Gamma Camera Image Quality

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Notes from my lecture for technologists at Lehigh Valley Medical Center. Includes noise/sensitivity, resolution, and ROC curves

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Gamma Camera Image Quality

  1. 1. Image quality of a gamma camera David S. Graff PhD
  2. 2. 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
  3. 3. Observer Noise Blur Collimators Performance
  4. 4. Observer Noise Blur Collimators Performance
  5. 5. Where does image noise come from? • A Patient is injected with 100 Bq of 99Tc. • How many decays will there be in 1 second?
  6. 6. 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?
  7. 7. Average of 100 detected photons 150 100 50 0
  8. 8. Basic statistics: • The uncertainty on a count is the square root of the count • Flip a many200 times coin • How 100 heads? • Expect • Sqrt(100)=10 10 • Uncertainty is or 90 – 110 • Expect 100±10
  9. 9. Average of 10 detected photons 15 10 5 0
  10. 10. Average of 100 detected photons 150 100 50 0
  11. 11. 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
  12. 12. 0.2 0.15 Frac 0.1 tion al un cert ainty 0.05 0 100 200 300 400 Number of detected photons
  13. 13. How to measure noise? • Standard Deviation (STDEV) of pixels • Depends on smoothing • Depends on Pixel size
  14. 14. Noise Contrast / Noise ratio (CNR)
  15. 15. Contrast-Noise Ratio
  16. 16. CNR = 1.6 Contrast Noise Ratio CNR = 1.6 Not the same as Detectability CNR = 1.6
  17. 17. CNR = 1.6 Contrast Noise Ratio CNR = 6.5 Not the same as Image Quality CNR = 16
  18. 18. 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
  19. 19. How to reduce noise: more counts! • Increase injected activity • Increase exposure time • Increase detector sensitivity • Increase collimator throughput
  20. 20. Observer Noise Blur Collimators Performance
  21. 21. Blur
  22. 22. Blur
  23. 23. Point Spread Function (PSF)
  24. 24. Full Width Half Max FWHM
  25. 25. Full Width Half Max
  26. 26. Full Width Half Max
  27. 27. Full Width Tenth Max FWTM
  28. 28. Mod ulati on T rans fer F unctio n
  29. 29. Intrinsic Detector Resolution Gamma ray lands exactly between two PMTs 200 optical photons are emitted and detected How many are detected by the upper PMT?
  30. 30. Observer Noise Blur Collimators Performance
  31. 31. Collimator Resolution: Best close to collimator Position collimator as close to patient as possible
  32. 32. Collimator Efficiency: Constant and low
  33. 33. 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))
  34. 34. 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))
  35. 35. Large Integral uniformity Small Differential Uniformity
  36. 36. Large Integral Uniformity Large Differential Uniformity
  37. 37. Observer Noise Blur Collimators Performance
  38. 38. The goal of a medical image is to do the best for the patient. Patient needs / Image tasks Tumor detection tumor size etc. Why are we doing all this? defect localization Accurate diagnosis Beneficial action Healthy, happy patient
  39. 39. There are two types of task: Classification: group into discreet categories Healthy or diseased Stage 1, 2, 3 Estimation: give continuous number Tumor uptake Tumor location (x,y,z)
  40. 40. We can put the result of a binary classification into four categories: Reality Reality positive negative Test True False positive Positive Positive Test False True negative Negative Negative
  41. 41. Sensitivity is the fraction of positive patients that are correctly diagnosed Reality Reality positive negative Test True False positive Positive Positive Test False True negative Negative Negative What about a contaminated test that classifies all patients as positive?
  42. 42. Selectivity is the fraction of healthy patients that are correctly diagnosed Reality Reality positive negative Test True False positive Positive Positive Test False True negative Negative Negative What about a defective test that classifies all patients as negative?
  43. 43. Both selectivity and sensitivity are needed to judge a test
  44. 44. Results can vary depending on aggressiveness of tester Always Positive ng ) Positive when ti C ra O slight suspicion pe (R -O stic er ri iev cte Positive c a ewhen R ar very confident C h Never Positive
  45. 45. Area Under the Curve (AUC) is a common measure of test effectiveness
  46. 46. Questions?

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