Image Quality and Artifacts in CT

SAHANA KAYASTHA

MSC.MIT 1^ST YR

ROLL NO:26

MMC,IOM

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Overview

• Spatial Resolution
• Contrast resolution
• Noise
• Temporal resolution
• Linearity
• Uniformity
• Artifacts

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Introduction

• Image quality relates to how well the image represents the object scanned. However, the true test of the quality of a specific image is whether it serves the purpose for which it was acquired.
• At the most fundamental level, image quality is a comparison of the image to the actual object.
• In CT, image quality is directly related to its usefulness in providing an accurate diagnosis

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Image Quality

• Optimized imaging protocols demands that image quality should be sufficient to meet the clinical requirement for the examination.
• Optimizing image quality is the process of achieving a balance among the various characteristics (such as detail and noise) and adjusting the image quality to appropriate levels in order to manage the radiation dose to the patient.

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• For example, an image of an infant using a very low technique may appear quite noisy, but it may still be adequate if the image is taken to follow up a large abnormality, such as an abscess.
• Clearly, the usefulness of an image can only be assessed on a case-by-case basis.

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• Fig: top; reference image with optimum image quality, right;high noise, middle;low spatial resolution or blur, left; low contrast image

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Image Quality

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• A number of methods are available for measuring CT image quality, and principal characteristics are numerically assigned. These include
• spatial resolution
• contrast resolution
• Noise
• linearity, and
• Uniformity
• Depending on the diagnostic task, these factors interact to determine the ability to perceive low-contrast structures and the visibility of details.
• These tools help make possible comparisons of one imaging system to another, or the same system over time.

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Spatial resolution

• Spatial resolution can be expressed in two domains-
• Spatial domain refers three dimensions of set of CT or MRI image. Number of metrics that define the spatial resolution in spatial domain are-
• a) Point Spread Function
• b) Line Spread Function
• c) Edge Spread Function

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• Frequency Domain- Once a spatial domain is Fourier transformed the resulting data are considered to be in the frequency domain.

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• Fourier transform is an algorithm that decomposes a spatial domain signal into a series of sine waves with three (amplitude, frequency and phase) parameters that, when summed replicate that signal.

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Spatial Resolution

• It is another term used for detail resolution. It is the imaging system’s ability to resolve small independent objects in close proximity to one another.
• It is expressed in line pairs per millimeter(lp/mm) or centimeter (lp/cm), i.e. spatial frequency

Spatial Resolution: how it can be measured?

Spatial resolution of an image is measured in two ways:

• Direct method (by counting the line pair):

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• By analyzing spread of information within the system ( known as MTF)

Spatial resolution

• Direct measurement of spatial resolution
• Using a line pairs phantom( made of acrylic and has closely spaced metal strips)
• The phantom is scanned, and the number of strips that are visible are counted.
• Line pair(line +space)
• if 20 lines can be seen in a 1-cm section in an image of the phantom, the spatial resolution is reported as 20 line pairs per centimeter (lp/cm).
• How frequently an object will fit into a given space is given by its spatial frequency.

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Spatial Resolution

Spatial Frequency

• The no. of line pairs per unit length is called spatial frequency.
• Low spatial frequency represents large objects.
• High spatial frequency represents small objects.

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Fig: A simplified illustration of spatial frequency.

• fig:

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• The absolute object size that can be resolved is equal to one-half the reciprocal of the spatial frequency at the limiting resolution.

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• Absolute size of object that can be resolved.

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0.0125mm

Spatial Resolution

Positioning and alignment, CT number accuracy and slice thickness

Low contrast resolution

CT number uniformity assessment

High contrast (spatial) resolution

CT ACR 464 Phantom

Spatial Resolution

CT ACR 464 Phantom

Spatial Resolution

CT ACR 464 Phantom

Spatial Resolution�

Analyzing the spread of information using Modulation Transfer Function

• Most common method
• Is a graphical representation of a system’s capability of passing information to the observer.
• The MTF is the ratio of the accuracy of the image compared with the actual object scanned.
• The MTF scale is from 0 to 1.
• If the image reproduced the object exactly, the MTF of the system would have a value of 1.
• If the image were blank and contained no information about the object, the MTF would be 0.

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• In graphic form, MTF (y-axis) is charted against the spatial frequency (object size(x-axis)).
• shows that as the size of the object increases, the MTF also increases. i.e as the size of the object increases, it can be more accurately portrayed on the image

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Spatial Resolution

Modulation Transfer Function

Fig: graphing the MTF of two separate CT systems, An MTF curve extending to the right indicates a system with higher spatial resolution capabilities.

• The relationship between the sizeof the object and its portrayal on the image is not linear; hence an object twice the size of another object may not necessarily possess twice the image fidelity.
• Limiting resolution is the spatial frequency possible on a given CT system at an MTF equal to 0.1
• In this example (fig)scanner B will be better able to reproduce small objects than will scanner A.

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Fig: showing limiting resolution on a given CT system, at an MTF equal to 0.1. In this example, the limiting resolution of scanner A is 4.3 and scanner B is 5.0.

Modulation Transfer Function

An MTF curve that is higher at low spatial frequencies indicates better contrast resolution

Modulation Transfer Function

• Compared with conventional radiography, CT has significantly worse spatial resolution.
• The limiting spatial frequency for screen-film radiography is about 7 line pairs per millimeter (lp/mm), for digital radiography it is about 5 lp/mm; however, the limiting resolution for CT is approximately 1 lp/mm.
• It is contrast resolution that distinguishes CT from other clinical modalities.

Spatial Resolution

CT Spatial Resolution

• Spatial resolution is influenced by factors including
• System geometric resolution limits – focal spot size, detector width and ray sampling,
• Pixel size
• Properties of the convolution kernel/mathematical reconstruction filter
• Often measured in two orthogonal directions:
• Axial/In-plane (X-Y Plane) Resolution:-Determined by image matrix size, DFOV and pixel size.
• Longitudinal (Z–axis) resolution:-Determined by detector array thickness(MDCT)/collimation(SDCT).

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X

Y

Z

Factors affecting spatial resolution

• Pixel size, Matrix and FOV
• X-ray tube focal spot size
• Sampling: detector size (aperture) and sample spacing (detector pitch)
• Slice thickness
• Reconstruction filter
• Patient motion

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Pixel Size,FOV and matrix Size

• Smaller the pixel size better is the spatial resolution
• Transverse (in plane xy) resolution depends on pixel size
• Pixel size = DFOV/Matrix size.
• DFOV defined by the user based on anatomy to be displayed- DFOV<SFOV.

Fig: Two small objects in the patient showing how pixel size affect the resolution

Focal Spot Size

• The focal spot size affects image quality, but the effect is minimal.
• As in any x-ray imaging procedure, larger focal spots cause more geometric unsharpness in the image and reduce spatial resolution.
• Although focal-spot size does affect CT spatial resolution, CT resolution is generally limited by the size of the detector measurements (referred to as the aperture size) and by the spacing of detector measurements used to reconstruct the image
• Increase in effective focal spot = decrease SR because of increased penumbra causing more geometric unsharpness in the image.
• Z flying focal spot is responsible for increase in the cross plane resolution, by obtaining two overlapping slices for each detector row.

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Sampling (Detector width and Detector Spacing)

• Applied to CT image reconstruction, sampling theorem can be roughly summarized by the following statement: because an object may not lie entirely within a pixel, the pixel dimension should be half the size of the object to increase the likelihood of that object being resolved.
• Nyquist criteria states that resolving N lp/cm requires measurement of at least 2 × N samples per cm.

• Detector aperture size
• Width of active detector element in CT detector array
• Spatial resolution improves significantly in longitudinal direction as the detector aperture size decreases.
• The thinner the detector, higher the spatial resolution and that has been the driving force in MDCT
• In plane (x-y) spatial resolution is not affected by aperture size.

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Detector pitch(spacing)

• In addition to small aperture, closely spaced measurements are required for good resolution.
• For fixed FOV, as detector pitch decreases, number of rays increases and give better resolution.
• When the view data exhibits a sequence of higher and lower attenuations, the image also exhibit bars and spaces that are separate but fewer than those actually in the test object. Such an image is said to be aliased.
• Because of insufficient sampling, the higher spatial-frequency test pattern appears in the alias of a lower-frequency pattern and thus is not truly resolved.

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pitch

• Increasing the pitch reduces resolution in the image
• Lower pitch is slower(decreased temporal resolution)
• Higher pitch has more slice broadening ( decreased spatial resolution) but radiation savings
• Pitch <1 implies oversampling
• Pitch> 2 implies skipped data
• Can reconstruct an infinite number of images provided helical raw data is present

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Slice thickness

• In single slice CT, slice thickness equal to beam collimation
• In MDCT, equal to width of the detector in slice thickness direction.
• Slice thickness plays an important role in volume averaging, thereby affecting spatial resolution in the image.
• Thinner Slices:
• Higher Spatial Resolution
• Less Partial Volume effects
• More Noise

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Slice thickness

• The matrix divides data into squares with an x and y dimension. The operator’s selection of slice thickness accounts for the z axis.
• New CT scanners(MDCT) allow for very thin slice thickness; often the goal is to produce isotropic voxels.
• An isotropic voxel is a cube, measuring the same in the x,y and z directions
• When the imaging voxel is equal in all dimensions thre is no loss of information when data are reformatted in a differernt plane.
• An isotropic voxel ensures that there is no data loss with either multiplanar reformation(MPR) or volume rendering (VR)

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Isotropic Spatial Resolution

• It is the resolution where the cross-plane resolution (z) match that of the in-plane (x-y)
• Since in most CT scans the pixel length is considerably smaller than the slice thickness, the reformatted scan can have an unusual appearance or stair-step artifact
• Modern MDCT for body imaging has isotropic resolution.

Advantages :-

• Creates MPR images with the same spatial resolution as the original sections.
• Avoids the need for direct coronal scanning; reducing dose and acquisition time

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Isotropic Spatial Resolution

Image or Slice thickness

Fig: The MTF shows higher spatial resolution along z for thinner slice images, and the z-axis resolution degrades (the MTF amplitude is reduced) as the reconstructed slice thickness increases.

Slice Sensitivity Profile

• Is a graph that shows the effect of broadening of slice in z-axis.
• SSP is rectangular and width is equal to section width , but in spiral scan they are extended and more peaked.
• Due to continuous movement of patient through gantry the data are displaced along z axis causing widening of slice sensitivity profile.
• FWHM (Full Width at Half Maximum) of SSP gives Effective slice thickness.

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Slice Sensitivity Profile

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• Fig: In traditional axial scanning, selected slice thickness is equal to effective slice thickness. However, because of the interpolation process used in helical scanning, the effective slice thickness may be wider than the selected slice thickness. Also called the effective slice thickness.

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Slice Sensitivity Profile

• Increase in pitch widens SSP
• Increase in pitch means movement is greater than the slice width , so there is increased slice distortion and increase in effective slice thickness.
• 360˚ linear interpolation algorithm also widens slice sensitivity profile (replaced by 180˚ linear interpolation algorithm)
• SSP in 180˚ linear interpolation is reduced so allowed imaging at pitch greater than 1.

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Slice Sensitivity Profile

Fig: illustrates that in one multi-detector scanner, the table speed can affect the slice sensitivity profile and effective slice thickness. The top curve was obtained using 4 x 5mm collimation mode, pitch of 0.75 (table speed of 15 mm/rot) and a nominal reconstructed slice thickness of 5mm; the calculated FWHM was 5.31 mm. The bottom curve was obtained using the same parameters except that the pitch was increased to 1.5 (30 mm/rot) and the resulting calculated FWHM was 6.24 mm; a 17% increase.

Tradeoff Between Spatial Resolution and Slice thickness

• At same KV and mAs, number of detected photons varies linearly with slice thickness.
• Thinner slice provides higher spatial resolution but increased image noise.
• Thicker Slice provides higher contrast resolution but poor spatial resolution.

Reconstruction Filter

• Mathematical filter applied during reconstruction (filtered back projection) to remove the blur from images.
• Affects spatial resolution but requires tradeoffs depending on clinical needs.
• Sharp - high spatial resolution but yields greater image noise.
• Soft or Smooth - Reduces image noise but also degrades spatial resolution.

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Reconstruction Filter

FIGURE :CT spatial resolution phantom, consisting of 4–12 line-pairs per centimeter (from American College of Radiology accreditation phantom), reconstructed using standard (A) and bone (B, high-resolution) filters.

Contd…

Fig: shows the MTF of two different reconstruction filters measured on a multidetector scanner (GE LightSpeed Qx/I

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medium

sharp

Patient motion

• Motion creates blurring in the image and degrades spatial resolution
• Shortened scan time may help improve SR.

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Contrast Resolution

• Ability of a system to differentiate objects with similar densities on the image.
• CT is far superior in detecting low contrast differences because of :
• Scatter rejection by pre pt. and pre detector collimators
• Its consideration of the contribution of attenuation coefficient not only by atomic number differences but also by mass density differences
• Radiography can discriminate a density difference of approximately 5%, CT can detect density differences from 0.25% to 0.5%, depending on the scanner

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Contrast Resolution

Fig: No large differences are noted in mass density and effective atomic number among tissues, but the differences are greatly amplified by computed tomography imaging.

Contrast resolution

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ACR Phantom

• Low-contrast resolution can be measured with phantoms that contain low-contrast objects of

different sizes and with a small difference in density(typically frim 4 to 10 HU) from the

background

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Contrast Resolution

• 1% contrast difference corresponds to a difference of 10 HU
• The ability to image low-contrast objects with CT is limited by the size and uniformity of the object and by the noise of the system.
• The contrast between a structure and its surroundings is ONLY detectable if it is 3-5 times greater than the noise in the image.
• Because the difference between object and background is small, noise plays an important role in low-contrast resolution.

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Contrast resolution

• Artificially increased by adding a contrast medium such as Iodine.
• ↑ photon energy reduces contrast for high atomic number lesions, which contain iodine than for soft tissues.

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Factors affecting Contrast Resolution

• Pixel Size:- If all parameters are fixed ,increase in FOV increases pixel dimension and the no of x-rays passing through the pixel also increases thereby increasing CR.
• mAs :- By decreasing the mAs(tube current) increases the image noise and decreases the contrast resolution.
• Doubling the mAs of the study increases SNR by 40% decreasing quantum noise but the dose increases linearly with mAs per scan
• Slice Thickness :-
• Because thicker slices allow more photons to reach the detectors they have a a better SNR and appear less noisy
• Increasing the Slice thickness leads to improved contrast resolution at the expense of Spatial resolution.
• Decreasing the Slice thickness leads to decreased SNR and therefore degrades Contrast Resolution(other factors constant)

Factors affecting Contrast Resolution

• Reconstruction Filter:- Bone filter produce lower contrast resolution and soft tissue filter improves contrast resolution at the expense of spatial resolution.

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• Patient Size :- For same x-ray technique, larger patient attenuate more photons resulting in detection of fewer photons causes reduction in SNR as well as contrast resolution

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• Gantry Rotation Speed :- Faster the gantry rotation speed, lesser the contrast resolution.

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Noise

• It is the local statistical fluctuation in the CT numbers of individual picture elements of a homogeneous ROI.
• Even if we image a perfectly uniform object (e.g. a water filled object) there is still a variation in the Hounsfield units about a mean. This is due to noise.
• The SD measurement of an ROI of a known uniform phantom will indicate the degree of noise in an image. The smaller the SD, the less the noise and the better the contrast resolution capability.
• Noise degrades the image by degrading low contrast resolution and introducing uncertainty in the Hounsfield units of the images.
• CT no. are the average values i.e. pixels have a range of values greater than or less than CT no., these variation of pixel value represents image noise.

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Where, xi is each CT value, x is the mean CT value

& n is the no. of CT values averaged

Noise

• The major types of noise include quantum mottle, structural mottle and electronic mottle.
• The dominant source of image mottle:- Quantum mottle.
• Characterized by a grainy appearance of the image.
• Reducing the mAs is expected to increase the noise (measured standard deviation) by 1/√mas. Therefore, if the mAs is reduced by ½, then noise should increase by √2 =1.414 à(40% increase)

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Noise

Noise

• Depends on number of photons used by the detector to form an image which depends on several factors:-
• Incident x ray intensity (kVp , mAs and filtration) : ↑mAs→↑SNR & ↑ contrast and ↓ noise . Dose increases linearly with mAs per scan.
• Quantum detection efficiency of detector: QDE is the ability of the detector to capture the x-rays. More the amount of the x-rays are captured more will be the SNR.
• Slice thickness: ↓ in slice thickness ↑ noise
• Reconstruction filter or kernel: low pass filter reduces and high pass filter ↑ noise (bone filter).
• Pixel size : ↓ in voxel size increases noise. Reducing pixel size, increases spatial resolution (if dose levels are kept same) ↓ SNR & ↓ contrast due to SNR per pixel drop.

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Temporal resolution

• the ability to resolve fast moving objects in the displayed CT image.
• Refers to how rapidly data are acquired. reported in ms.
• gantry rotation speed of 330 ms of a specific 64-slice detector (Somatom Sensation 64, Siemens Medical Solutions, Forchheim, Germany) reports the temporal resolution as 83 to 165 ms.
• Good temporal resolution avoids motion artifacts and motion induced blurring of the image.
• controlled by gantry rotation speed, the number of detector channels in the system, and the speed with which the system can record changing signals.
• A good temporal resolution in CT is realized by fast data acquisition (fast rotation of the X-ray tube)
• can be improved further by using dedicated reconstruction algorithms (cardiac CT with a segmented reconstruction) or by using a dual source CT scanner.

Factors Affecting Temporal Resolution

• Gantry rotation time- decrease in gantry rotation time increases temporal resolution
• Number of detector channel- increase in detector channel in z-direction increases temporal resolution
• Reconstruction method- single-segment reconstruction method has less temporal resolution than multi-segment reconstruction method

Linearity

• refers to the relationship between CT numbers and the linear attenuation values of the scanned object at a designated kVp value.
• CT no. should be consistently same for a particular tissue. Eg.- For water= 0
• To check linearity, calibration should be done frequently by catphan or 5 pin performance test phantom
• Each of the 5 pins are made up of diff. plastic material having known physical and x-ray attenuation properties
• Plot of CT No. Vs linear attenuation co-efficient should be straight line.
• Deviations from linearity should not exceed +/- 5HU over specific ranges (soft tissue or bone).
• Linearity is typically measured semiannually

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Linearity

Fig:Computed tomography (CT) linearity is acceptable if a graph of average CT number versus the linear attenuation coefficient is a straight line that passes through 0 for water.

Uniformity

The CT No. measurement should not change with the location of the selected region of interest (ROI) or with the phantom position relative to the isocentre of the scanner.

• This characteristic of CT system is known as spatial uniformity.
• uniformity is most commonly measured using a water phantom.
• For uniformity measurements, there should be no more than a ±2 HU variation from an ROI placed at the center of the water phantom to those placed at the periphery.
• These tests should be performed on a weekly basis

Artifacts In CT

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• Artifact is “an unwanted density seen in an image which may not be present in the object”
• In computed tomography (CT), the term artifact is applied to any systematic discrepancy between the CT numbers in the reconstructed image and the true attenuation coefficients of the object.

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Classification of Artifacts

• On the basis of Appearance:

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Classification of Artifacts

• On the basis of Appearance:

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Fig:Different appearances of artifacts. A, Streak. B, Ring. C, shading.

Classification of Artifacts

• On the basis of Origin
• Physics based-
• Beam Hardening
• Partial volume effect
• Photon starvation
• Under sampling
• Edge gradient artifact
• Patient based-
• Metal artifacts
• Motion artifacts
• Out of field

• Scanner based
• Ring Artifact
• Tube Arcing
• Helical and Multisection-
• Cone beam artifacts
• Wind mill Artifacts
• Artifacts due to Multiplanar and 3-D reformation
• Stair step Artifacts
• Zebra artifacts

Beam Hardening Artifacts

• Refers to an increase in the mean energy of the x-ray beam as it passes through the patient
• Caused by polychromatic nature of the beam.
• Low energy photons are preferentially absorbed, beam becomes more penetrating causing underestimation of the attenuation coefficient(HU).
• The beam hardening phenomenon induces artifacts in Ct because rays from some projection angles are hardened to a differing extent than rays from other angles and this confuse the reconstruction algorithm.

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Beam Hardening Artifacts

Fig:Effect of beam hardening as the x-ray beam traverses different object sizes. A, Original spectrum. B, After traversing 15 cm water. C, After traversing 30 cm water.

Beam Hardening Artifacts

• A . Cupping artifacts :
• Occurs when hardening is more prone in the center and less at the periphery
• the resultant attenuation profile differs from the ideal profile that would be obtained without beam hardening. A profile of the CT numbers across the phantom displays a characteristic cupped shape

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Uncorrected

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Corrected

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• B . Streaks and dark band
• In very heterogeneous cross sections, dark bands or streaks can appear between two dense objects in an image.
• If a high density materials severely reduces transmission, the detector may record no transmission and streaks and dark band appear.

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Beam Hardening Correction

• Filtration :- a flat piece of metallic material is used to pre-harden the beam and also a bow tie filter is used
• Calibration correction :- Scanners are calibrated using phantoms in a range of sizes
• Beam hardening correction software :-Iterative correction algorithm may be applied when images of bony regions are being reconstructed
• By Operator :- avoid scanning bony regions, either by means of patient positioning or by tilting the gantry .
• select the appropriate scan field of view
• ensure that the scanner uses the correct calibration and beam hardening correction data and, on some systems, the appropriate bowtie filter.
• Using dual energy CT technique

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Beam Hardening Correction

Beam Hardening Correction

• dark banding

Fig:CT images of the posterior fossa show the that occurs between dense objects when only calibration correction is applied (a) and the reduction in artifacts when iterative beam hardening correction is also applied

Partial Volume Artifacts

• Arise when voxel contain many types of tissues.
• Result of averaging the linear attenuation coefficient in a voxel that is heterogeneous in composition .
• Arise essentially from reconstructing low resolution images, typically thick slice images.
• It produces CT numbers as an average of all types of tissues.
• It will appear as bands or streaks.
• These artifacts are a separate problem from partial volume averaging, which yields a CT number representative of the average attenuation of the materials within a voxel.

Partial Volume Artifacts

Remedy

• Using a thin acquisition section width. To limit image noise, thicker sections can be generated by adding together several thin sections.
• reconstructing multiple CT projections, such as the axial and coronal projections.

Partial Volume Artifacts

Photon Starvation Artifacts

• Occurs in highly attenuating region due to inefficient photons passing through the widest part of the patient.
• Manifestation of irregularities caused by noise in the raw data profile
• Image appears noisy with streaks

Photon Starvation Artifacts

• Remedy
• Automatic Tube Current Modulation: the tube current is automatically varied during the course of each rotation
• Adaptive Filtration: This software correction smooths the attenuation profile in areas of high attenuation before the image is reconstructed

Photon Starvation Artifacts

Fig:Projection data as they might appear for a horizontal x-ray beam passing through the shoulders. Diagrams show the data in their original form (a) and with adaptive filtration (b).

Fig:Original axial CT images (top) and coronal reformatted images (bottom) in their original form (a) and after reconstruction with multidimensional adaptive filtration (b).

Undersampling/Aliasing

• Occurs when there is no adequate amount of data within each projection
• Too few projection images acquired to reconstruct high- frequency objects in the image. This is undersampling
• This undersampling causes inaccuracies resulting in an artifact k/a aliasing
• Fine stripes appear to be radiating from dense structure

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Fig:CT image of a Teflon block in a water phantom shows aliasing (arrow) due to undersampling of the edge of the block

Undersampling/Aliasing

• Reduction method
• View aliasing: Increasing the largest possible no. of projection per rotation
• Ray aliasing: Using high resolution technique like flying focal spot and quarter detector shift
• Increase scan time(by slowing the gantry rotation speed or by reducing the helical pitch.

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Edge Gradient Artifact

• Arise from irregularly shaped object that have a pronounced difference in density from surrounding structure
• A common clinical example is artifacts that result when barium and air lie adjacent to each other in the stomach
• Results in streak artifact or shading
• Remedy
• Using thinner slices
• Using a low HU- value oral contrast or neutral contrast such as water
• Change in patient’s position

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Fig:The irregular shading in the left lobe of the liver (indicated by arrows) in this image

Classification of Artifacts

• Physics based artifacts
• Patient based artifacts
• Metal artefacts
• Motion artefacts
• Incomplete projections
• Scanner based artifacts
• Helical & multisection artifacts

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Metal Artifacts

• Manifest itself as “star streaking” artifact.
• It’s caused by presence of metallic objects inside or outside the patient.
• Metallic object absorbs the photons causing an incomplete attenuation profile.
• Reduction methods:-
• A. By operator :-
• Taking off removable metal objects before scanning
• Use of gantry angulation
• Increase technique (kv)
• Use thin slices

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Metal Artifacts

• B. By software correction :-
• Artifact reduction (MAR) algorithms are used to improve CT image quality in patients with metal ware
• There are a number of commercially-available algorithms (in 2019):
• Iterative MAR (iMAR) - Siemens
• MAR for orthopedic implants (O-MAR) - Philips
• single-energy MAR (SEMAR) - Toshiba/Canon
• SmartMAR – GE
• Beam hardening correction software should also be used when scanning metal objects to minimize the additional artifacts due to beam hardening.
• The usefulness of metal artifact reduction software is sometimes limited because, although streaking distant from the metal implants is removed, there still remains a loss of detail around the metal-tissue interface

Metal Artifacts

Fig: CT images of a patient with metal spine implants, reconstructed without any correction (a) and with metal artifact reduction (b

Motion Artifacts

• Occurs due voluntary/involuntary motions, sometimes random or unpredictable motions.
• Produces “GHOSTING” Effect. Also shading , streak or blurring.
• Image appears– as if it is composed of superimposed images.

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Fig: The diagonal shading degrading this image is caused from patient movement during the scan

Motion Artifacts

Remedy

• By operator
• Positioning aids - prevent voluntary movement in most patients.
• Sedation - to immobilize the patient (eg,pediatric patients)
• Short scan time
• Breath hold
• Built-in Features for Minimizing Motion Artifacts
• Overscan and underscan modes.
• Software correction
• Cardiac gating for cardiac imaging

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Motion Artifacts

Fig: Patient peristaltic motion. A, Without compensation. B, With correction

Out of field/Incomplete Projection Artifacts

• Occurs when patient dimension exceed scan field.
• If any portion of the Pt. lies outside the scan field of view, the computer will have incomplete information relating this portion and streaking or shading artifacts can result.

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Reduction:-

• Selection of larger SFOV unit if possible.
• Proper positioning; e:g. Raising patients arms above their head on the scan of chest and abdomen

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Classification of Artifacts

• Physics based artifacts
• Patient based artifacts
• Scanner based artifacts
• Ring artifacts
• Tube arcing
• Helical & multisection artifacts.

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Ring Artifacts

• Occurs in 3rd generation scanner, due to miscalibration of any one of the detectors.
• The detector will record incorrect data in each angular position.
• Detectors towards the center of the detector array contributes ring artifact that is small in diameter than detector in periphery

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Ring artifacts

Avoidance of Ring Artefacts

• Detector calibration
• Detector replacement
• Selecting the correct scan field of view
• Software corrections

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Tube Arcing

• Occurs when there is a short circuit within the tube, typically from cathode to tube envelope.
• Tungsten vapor from anode and cathode intercepts the projectile electrons intended for collisions with the target.
• Causes momentary loss of x-ray output.
• Appear as near-parallel and an equidistant streak pattern on transaxial computed tomography (CT) images and as a “horizontal” hypodense band on the coronal and sagittal CT images.

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Remedy:

• Tube Replacement.

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Tube Arcing

Fig: CT tube arcing artifact seen

Fig: Plot of generator kV versus time in images showing drop of voltage corresponding to appearance of tube arcing artifact (A and B)

Classification of Artifacts

• Physics based artifacts
• Patient based artifacts
• Scanner based artifacts
• Helical & multi-section artifacts.
• Cone beam artifacts
• Wind mill

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Helical Artifacts in the Axial Plane

• due to the helical interpolation and reconstruction process.
• occur when anatomic structures change rapidly in the z direction (eg, at the top of the skull) and are worse for higher pitches.

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Fig: Consecutive axial CT images from a helical scan of a cone-shaped phantom lying along the scanner axis.

Helical Artifacts in the Axial Plane

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Fig: Series of CT images from a helical scan of the abdomen shows helical artifacts (arrows).

Helical Artifacts in the Axial Plane

To keep helical artifacts to a minimum

• steps must be taken to reduce the effects of variation along the z axis
• This means using, where possible, a low pitch, a 180° rather than 360° helical interpolator
• thin acquisition sections rather than thick.
• Sometimes, it is still preferable to use axial rather than helical imaging to avoid helical artifact

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Cone Beam Effect

• Caused by incomplete or insufficient projection samples as a result of the cone beam geometry of multislice CT.
• As the number of sections acquired per rotation increases, a wider collimation is required and the x-ray beam becomes cone shaped rather than fan shaped.

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Cone Beam Effect

• Detector elements in the periphery of the array are exposed more obliquely to those of the center.
• However, it is drastically reduced by use of cone beam reconstruction algorithms

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Cone Beam Effect

• Cone beam effect is more apparent with larger cone angle or large pitch
• Remedy
• acquire a more complete data set

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Fig:CT images from data collected by an outer detector row (a) and an inner detector row (b) show cone beam artifacts around a Teflon rod, which was positioned 70 mm from the isocenter at an angle of 60° to the scanner axis.

Wind Mill Artifact

• More complicated form of axial image distortion.
• Seen in thin slice images reconstructed from high pitch helical multislice CT images.
• Type of aliasing artifact
• The term wind mill comes from the spiral appearance of shading artifact.

Remedy

• Z-filter helical interpolators
• Using low pitch when possible

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Avoidance and Correction of Helical and Cone Beam Artifacts

• Reconstruction techniques like 3D Back projection, Adaptive multiple plane reconstruction (AMPR), Weighted Hyper plane reconstruction (WHR) are used that account for the cone beam angle thereby reducing cone beam artifact
• Lower Pitch (<1) can be used
• Z sampling methods used during reconstruction to remove windmill artifact.

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Classification of artefacts

• Physics based artifacts
• Patient based artifacts
• Scanner based artifacts
• Helical & multi-section artifacts.
• Artifacts due to Multiplanar and 3-D Reformation
• Stair Step Artifacts
• Zebra Artifacts

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Stair Step Artifacts

• Improper selection of slice thickness and slice increment when generating MPR and 3D image.
• Appears around the edges of the structures in the reformatted images.
• Less severe with the helical scans .

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Stair Step Artifacts

Remedy:

• Using Thin slice
• 50% overlap on recon slice incrementation.
• Stair step artifacts are virtually eliminated in multiplanar and three-dimensional reformatted images from thin-section data obtained with today’s multisection scanners

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Zebra Artifacts

• Appears as faint stripes in the Multiplanar and 3D reformatted images from helical interpolation.
• Because the helical interpolation process gives rise to a degree of noise inhomogeneity along the z axis
• Becomes more pronounced away from the axis of rotation because the noise in homogeneity is worse at off-axis

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Fig: Maximum intensity projection image obtained with helical CT shows zebra artifact

Summary

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Conclusion

• There is always compromise between spatial resolution, contrast resolution and the random noise.
• Demand for high spatial resolution has been the driving force behind MDCT technology’s strive for thinner detectors in z-direction.
• Artifacts arise from a range of sources and can degrade the quality of an image to varying degrees.
• Designed features incorporated in some scanners minimizes some artifacts and some can be partially corrected by the scanner software.
• Careful patient positioning and optimum selection of scan parameters are the most important factors in avoiding image artifacts.

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• Bushberg., 2012. Essential Physics of Medical Imaging, The. Lippincott, Williams & Wilkins, 3^rd ed
• Romans, L. Computed tomography for technologists, 2^nd ed
• Seeram, E. Computed tomography: Physical Principles, Clinical Applications, and Quality, 4^th ed
• Bushong, S. Radiologic science for technologists 11^th ed
• Barrett, Julia F., and Nicholas Keat. “Artifacts in CT: Recognition and Avoidance.” RadioGraphics, vol. 24, no. 6, 2004, pp. 1679–1691., doi:10.1148/rg.246045065.
• Goldman, Lee W. “Principles of CT: Radiation Dose and Image Quality.” Journal of Nuclear Medicine Technology, Society of Nuclear Medicine, 1 Dec. 2007, tech.snmjournals.org/content/35/4/213.
• Tradeoffs in CT Image Quality and Radiation Dose ... aapm.org/meetings/04AM/pdf/14-2328-89141.pdf.
• Daniel E.Wessell physics CT lectures I & II, Mayo Clinic

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References�

Thank you

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