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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