gamma camera image quality
DESCRIPTION
Notes from my lecture for technologists at Lehigh Valley Medical Center. Includes noise/sensitivity, resolution, and ROC curvesTRANSCRIPT
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?