Download Image Quality

Image Quality
Brief review
Hsieh, J. (2009, NovembAer). Computed tomography: principles, design, artifacts, and recent advances. Bellingham, WA:
Copyright © 2016, Elsevier Inc. All Rights Reserved.
SPIE.
2
Brief review
Hsieh, J. (2009, NovembAer). Computed tomography: principles, design, artifacts, and recent advances. Bellingham, WA:
Copyright © 2016, Elsevier Inc. All Rights Reserved.
SPIE.
3
Brief review
Hsieh, J. (2009, November). Computed tomography: principles,
design, artifacts, and recent advances. Bellingham, WA: SPIE.
Hsieh, J. (2009, NovembAer). Computed tomography: principles, design, artifacts, and recent advances. Bellingham, WA:
SPIE.
4
Introduction
The one comprehensive CT image quality characteristic is visibility of anatomical
structures, various tissues, and signs of pathology.

Visibility depends on a complex combination of the five (5) image
characteristics shown here:
1. Spatial Resolution
 Visibility of Detail, as affected by blurring. – how small of an object can be
detected?
2. Contrast Resolution or Contrast Sensitivity
 Ability to differentiate attenuation coefficients of adjacent area of tissue.
3. Image Noise
 Variation of HU or CT number
4. Artifacts
5. Spatial or geometric characteristics of the image/body relationship
6. Temporal Resolution
 Ability to capture ob
Spatial Resolution
•
Spatial resolution is a measure of the size of the of the
smallest object that can be visualized or resolved in an image.
•
It is affected by:
Geometric factors – such as focal spot size
• Detector width
• Distance of patient from the focal spot
• Distance of patient from the detectors
•
•
Factors that affect it that we can control are:
•
•
•
•
Slice thickness
Display FOV
Image Matrix
Convolution Filter
Spatial Resolution (cont’d)
•
There are two components of spatial resolution:

In-plane resolution: resolution in the x,y direction

Longitudinal resolution: resolution in the z direction
Copyright © 2016, Elsevier Inc. All Rights Reserved.
7
In-plane resolution
•
•
•
•
Resolution measured in the x y plane.
Often expressed in terms of line pairs
Refers to a test performed with phantom (bar phantom) that has several plastic holes
within a water phantom.
One hole and one space is referred to as a line pair.
Bushong, S. Radiologic Science for Technologists
In-Plane Spatial Resolution
•
Specified in terms of line pairs per centimeter (lp/cm) or line pairs per millimeter
(lp/mm)
 Bar pattern
 Modulation transfer function (MTF)
9
Bar Pattern
•
A set of uniformly spaced comb-shaped bars with 0.5-mm wide teeth
•
A bar pattern (object) consists of line pairs (one line pair equals one bar plus one
space). The number of line pairs per unit length is called the spatial frequency.
Large objects have a low spatial frequency, whereas small objects have a high
spatial frequency. Lp = line pair.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
10
Bar Pattern
Q: A CT imaging system has a spatial resolution of
5 lp/mm. How small an object can it resolve?
A: 5 lp/mm = (5 * 2 lines) = 10 objects or lines in 1 mm. Same
as saying 10 lines / mm.
The reciprocal is the answer. 1 / 10 = 0.1 mm.
11
In-Plane Spatial Resolution, ctnd.
When x-rays are detected, a signal is
generated. This signal has a specific
frequency that relates to the size and
density of the object being imaged.
•
Larger objects of uniform density
are represented by low spatial
frequency signal
•
Smaller dense objects and sharp
borders by high spatial frequency
signal.
•
Copyright © 2016, Elsevier Inc. All Rights Reserved.
12
Modulated Transfer Function (MTF)
•
Describes with numbers the accuracy with
which the object being imaged is
represented.
• At an MTF 1.0, the image is a perfect
reproduction of the object.
• At 0, the object will not appear in the
image.
•
The relationship of this signal to the object
detail (lp/cm) is the MTF.
•
The MTF can be used to compare scanner
performance.
1.
2.
Modern systems can reach in-plane
resolution of 25 lp/cm, possibly more.
Scanner A has 11.2 lp/cm at 0.1 MTF
Scanner B has 15 lp/cm at 0.1 MTF
Means scanner B has better spatial
resolution.
13
MTF in Pictures
Copyright © 2016, Elsevier Inc. All Rights Reserved.
14
In-Plane Resolution Measurements

Can be measured by evaluating the point
spread function (PSF).

Is used to quantify amount of blurring that
occurs within an image of an object.

To measure the PSF, a phantom with small
wire is scanned.

Alternatively can measure resolution with a
pattern of bars or the smallest range of
closely spaced holes that can be discerned.
Factors Affecting In-Plane Spatial
Resolution
•
Scanner Geometry
•
•
Focal spot size
Detector Size/spacing
• Detector aperture (width of active element))
•
•
Reconstruction algorithm
Pixel size (Reconstruction FOV)
•
•
•
•
•
Matrix size
Display field of view
Sampling Frequency
Pitch/Slice Sensitivity Profile
Patient motion
Focal spot and detector size
•
Small FS size improves resolution.
•
Large FS size increases geometric
unsharpness – penumbra – resolution
is slightly degraded due to blur.
•
Selection of FS size tied to mA. Each
CT system has an mA cutoff point,
where the FS switches from small to
large.
Detector Size (aka detector aperture)
• Smaller, more closely spaced detectors
improve spatial resolution, by improving
sampling of a signal.
• More detectors increase the signal
recording capability of a detector array,
improving its sensitivity to the wide range
of encountered spatial frequencies and
increase the in-plane resolution.
• Suppose we have bar patterns that are
1mm (black and white area are 1mm
each) in size. The detector size and
spacing are 2 mm.
A. Detector size is larger than the size of the
bars within the pattern. Can’t resolve
difference between patterns.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
18
Detector Size (aka detector aperture)
B. Make detectors smaller so that they are
the same size as the bars, leave
spacing as is. Now, we can’t resolve the
difference between each bar since the
spacing between the detectors is large.

Bars still are not resolved because the
samples are too far apart (in this case,
the spaces between bars are missed
C. Eliminate spacing. Now we can detect both
the black and white areas of the bars.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
19
Aliasing
•Sampling occurs at the CT detectors where the incoming
x-ray beam is recorded (or sampled) to ensure that enough
signal is present to produce an image.
•At this point, the signal is in analog form.
• The analog signal is sampled at various times to
measure its strength at different points.
•The more points sampled the better, the better
represented the analog signal.
• After sampling, the detector will assign a grayscale
quantity based on the amount of radiation absorbed by the
detector; this grayscale quantity is the CT number or
Hounsfield unit.
•Aliasing refers to a sampling problem that arises when
structures and spaces cannot be distinguished.
•These artifacts show up as streaks and are caused by an
insufficient number of samples available for image
reconstruction.
•Aliasing often is referred to as a sampling error.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
20
Aliasing
Copyright © 2016, Elsevier Inc. All Rights Reserved.
21
Reconstruction Algorithm
•
In-plane resolution is also strongly influences by recon algorithms.
•
If we only back-project, blurring results.
•
To remove the blurring, a ramp filter (convolution process) is applied
before back projection.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
22
Reconstruction algorithm
(aka convolution kernel)
-
High spatial frequency algorithms, such as those used for edge or bone,
emphasize high frequency signals during reconstruction.
-
Lower spatial frequency algorithms, such as soft tissue, produce low spatial
frequency information.
-
Selection of algorithm is based on clinical application.
Reconstruction Algorithm Selection
Copyright © 2016, Elsevier Inc. All Rights Reserved.
24
Pixel size

Combination of large matrix and small DFOV (large zoom factor) results in a
smaller pixel size and increase in the in-plane resolution.

Relationship of DFOV and matrix directly controls the size of the smallest
detail a CT system is able to resolve. Smaller pixels will represent tissue
more accurately.
Sampling Frequency
•
The number of view obtained by the CT system during
acquisition. Aka – views per rotation.
•
Higher sampling frequency obtained by increasing the number
of detectors or sampling rate.
Pitch revisited
- With tables motion and continuous x-ray production,
data are collected continuously, and an image then
can be reconstructed at any desired z-axislocation.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
27
Pitch Revisited –
Single Slice CT vs. MSCT
Copyright © 2016, Elsevier Inc. All Rights Reserved.
28
At any z-location? = Interpolation!
Reconstruction of an image at any z-axis location is
due to a mathematical process known as
Interpolation.
Three kinds of interpolation:
1. 360 degree
2. 180 degree
3. Z-filtering
Copyright © 2016, Elsevier Inc. All Rights Reserved.
29
Interpolation
•
•
360 degree linear interpolation
• Reconstructed data is
interpolated from data that
is acquired one revolution
apart.
• May cause more blurring in
the image and reformats
180 degree linear interpolation
• Reconstruct image from
data that is acquired half a
revolution apart.
• Less blurring and improved
reformats into sagittal and
coronal planes.
30
Interpolation, cont’d
•
Z-filtering allows for thin sections to be
reconstructed at any point along the acquired zaxis volume.
•
It uses multiple complementary rays beyond
those immediately above and below the
particular slice plane.
•
Using z-filtering, images with different slice
widths can be retrospectively reconstructed from
the same raw data.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
31
Cross-Plane Spatial Resolution

Helical/spiral and multislice/volumetric CT
scanning introduction has allowed radiologists to
do the following:

Multiplanar reformat (MPR)
 Maximum intensity projection (MIP)
 Volume rendering (VR)
 Reduce partial volume averaging
Copyright © 2016, Elsevier Inc. All Rights Reserved.
32
Longitudinal Spatial Resolution

Aka cross-plane spatial resolution
 Described by the slice sensitivity profile (SSP).
 The width of the plot (aka full width half maximum is the slice thickness)

Represent the broadening of the slice thickness (or thickness of tissue) in the z-direction.
• The SSP of conventional CT is generally rectangular
• The SSP represents the a known thickness of tissue centered at a specific location
along the patients longitudinal axis.

As pitch is increased, the SSP indicates that there is broadening of the slice thickness along the z-axis.

Effective section width refers to the widened SSP that occurs with increased pitch.
Wilting, J.E. “Technical aspects of spiral CT”
Slice Sensitivity Profile (SSP)

Used to describe cross-plane spatial resolution

Methods of measuring SSP
• Full width at half-maximum (FWHM)
• Full width at tenth-maximum (FWTM)
• Shallow-angled slice ramp
Copyright © 2016, Elsevier Inc. All Rights Reserved.
34
FWHM/FWTM
Figure 9-13 Illustration of FWHM and FWTM. FWHM represents the distance between
two points on the SSP curve whose intensity is 50% of the peak. FWTM represents the
distance between two points on the SSP whose intensity is 10% of the peak.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
35
SSP

Effective section width is the Full Width at Half Maximum (FWHM) of the
SSP. It is measured by examining the SSP at half of its maximum height.
Shallow-Angled Slice Ramp
Figure 9-15 Illustration of the use of slice ramp to measure SSP. A, Acquired with 5-mm slice
thickness in step-and-shoot mode. B, Acquired with 10-mm slice thickness in step-and-shoot mode.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
37
Factors that affect Longitudinal Spatial
Resolution



Spiral interpolation algorithm – applied to helical
acquisitions to reduce the SSP broadening
effects.
180 LI interpolation most commonly used. 360 is
also used, but is wider than the 180.
Pitch – For MSCT the detector collimation along
the z-axis sometimes compensates for the
negative effects of pitch.
Low-Contrast Resolution

Ability to observe low-contrast objects whose
density is slightly different from the background


CT can detect density differences from 0.25% to 0.5%
Conventional radiography can detect approximately
10% density difference
Copyright © 2016, Elsevier Inc. All Rights Reserved.
39
Low-Contrast Resolution (Cont.)
•
Measured with phantoms
that contain low-contrast
objects of different sizes
•
The smallest object that
can be visualized at a
given contrast level and
dose determines a CT
scanner’s low-contrast
detectability (LCD) or lowcontrast performance
Figure 9-17 Image of a low-contrast portion of the
Catphan phantom.
40
Factors Affecting Contrast Resolution

Inherent subject contrast

Beam Collimation


Reconstruction algorithm


Scatter radiation reduces contrast resolution.
Low spatial frequency algorithms, like soft tissue or standard, improve contrast resolution.
Window Width and Window Level
Factors Affecting Contrast Resolution

Detector Collimation
 Increased detector collimation results in reconstruction of thinner
sections. As section width decreases, the photon flux for each pixel also
decreases.

**Noise**


Any increase in noise results in decreased contrast resolution.
CT NOISE INCREASES WITH:
 SMALL PIXEL SIZES
 THIN CT SLICES
 LARGE PATIENT SIZES
 LOW CT DOSES
Subject Contrast
•Thickness and atomic density of object as compared to surrounding tissue i.e.
intensity difference of object to background (contrast).
•Inherent contrast can be augmented with administration of IV contrast.
•Object Size
Copyright © 2016, Elsevier Inc. All Rights Reserved.
43
Subject Contrast
•Thickness and atomic density of object as compared to surrounding tissue i.e.
intensity difference of object to background (contrast).
•Inherent contrast can be augmented with administration of IV contrast.
•Object Size
Copyright © 2016, Elsevier Inc. All Rights Reserved.
44
Windowing
• After raw data is reconstructed, a number, the Hounsfield unit is assigned to
represent the attentuation properties of the tissues in each pixel.
• CT numbers may range from -1000 to upwards of 3071 HU. This is over 2000
shades of gray.
•Monitors can typically display 256 shades of gray.
•To overcome this limitation we window/level.
45
Windowing
•The very high contrast sensitivity of CT is derived from the ability to select a small
range of CT numbers and display them over the full brightness range (dark black to
bright white) in the image.
•The range of CT numbers to be displayed in the image is designated as the
WINDOW.
•The two adjustable protocol factors that control the window are the LEVEL and the
WIDTH.
•The LEVEL control sets the midpoint of the window range along the CT number
scale. It can be used to optimize the image contrast for viewing different anatomical
regions. A relative low window level might be used for seeing the contrast within the
lungs and a high window level to see contrast within bones.
•The WIDTH setting is very much of an image contrast control. It adjusts how
contrasty the image appears. Reducing the window width increases the contrast
among tissues as they are displayed in an image.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
46
Windowing
• The width determines the range of CT
numbers displayed
•Increasing the width means represents
a wider range of tissues on the image
•The result is an image with less
contrast between tissues of similar
densities.
• Mostly used when viewing structures
with high contrast, like the lungs.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
47
Windowing
• Narrow widths is useful
for exhibitng subtle
inherent contrast.
•It increases the contrast
between tissues of
similar intensity.
•It is most appropriate for
soft tissue regions like
the brain.
•
Copyright © 2016, Elsevier Inc. All Rights Reserved.
48
Noise
•
•
•
•
Any portion of signal that contains no useful information.
Is random (stochastic) statistical variation in signal.
Appears as overall graininess.
3 types
1. Quantum noise – results when there is an insufficient x-ray flux. Is inversely related to
the amount of radiation exposed to each voxel.
a) What determines amount of radiation??
2. Electronic noise – signal may be lost and noise introduced by the reconstruction
process.
3. Artifactual noise – artifacts may be viewed as a type of noise.
Signal to Noise ratio (SNR) – describes or quantified the amount of noise in a
displayed CT image.
• Goal is produce an image with high SNR while keeping dose and spatial
resolution appropriate.

Parameters that most directly affect Noise

X-ray photon flux – rate at which x-ray photons pass through a given
unit of tissue.
 Largely controlled by mA and time and kVp.

Voxel Size – Larger voxel has greater photon flux and less noise.
Size can be increased by increasing DFOV (decrease zoom factor),
decrease matrix size, or increase section width.

Pitch/Table Speed – Increase in pitch means that each voxel is
exposed to less photons.

Detector sensitivity
Patient factors
Algorithm/kernel.


Noise Affecting Low-Contrast
Detectability

Factors that can be controlled by the operator







X-ray tube current (mA)
X-ray tube voltage (kV)
Scan speed
Pitch
Slice thickness
Use of iterative reconstruction algorithms
Changing the above parameters can also affect these
other factors



Patient dose
Tube overheating
Spatial resolution
Copyright © 2016, Elsevier Inc. All Rights Reserved.
51
Tube current relationship to noise and
image quality
•mA is the tube current. mAs is
the product of the mA and tube
rotatinon time in seconds.
•It determines the total number of
photons that will pass through
the patient and strike the
detector.
•Higher mAs:
•More photons that pass
through patient
•Less noise
•More Dose
Copyright © 2016, Elsevier Inc. All Rights Reserved.
52
Copyright © 2016, Elsevier Inc. All Rights Reserved.
53
Tube Current – Dose and Noise
•
The general procedure in setting up a
scan is to set the MA to a value that
will keep the noise to an "acceptable"
level.
•
This might be done manually by the
operator or by some combination of
automatic control functions.
•
It should be recognized that the MA is
also controlling the dose.
•
An objective in setting up a protocol is
to select a MA value that is
appropriate for individual body sizes,
shapes, and compositions that will
provide a specific noise level without
unnecessary dose to the patient
54
kVp - Tube Potential
•Increase in kVp causes an increase in the energy of the x-ray photons
leaving the tube.
•This increases their ability to penetrate the body.
• Tissue differentiation is dependent on kVp
• Too high a kVp, the photons will easily pentrate the tissue. Too low and
they will be easily absorbed.
• Best tissue contrast is achieved by adjusting kVp so that photons will be
attenuated by denser structures and will pass through less dense
structures.
• kVp ultimately controls contrast in a CT image.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
55
kVp
•
•
Fig. 1 ( a – d ) Axial computed
tomography arthrogram images (0.9 mm
thickness; sharp kernel; window
level/width, 400/2000 HU) of the right
shoulder acquired on a fresh frozen
cadaver at a fi xed dose level (CTDI vol ,
15 mGy) using decreasing tube voltages,
from
(a) 140 to (d) 80 kV, respectively. Note
that as tube voltage decreases, the
progressive loss of conspicuity of the
humeral head articular cartilage results in
overestimation of the cartilage lesion
(arrows) and an increase in streak (i.e.,
beam hardening, asterisks) and “
blooming ” (due to iodine) artifacts.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
56
Slice Thickness
•
Slice thickness is the
number of millimeters of
anatomy penetrated by
the thickness or width of
the beam
•
It impacts the noise,
resolution along the zdirection, and patient
dose.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
57
Slice Thickness and Noise
Copyright © 2016, Elsevier Inc. All Rights Reserved.
58
Table Increment
•
•
Is the increment of the patient table between
slices on axial scans.
Table increment determines if there will be gaps,
overlaps or contiguous slices.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
59
Table Increment
•
•
•
Also affects visibility of detail
If greater than slice thickness and gaps occur, some anatomy will be
skipped.
If less than slice thickness, overlap will occur.
•
Consider a 4mm between two
contiguous slices. Might have poor
visualization due to partial
representation of the lesion in both
slices.
•
If overlap happens, the lesion may be
more completely contained in one
slice due to repetition of anatomy in
that slice.
•
Downside is that it might lead to more
dose for the patient.
60
Reconstruction Interval
•
•
•
•
Specific to Helical Scans, reconstruction interval
indicates the amount of overlap between
adjacent slices.
If equal to slice thickness, reconstructed slices
are contiguous.
If less than slice thickness, there will be overlap.
Same principle as with axial scanning, but no
dose increase in this case.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
61
Magnification
•
Magnification does not affect the spatial
resolution or the appearance of image noise. It
simply takes the pixels and enlarges them for
viewing.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
62
Other Contrast Resolution
Considerations



Pixel size - discussed
Slice thickness - discussed
Patient size
Temporal Resolution



Ability of CT system to freeze motions of the
scanned object
Has grown in notability with cardiac CT imaging
Factors that affect temporal resolution



Scan speed
Half-scan reconstruction algorithms
Multisector reconstruction
Copyright © 2016, Elsevier Inc. All Rights Reserved.
64
Techniques to Reduce Motion Artifact

Gating



Achieved with the
use of an
electrocardiogram
(ECG) signal to
capture periods in
which the heart is in a
relatively motionless
state
Two types
• Prospective
• Retrospective
Coverage


Ability for CT scanner to
cover the entire heart in a
single x-ray tube rotation
Relies on total data
acquisition
• Total data acquisition is
determined by the
following:



Gantry speed
Helical pitch
Detector coverage
Copyright © 2016, Elsevier Inc. All Rights Reserved.
65
CT Number Accuracy

Accuracy

Precisely relating the attenuation coefficient of an
object to its CT number
• Water = 0
• Air = −1000

Tested by using manufacturer water filled phantom to
ensure water portion measures close to 0
Copyright © 2016, Elsevier Inc. All Rights Reserved.
66
CT Number Linearity

Linearity


Refers to the
relationship of CT
numbers to the linear
attenuation
coefficients of the
object to be imaged
Checked with a daily
calibration test to
determine the
acceptance of the CT
system
Figure 9-26 Plot of average CT numbers as a function of
linear attenuation coefficients. This indicates acceptable
CT linearity if the relationship is a straight line. (From
Bushong, S. (2009). Radiologic science for
technologists (9th ed.). St. Louis, MO: Mosby.)
Copyright © 2016, Elsevier Inc. All Rights Reserved.
67
Uniformity

Method to determine that
CT number measurement
does not change with the
location of the selected
regions of interest (ROI)
within an uniform
phantom

Factors that affect
uniformity
• Beam hardening
• Scatter
• CT system stability
Figure 9-28 Water phantom for CT number uniformity
measurement.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
68
Noise

Measurements



Image noise is measured typically on uniform phantoms
Several ROIs are used, and average the value of standard
deviations is reported
Noise sources

Three major noise contributors:
• Quantum noise
• Inherent physical limitations of the system
• Reconstruction parameters

Noise power spectrum


Uses the Fourier transform to break down the image noise into
its frequency components
Proves that standard deviation alone is insufficient when
characterizing noise in a CT image
Copyright © 2016, Elsevier Inc. All Rights Reserved.
69
Artifact: Definition



An artifact is “a distortion or error in an image that is
unrelated to the subject being studied”
A CT image artifact is defined as “any discrepancy
between the reconstructed CT numbers in the image and
the true attenuation coefficients of the object”
Artifact sources





Patient
Inappropriate selection of protocols
Reconstruction process
Equipment problems
Physic limitations
Copyright © 2016, Elsevier Inc. All Rights Reserved.
70
Artifact: Types and Causes


Artifacts in CT are classified according to cause
and appearance
Four major image appearance categories

Streaks
 Shadings
 Rings and bands
 Miscellaneous
Copyright © 2016, Elsevier Inc. All Rights Reserved.
71
Common Artifacts








Patient motion artifacts
Metal artifacts
Beam-hardening artifacts
Partial volume artifacts
Aliasing artifacts
Noise-induced artifacts
Scatter
Cone-beam artifacts
Copyright © 2016, Elsevier Inc. All Rights Reserved.
72
Patient Motion

Appearance



Streak artifact
Caused by both voluntary
and involuntary
Corrective measures




Immobilize patients and
use positioning aids to
make them comfortable
Patient education and
understanding of the exam
Shorten scan time
Underscan weighting
software
Figure 9-33 Illustration of patient involuntary head
motion. A, Without compensation. B, With correction.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
73
Metallic Artifacts

Appearance



Star-shaped streak
artifacts
Result from metal highly
attenuating the x-ray
beam
Corrective measures


Use a higher keV
Projection inpainting
• Method of replacing the
corrupted projection
samples with estimated
projection samples
Figure 9- 35 Coronal images of a patient with bilateral hip
implant (A) original image (B) with MAR algorithm.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
74
Beam Hardening

Appearance



Dark shading artifacts
Result from an increase in
the mean energy of the xray beam as it passes
through the patient
Corrective action

Performing a polynomial
mapping of the measured
projections before the
reconstruction
Figure 9-40 Illustration of bone beam hardening with a
human skull phantom. A, Without correction. B, With
correction.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
75
Partial Volume

Appearance


Bands and streaks
Created by multiple
materials within a
voxel being averaged
• Known as partial
volume averaging

Corrective measures


Figure 9-42 Partial volume effect. A, 7-mm detector
aperture. B, 1-mm detector aperture.
Reduced using thinner
slice acquisitions
Computer algorithms
Copyright © 2016, Elsevier Inc. All Rights Reserved.
76
Aliasing

Appearance



Streaks
Caused by insufficient
projection sampling or from
insufficient view sampling
Corrective measures


Increase number of views
or ray samples per view
Modify convolution kernel
Figure 9-44 View aliasing artifacts resulting from 50% of the
normal number of views used to scan this torso phantom. The
artifacts are apparent at the periphery of the phantom.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
77
Noise Induced

Appearance



Severe streak artifacts
Occur from not having
enough photons striking
the CT detector
Corrective measures




Optimize patient
positioning
Decrease scan speed
Increase exposure
technique factors
Adaptive filtering
algorithms
Figure 9-46 A, Streak artifacts can arise from an
increase in noise resulting from reduced photons at the
detector. B, Correction of the streaks by adaptive
filtering.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
78
Scatter

Appearance



Dark shading artifacts
Created by the
interaction between
x-ray photons and
matter
Corrective measures
Figure 9-48 Impact of scattered radiation. A, Without
correction. B, With correction.

Postpatient collimator
 Scatter correcting
algorithms
Copyright © 2016, Elsevier Inc. All Rights Reserved.
79
Cone Beam

Appearance



Dark shading ovals or
ellipsoids
Caused by incomplete or
insufficient projection
samples as a result of the
cone-beam geometry of
multislice CT
Corrective measure

Research is ongoing on
how to reduce these
artifacts
Figure 9-49 Images of a helical body phantom
reconstructed with FDK algorithm. A, 12.8 mm from the
center plane. B, 17.8 mm from the center plane.
Copyright © 2016, Elsevier Inc. All Rights Reserved.
80
Quality Control


Ensures the optimal performance of the CT scanner
through a series of daily, monthly, and annual tests
Tests that constitute a general quality control program for
CT scanners








Spatial resolution
Contrast resolution
Noise
Slice width
Peak kV waveform
Average CT number of water
Standard deviation of CT numbers in water
Radiation scatter and leakage
Copyright © 2016, Elsevier Inc. All Rights Reserved.
81