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Seeram Chapter 11: Image Quality

CT. Seeram Chapter 11: Image Quality. CT Image Quality Parameters. Spatial Resolution. Contrast Resolution. Image Noise. Artifacts. Factors Influencing CT Image Quality. Beam Characteristics. Subject Transmissivity. Dose. Slice Thickness. Scatter. Display Resolution.

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Seeram Chapter 11: Image Quality

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  1. CT Seeram Chapter 11: Image Quality

  2. CT Image Quality Parameters Spatial Resolution Contrast Resolution Image Noise Artifacts

  3. Factors InfluencingCT Image Quality Beam Characteristics Subject Transmissivity Dose Slice Thickness Scatter Display Resolution Reconstruction Algorithm

  4. Spatial Resolution • Quantifies image blurring • “Ability to discriminate objects of varying density a small distance apart against a uniform background” • Minimum separation required between two high contrast objects for them to be resolved as two objects

  5. Spatial Resolution

  6. Resolvable Object Size &Limiting Resolution • Smallest resolvable high contrast object • Often expressed as line pairs / cm • “Pair” is one object + one space One Pair

  7. Resolvable Object Size:Limiting Resolution • Smallest resolvable high contrast object is half the reciprocal of spatial frequency • Example: • Limited resolution = 15 line pairs per cm • Pair is 1/15th cm • Object is half of pair • 1/15th / 2 • 1/30th cm • .033 cm • 0.33 mm 1/15th cm 1/30th cm

  8. Geometric Factors affectingSpatial Resolution • Focal spot size • detector aperture width • slice thickness or collimation • Less variation likely for thinner slices • attenuation variations within a voxel are averaged • partial volume effect

  9. Geometric Factors affectingSpatial Resolution Finite focalspot size focal spot - detector distance focal spot - isocenter distance

  10. Geometric Unsharpness & CT Focal Spot • Decreased spatial resolution if object blurred over several detectors • Detector aperture size • must be < object for object to be resolved Small Object to be Imaged Detectors

  11. Non-geometric Factorsaffecting Spatial Resolution • # of projections • Display matrix size • 512 X 512 pixels standard • Reconstruction algorithms • smoothing or enhancing of edges

  12. Reconstruction Algorithm &Spatial Resolution • Back projecting blurs image • Algorithms may be anatomically specific • Special algorithms • edge enhancement • noise reduction • smoothing • soft tissue or bone emphasis

  13. Hi-Resolution CT Technique • Very small slice thicknesses • 1-2 mm • High spatial frequency algorithms • increases resolution • increases noise • Noise can be offset by using higher doses • Optimized window / level settings • Small field of view (FOV) • Known as “targeting”

  14. Contrast Resolution • Ability of an imaging system to demonstrate small changes in tissue contrast • The difference in contrast necessary to resolve 2 large areas in image as separate structures

  15. CT Contrast Resolution • Significantly better than radiography • CT can demonstrate very small differences in density and atomic # This’ll be on your test. I guarantee it. Contrast Resolution Radiography10% CT<1%

  16. CT Contrast Resolution Depends Upon • reconstruction algorithm • low spatial frequency algorithm smooths image • Loss of spatial resolution • Reduces noise • enhances perceptibility of low contrast lesions • image display

  17. CT Contrast Resolution Depends on Noise

  18. CT Contrast Resolution Contrast depends on noise Noise depends on # photons detected # photons detected depends on …

  19. # of Photons Detected Depends Upon • photon flux (x-ray technique) • slice thickness • patient size • Detector efficiency • Note: • Good contrast resolution requires that detector sensitivity be capable of discriminating small differences in intensity

  20. Small Contrast Difference Harder to Identify in Presence of Noise

  21. CT Image Noise • Fluctuation of CT #’s in an image of uniform material (water) • Usually described as standard deviation of pixel values

  22. CT Image Noise S(xi - xmean)2 Noise (s) = ------------------- (n-1) • Standard deviation of pixel values Xi = individual pixel value Xmean = average of all pixel values in ROI n =total # pixels in ROI

  23. Noise Level • Units • CT numbers (HU’s)or • % contrast

  24. Noise Measurement in CT • Scan water phantom • Select regions of interest (ROI) • Take mean & standard deviation in each region • Standard deviation measures noise in ROI

  25. CT Noise Levels Depend Upon • matrix size (pixel size) • slice thickness • algorithm • electronic noise • scattered radiation • object size • Photon flux to detectors… • # detected photons • quantum noise

  26. Photon Flux to Detectors • Tube output flux (intensity) depends upon • kVp • mAs • beam filtration • Flux is combination of beam quality & quantity • Flux to detectors modified by patient • Larger patient = less photons to detector

  27. Slice Thickness • Thinner slices mean • less scatter • better contrast • less active detector area • less photons detected • More noise • To achieve equivalent noise with thinner slices, dose (technique factors) must be increased

  28. Noise Levels in CT: • Increasing slice width = less noiseBUT • Increasing slice width degrades spatial resolution • less uniformity inside a larger pixel • partial volume effect

  29. CT Image Quality inEquation Form s2(m) = kT/(td3R) Where s is variance resulting from noise k is a conversion factor (constant) T is transmissivity (inverse of attenuation) t is slice thickness d is pixel size R is dose

  30. Noise Levels in CT: • When dose increases, noise decreases • dose increases # detected photons • Doubling spatial resolution (2X lp/mm) requires an 8X increase in dose for equivalent noise • Smaller voxels mean less radiation per voxel

  31. CT Image Quality Trade-off s2(m) = kT/(td3R) To hold noise constant • If slice thickness goes down by 2 • Dose must go up by 2

  32. Measurements of Image Quality • PSF = Point Spread Function • LSF = Line Spread Function • CTF = Contrast Transfer Function • MTF = Modulation Traffic Function

  33. Point Spread FunctionPSF • “Point” object imaged as circle due to blurring • Causes • finite focal spot size • finite detector size • finite matrix size • Finite separation between object and detector • Ideally zero • Finite distance to focal spot • Ideally infinite

  34. Quantifying Blurring Intensity ? • Object point becomes image circle • Difficult to quantify total image circle size • difficult to identify beginning & end of object

  35. Quantifying BlurringFull Width at Half Maximum (FWHM) • width of point spread function at half its maximum value • Maximum value easy to identify • Half maximum value easy to identify • Easy to quantify width at half maximum Maximum HalfMaximum FWHM

  36. Line Spread FunctionLSF • Line object image blurred • Image width larger than object width Intensity ?

  37. Contrast Response FunctionCTF or CRF • Measures contrast response of imaging system as function of spatial frequency Lower Frequency Higher Frequency Loss of contrast between light and dark areas as bars & spaces get narrower. Bars & spaces blur into one another.

  38. Contrast Response FunctionCTF or CRF • Blurring causes loss of contrast • darks get lighter • lights get darker Lower Frequency Higher Frequency Higher Contrast Lower Contrast

  39. CT Phantoms • Measure • noise • spatial resolution • contrast resolution • slice thickness • dose • Available from • CT manufacturer • private phantom manufacturers • American Association of Physicists in Medicine • AAPM

  40. CT Spatial vs. Contrast Resolution • Spatial & contrast resolution interact • High contrast objects are easier to resolve • Omprove one at the expense of the other • Can only improve both by increasing dose Increasing object size Increasing contrast

  41. Contrast & Detail • Larger objects easy to see even at low contrast Increasing object size Increasing contrast

  42. Contrast & Detail • Small objects only visible at high contrast Increasing object size Increasing contrast

  43. Contrast – Detail Relationship • Contrast vs. object diameter • less contrast means object must be larger to resolve Visibility Increasing object size Difference in CT # Object Diameter Increasing contrast

  44. Modulation Transfer FunctionMTF • Fraction of contrast reproduced as a function of frequency Freq. = line pairs / cm 1 MTF 50% Recorded Contrast(reduced by blur) Contrast provided to film 0 frequency

  45. MTF • Can be derived from • point spread function • line spread function • MTF = 1 means • all contrast reproduced at this frequency • MTF = 0 means • no contrast reproduced at this frequency

  46. MTF • If MTF = 1 • all contrast reproduced at this frequency Contrast provided to film Recorded Contrast

  47. MTF • If MTF = 0.5 • half of contrast reproduced at this frequency Contrast provided to film Recorded Contrast

  48. MTF • If MTF = 0 • no contrast reproduced at this frequency Contrast provided to film Recorded Contrast

  49. CT Number • Calculated from reconstructed pixel attenuation coefficient (mt - mW) CT # = 1000 X ------------ mW Where: ut = linear attenuation coefficient for tissue in pixel uW = linear attenuation coefficient for water

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