1. Seeram Chapter 11: Image QualityCT
Seeram Chapter 11: Image Quality

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

3. Factors Influencing CT 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 affecting Spatial 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 affecting Spatial Resolution
Finite focal spot size
focal spot - detector distance
focal spot - isocenter distance

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

11. Non-geometric Factors affecting 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
Radiography 10%
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

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

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

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

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

25. 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

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

27. 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

28. 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

29. Noise Levels in CT: Increasing slice width = less noise BUT
Increasing slice width degrades spatial resolution
less uniformity inside a larger pixel
partial volume effect

30. CT Image Quality in Equation 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

31. 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

32. 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

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

34. Point Spread Function PSF
“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

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

36. Quantifying Blurring Full 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
Half Maximum
FWHM

37. Line Spread Function LSF
Line object image blurred
Image width larger than object width
Intensity ?

38. Contrast Response Function CTF 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.

39. Contrast Response Function CTF or CRF
Blurring causes loss of contrast
darks get lighter
lights get darker
Lower
Frequency
Higher
Frequency
Higher
Contrast
Lower
Contrast

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

41. 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

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

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

44. 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

45. Modulation Transfer Function MTF
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
frequency

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

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

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

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

50. 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

51. Linearity
Linear relationship of CT #’s to object linear attenuation coefficients
Checked with phantom of several known materials
average CT # of each material obtained from ROI analysis
Compare CT #’s with known coefficients 77
-100
325 50
-44

52. CatPhan

54. Cross-Field Uniformity
Use uniform phantom (water)
CT pixel values should be uniform anyplace in image
Take 5 ROI
1 center ROI
4 corners ROI’s
Compare standard deviation between ROI’s

55. CT Artifacts Distortion
Areas where image not faithful to subject
Sources
patient
image process
equipment

56. CT Artifacts Distortion
Phantoms with evenly distributed objects

57. Preview! CT Artifacts: Causes
motion
metal & high-contrast sharp edges
beam hardening
partial volume averaging
sampling
detectors

58. Motion Artifacts Causes streaks in image
Algorithms have trouble coping because of inconsistent data

59. Artifacts: Abrupt High Contrast Changes
Examples:
prostheses
dental fillings
surgical clips
Electrodes
bone
Metal absorbs all radiation in ray
causes star-shaped artifact
Can be reduced by software

60. CT Artifacts: Beam Hardening
Increase in mean energy of polychromatic beam as it passes through patient
Can cause broad dark bands or streaks
cupping artifact
Reduced by beam hardening correction algorithms

61. CT Artifacts: Partial Volume Effect
CT #’s based on linear attenuation coefficient for tissue voxels
If voxel non-uniform (contains several materials), detection process will average

62. Partial Volume Effect Can appear as Minimizing incorrect densities
streaks
bands
Minimizing
Use thinner slices

63. Image Artifacts: Ring Artifact in 3rd Generation
Causes
1 or more bad detectors
small offset or gain difference of 1 detector compared to neighbors
detector calibration required
Reason: rays measured by a given detector are all tangent to same circle

64. Quality Control in CT spatial resolution contrast resolution noise
Performance tested at prescribed intervals
Image Quality Tests
spatial resolution
contrast resolution
noise
slice width
kVp waveform
average & standard deviation of water phantom CT #
scatter & leakage