cause noise?

• Two adjacent segments of a patient’s Myocardium each take up 100 kBq of 99Tc.

• only 0.1% of all emitted photons are detected by the gamma camera.

• Will the same number of counts be gathered from the two segments?

• How many counts will the gamma camera record from each segment?

0

50

100

150

Average of 100 detected photons

Basic statistics:

• The uncertainty on a count is the square root of the count

• Flip a coin 200 times• How many heads?• Expect 100• Sqrt(100)=10• Uncertainty is 10• Expect 100±10 or 90 – 110

0

5

10

15

Average of 10 detected photons

0

50

100

150

Average of 100 detected photons

Absolute and relative noise

• The absolute uncertainty in a count is sqrt(N)

• More counts: more absolute uncertainty

• The relative uncertainty is noise÷signal

• Relative uncertainty is 1/sqrt(N)

• More counts: less relative uncertainy

Number of detected photons

Fractional uncertainty

0 100 200 300 400

0.05

0.1

0.15

0.2

How to measure noise?

• Standard Deviation (STDEV) of pixels

• Depends on smoothing

• Depends on Pixel size

NoiseContrast / Noise ratio(CNR)

Contrast-Noise Ratio

Contrast Noise RatioNot the same as Detectability

CNR = 1.6

CNR = 1.6

CNR = 1.6

CNR = 1.6

CNR = 16

CNR = 6.5

Contrast Noise RatioNot the same as Image Quality

Using Contrast-Noise ratio

• CNR alone does not describe image quality

• All other things kept constant, CNR does describe image quality

• CNR is easy to measure

• Can be used for daily QC

How to reduce noise:more counts!

• Increase injected activity

• Increase exposure time

• Increase detector sensitivity

• Increase collimator throughput

Noise Blur Collimators Observer Performance

Blur

Blur

Point Spread Function(PSF)

Full Width Half MaxFWHM

Full Width Half Max

Full Width Half Max

Full Width Tenth MaxFWTM

Modulation Transfer Function

200 optical photons are emitted and

detected

How many are detected by the upper PMT?

Gamma ray lands exactly between

two PMTs

Intrinsic Detector Resolution

Noise Blur Collimators Observer Performance

Collimator Resolution:Best close to collimator

Position collimator as close to patient as possible

Collimator Efficiency:Constant and low

INTEGRAL UNIFORMITY: For pixels within each area (CFOV and UFOV), the maximum and the minimum values are to be found from the smoothed data. Integral Unif. =100% ((Max - Min) / (Max + Min))

DIFFERENTIAL UNIFORMITY: For pixels within each area (CFOV and UFOV) the largest difference between any two pixels within a set of 5 contiguous pixels in a row or column. Differential Uniformity = + 100% ((Max - Min) / (Max + Min))

Large Integral uniformitySmall Differential Uniformity

Large Integral UniformityLarge Differential Uniformity

Noise Blur Collimators Observer Performance

The goal of a medical image is to do the best for the patient.

Patient needs / Image tasks

Accurate diagnosis

defect localization

tumor size

Beneficial action

Tumor detection

Healthy, happy patient

etc.Why are we doing all this?

There are two types of task:Classification: group into discreet categoriesHealthy or diseasedStage 1, 2, 3

Estimation: give continuous numberTumor uptakeTumor location (x,y,z)

We can put the result of a binary classification into four categories:

Reality positive

Reality negative

Test positive

True Positive

False Positive

Test negative

False Negative

True Negative

Sensitivity is the fraction of positive patients that are correctly diagnosed

Reality positive

Reality negative

Test positive

True Positive

False Positive

Test negative

False Negative

True Negative

What about a contaminated test that classifies all patients as positive?

Selectivity is the fraction of healthy patients that are correctly diagnosed

Reality positive

Reality negative

Test positive

True Positive

False Positive

Test negative

False Negative

True Negative

What about a defective test that classifies all patients as negative?

Both selectivity and sensitivity are needed to judge a test

Results can vary depending on aggressiveness of tester

Always Positive

Never Positive

Positive when very confident

Positive when slight suspicion

Reciev

er-Ope

rating

Charac

terist

ic (R

OC)

Area Under the Curve (AUC) is a common measure of test effectiveness

Questions?