Banding Artifacts

In very heterogeneous cross sections, dark bands or streaks can appear between two dense objects in an image. They occur because the portion of the beam that passes through one of the objects at certain tube positions is hardened less than when it passes through both objects at other tube positions. This type of artifact can occur both in bony regions of the body and in scans where a contrast medium has been used. Built-in Features for Minimizing Beam Hardening.—Manufacturers minimize beam hardening by using filtration, calibration correction,
and beam hardening correction software.
Filtration: A flat piece of attenuating, usually metallic material is used to “pre-harden” the
beam by filtering out the lower-energy components before it passes through the patient. An additional “bowtie” filter further hardens the edges of the beam, which will pass through the thinner parts of the patient. Calibration correction: Manufacturers calibrate their scanners using phantoms in a range of sizes. This allows the detectors to be calibrated with compensation tailored for the beam hardening effects of different parts of the patient.
Beam hardening correction software:
An iterative correction algorithm may be applied when images of bony regions are being reconstructed. This helps minimize blurring of the bone–soft tissue interface in brain scans and also reduces the appearance of dark bands in nonhomogeneous cross sections (Fig a-b)
Banding artifact:

Cause
Effective energy is shifted to higher value as the X-rays pass through an object
Correction
• Prefilter the X-ray beam near the focus
• Avoid highly absorbing bony regions
• Algorithms

ARTIFACTS IN SPIRAL CT

Misregistration

Patient motion can cause misregistration artifacts, which usually appear as shading or streaking in the reconstructed image (Fig 16). Steps can be taken to prevent voluntary motion, but some involuntary motion may be unavoidable during body scanning. However, there are special features on some scanners designed to minimize the resulting artifacts.

Avoidance of Motion Artifacts by the Operator.—
The use of positioning aids is sufficient to prevent voluntary movement in most patients.
However, in some cases (e.g, pediatric patients), it may be necessary to immobilize the patient by means of sedation. Using as short as scan time as possible helps minimize artifacts when scanning regions prone to movement. Respiratory motion can be minimized if patients are able to hold their breath for the duration of the scan. The sensitivity of the image to motion artifacts depends on the orientation of the motion. Therefore, it is preferable if the start and end position of the tube is aligned with the primary direction of motion, for example, vertically above or below a patient undergoing a chest scan. Specifying body scan mode, as opposed to head scan mode, may automatically incorporate some motion artifact reduction in the reconstruction.

Built-in Features for Minimizing Motion Artifacts.—
Manufacturers minimize motion artifacts by using overscan and underscan modes, software correction, and cardiac gating. Overscan and underscan modes: The maximum discrepancy in detector readings occurs between views obtained toward the beginning and end of a 360° scan. Some scanner models use overscan mode for axial body scans, whereby an extra 10% or so is added to the standard 360° rotation. The repeated projections are averaged, which helps reduce the severity of motion artifacts. The use of partial scan mode can also reduce motion artifacts, but this may be at the expense of poorer resolution. Software correction: Most scanners, when used in body scan mode, automatically apply reduced weighting to the beginning and end views to suppress their contribution to the final image. However, this may lead to more noise in the vertical direction of the resultant image, depending on the shape of the patient. Additional, specialized motion correction is available on some scanners. The effectiveness of one such technique in correcting artifacts due to motion of a fluid interface is demonstrated in Figure 17.
Cardiac gating: The rapid motion of the heart can lead to severe artifacts in images of the heart and to artifacts that can mimic disease in associated structures, for example, dissected aorta. To overcome these difficulties, techniques have been developed to produce images by using data from just a fraction of the cardiac cycle, when there is least cardiac motion. This is achieved by combining electrocardiographic gating techniques with specialized methods of image reconstruction (4).


PURPOSE: To determine the frequency and patterns of respiratory-induced misregistration artifact seen on spiral CT of the liver. MATERIALS AND METHODS: Two hundred patients with hepatic mass underwent spiral CT, and arterial phase images were compared with those of the portal phase in all cases and or of the delayed phase in 138. The patterns of misregistration artifact were divided into two groups: skipping, where at least two slices in the craniocaudal length of the mass were missed, and the partial volume veraging artifact thus excluded; and overlapping, where the same or reversed images were seen in succeeding sequences. We reviewed the location and size of the masses, and the presence or absence, and patterns of the misregistration artifact. RESULTS: Fourteen (7%) of 200 spiral CT scans demonstrated the misregistration artifact; in five of these there was skipping (involving a hepatic mass larger than 2 cm in two cases, and one smaller than 2 cm in three cases), and in nine there was overlapping (six masses larger than 2 cm, and three smaller than this). A lipiodol-laden mass measuring 5 mm was completely missed during the arterial phase. and in one case the spleen sequence was reversed. Thirteen (93%) of fourteen masses were located in the right lobe. CONCLUSION: Two patterns of misregistration artifact, skipping and overlapping, were observed, and their combined frequency was 7%. So as not to miss small hepatic masses or overestimate their size, careful respiratory control is therefore needed.

A)Topographic 3D displays of helical CT PET with a mild-to-moderate anterior and lateral defect (top row) that is not present on cine CT PET (bottom row). White indicates the highest myocardial uptake of 82Rb, reflecting the highest myocardial perfusion, with red being the next highest and progressively lower perfusion indicated by color gradations from red to yellow, green, and blue. (B) For same patient as in A, misregistration on helical CT-PET fusion images in transaxial (top) and coronal (bottom) views. Arrows indicate heart borders on helical CT and PET emission images as unmatched, with region of misregistration corresponding to area of artifactual defect. Magnified inset illustrates quantification of misregistration in transaxial view—here, 12 mm—using an electronic caliper on the screen. (C) For same patient, cine CT-PET fusion images show good coregistration associated with no defect and a normal scan.

Stair-stepping Artifacts


Stair step artifacts appear around the edges of structures in multiplanar and three-dimensional reformatted images when wide collimations and nonoverlapping reconstruction intervals are used. They are less severe with helical scanning, which permits reconstruction of overlapping
sections without the extra dose to the patient that would occur if overlapping axial scans were
obtained (Fig 28). Stair step artifacts are virtually eliminated in multiplanar and three-dimensional reformatted images from thin-section data obtained with today’s multisection scanners

Stair-step artifacts result from data reconstruction and misregistration. With loss of data along the z-axis, apparent areas of both decreased and increased attenuation occur. This apparent decrease in attenuation of the vessel lumen on multiple images can mimic the appearance of PE. On axial images, the vessel lumen is generally affected on alternating images with normal intervening vessel enhancement. On reconstructed images, the "stair-step" appearance can be appreciated (figure 11). Decreasing the slice thickness and overlapping images will reduce the amount of artifact along the z-axis; however, when slice thickness is too thin, quantum mottle, scan time, and radiation dose to the patient can provide significant problems.

Stair-step artifact. (A) Axial CT shows central low attenuation in the right lower lobe pulmonary artery raising suspicion for pulmonary embolus (arrow). (B) Coronal reformatted image shows linear low attenuation across the vessel from data misregistration along z-axis


Stair step artifact is associated with inclined surfaces in
reformatted slices
Causes
• Large reconstruction interval
• Asymmetric helical interpolation
Correction
• Collimation and feed less than feature sizes,
and small reconstruction interval
• Adaptive interpolation

Pitch Effect





In general the same artifacts are produced in spiral and conventional scanning. Meanwhile, because the spiral scanning requires an interpolation process to recover the consistent projections of individual slices, additional artifacts may be produced. Appearance and severity of spiral artifacts depend on scanning pitch and the type of interpolation algorithm. In single CT spiral scanner, the pitch is the table movement per tube rotation/slice collimation. For a typical 1 second rotation scanner a pitch of 2 means the table traveled 10 mm with a 5 mm slice width or collimation. In multi-slice CT spiral scanners, the definition is table movement per rotation/single slice collimation. With a 1 sec scanner there is 1 rotation per second. So if the table travels 4 mm in a second and a 1 mm collimator is used then the pitch would be 4. Fig.
6 shows a spiral scanning and the pitch for this scanning. If pitch is increased while holding kVp, mA, and beam collimation constant, then the table speed increases, mAs decreases, patient dose decreases, and either the effective slice width increases or the image noise increases. So for reducing the artifacts due to spiral rotation, we should decrease pitch. Fig. 7 shows the effect of reducing pitch for a multi-slice spiral scanner.

To understand the effect of pitch on raw data interpolation in multi-slice spiral/helical CT, and provide guidelines for scanner design and protocol optimization. Multi-slice spiral CT is mainly characterized by the three parameters: the number of detector arrays, the detector collimation, and the table increment per X-ray source rotation. The pitch in multi-slice spiral CT is defined as the ratio of the table increment over the detector collimation. In parallel to the current framework for studying longitudinal image resolution, the central fan- beam rays of direct and opposite directions are considered, assuming a narrow cone-beam angle. Generally speaking, sampling in the Radon domain by the direct and opposite central rays is non-uniform along the longitudinal axis. Using a recently developed methodology for quantifying the sensitivity of signal reconstruction from non-uniformly sampled finite points, the effect of pitch on raw data interpolation is analyzed in multi-slice spiral CT. Unlike single-slice spiral CT, in which image quality deceases monotonically as the pitch increases, the sensitivity of raw data interpolation in multi-slice spiral CT increases in an alternating way as the pitch increases, suggesting that image quality does not decrease monotonically in this case. The most favorable pitch can be found from the sensitivity-pitch plot for any given set of multi-slice spiral CT parameters. An example for four-slice spiral CT is provided. The study on the pitch effect using the sensitivity analysis approach reveals the fundamental characteristics of raw data interpolation in multi-slice spiral CT, and gives insights into interaction between pitch and image quality. These results may be valuable for design of multi-slice spiral CT scanners and imaging protocol optimization in clinical applications.

Pitch dependence of longitudinal sampling and aliasing effects in multi-slice helical computed tomography (CT).

In this work, we investigate longitudinal sampling and aliasing effects in multi-slice helical CT. We demonstrate that longitudinal aliasing can be a significant, complicated, and potentially detrimental effect in multi-slice helical CT reconstructions. Multi-slice helical CT scans are generally undersampled longitudinally for all pitches of clinical interest, and the resulting aliasing effects are spatially variant. As in the single-slice case, aliasing is shown to be negligible at the isocentre for circularly symmetric objects due to a fortuitous aliasing cancellation phenomenon. However, away from the isocentre, aliasing effects can be significant, spatially variant, and highly pitch dependent. This implies that measures more sophisticated than isocentre slice sensitivity profiles are needed to characterize longitudinal properties of multi-slice helical CT systems. Such measures are particularly important in assessing the question of whether there are preferred pitches in helical CT. Previous analyses have generally focused only on isocentre sampling patterns, and our more global analysis leads to somewhat different conclusions than have been reached before, suggesting that pitches 3, 4, 5, and 6 are favourable, and that half-integer pitches are somewhat suboptimal.

Scalloping

Intensity based registration (e.g., mutual information) suffers from a scalloping artifact giving rise to local maxima and sometimes a biased global maximum in a similarity objective function. Here, we demonstrate that scalloping is principally due to the noise reduction filtering that occurs when image samples are interpolated. Typically at a much smaller scale (100 times less in our test cases), there are also fluctuations in the similarity objective function due to interpolation of the signal and to sampling of a continuous, band-limited image signal. Focusing on the larger problem from noise, we show that this phenomenon can even bias global maxima, giving inaccurate registrations. This phenomenon is readily seen when one registers an image onto itself with different noise realizations but is absent when the same noise realization is present in both images. For linear interpolation, local maxima and global bias are removed if one filters the interpolated image using a new constant variance filter for linear interpolation (cv-lin filter), which equalizes the variance across the interpolated image. We use 2D synthetic and MR images and characterize the effect of cv-lin on similarity objective functions. With a reduction of local and biased maxima, image registration becomes more robust and accurate. An efficient implementation adds insignificant computation time per iteration, and because optimization proceeds more smoothly, sometimes fewer iterations are needed.
New spiral-related reconstruction artifacts arise with this CT method, which are mainly due to interpolation inaccuracies in combination with the selection of the pitch factor. In principle, one finds here as many image error types as interpolation method. In this section, the interpolation problem cannot be discussed in detail, but the so-called scalloping artifact should be mentioned, which is due to the fact that the slice sensitivity profile is increased in spiral CT so that partial volume artifacts also become stronger.
Scalloping is phenomenon arising, for example, in skull tomographies, particularly in slice positions in which the skull diameter quickly changes its axial direction. Two slices with different curvatures in axial direction have been selected in a skull phantom. For comparison, the slices with a thickness of 1 mm were measured for both positions conventionally, i.e., without a continuous table feed.
The virtually different thickness of the skull in two slice position is due to the fact that the angle between the corresponding reconstruction layer and the local skull surface normal vector varies.

Endosteal scalloping demonstrated on CT scan. Axial CT through the mid humerus clearly demonstrates endosteal erosion (arrow) resulting from a thyroid carcinoma metastatic lesion.

What is Segmentation? Explain how it works, and provide images as examples of the technique.

Where different structures have similar radiodensity, it can become impossible to separate them simply by adjusting volume rendering parameters. The solution is called segmentation, a manual or automatic procedure that can remove the unwanted structures from the image

Segmentation can be performed manually or (semi)automatically. Segmentation algorithms are often based on the principle of region growing. Placing one or more seed points initiates the segmentation of the target structure. From these seed points, more and more neighboring voxels that fulfill predefined criteria are included in the segmentation (14). The technique can be applied in two ways: segmentation of the desired tissue or segmentation of the undesired tissue with subsequent removal from the data. The latter method removes only interfering tissue (bone or densely enhanced veins) from the CT angiography data and retains soft tissue as well as contrast-enhanced vessels for further evaluation. To refine the boundary of the segmented structures, morphologic dilation operations may be applied. A particular problem in threshold-based segmentation algorithms are areas with close contact of two tissue types with comparable attenuation, such as bone and contrast-enhanced vessels (course of the ICA through the skull base; intraforaminal sections of the vertebral artery) (Fig 6). Although the process of segmentation is semiautomatic, user interaction is necessary to set additional seeding points or to intervene in cases of inclusion of neighboring structures due to leakage of the region-growing algorithm. These procedures can be time-consuming and may exceed practical limits in routine clinical work flow.

The segmentation involves two issues; separating the bone from the surrounding soft tissue and separating different parts of the bone from each other. The goal with the segmentation process is a surface model representing the anatomy of a specific patient that can be incorporated into the simulator system and used for practice or pre-operative planning.
The method that has been employed for the automatic segmentation process is the Morphon method [1-3]. This is a general non-rigid registration method that has been developed at our department. The method takes a prototype image/volume and deforms it until it fits the target image/volume. The registration process is an iterative algorithm where each repetition passes through the following steps: * Displacement estimation * Deformation field accumulation and regularisation * Prototype deformation The algorithm is initiated on a coarse resolution scale to catch large, global displacements, and continues to finer resolution scales until an optimal match between the two datasets is obtained. For a more detailed description of this method the reader is referred to references [1-3].
As mentioned above the algorithm works on both 2D and 3D datasets. In this project we work with 3D CT volumes collected from patients with this type of fracture. The prototype is a volume where each voxel is labeled such that it belongs either to the pelvis, the femur or the background. By deforming this segmented representation of the hip area until it fits the corresponding structure in a specific patient we obtain a volume with a labeled representation of that specific patient's anatomy. From the labeled volume it is easy to generate surface models of the pelvis and femur as two separate objects.

Maximum Intensity Projection

A maximum intensity projection (MIP) is a computer visualization method for 3D data that projects in the visualization plane the voxels with maximum intensity that fall in the way of parallel rays traced from the viewpoint to the plane of projection. This implies that two MIP renderings from opposite viewpoints are symmetrical images.
This technique is computationally fast, but the 2D results do not provide a good sense of depth of the original data. To improve the sense of 3D, animations are usually rendered of several MIP frames in which the viewpoint is slightly changed from one to the other, thus creating the illusion of rotation. This helps the viewer's perception to find the relative 3D positions of the object components. However, since the projection is orthographic the viewer cannot distinguish between left or right, front or back and even if the object is rotating clockwise or anti-clockwise.
MIP is used for the detection of lung nodules in lung cancer screening programs which utilise computed tomography scans. MIP enhances the 3D nature of these nodules, making them stand out from pulmonary bronchi and vasculature.
MIP imaging was invented for use in Nuclear Medicine by Jerold Wallis, MD, in 1988, and subsequently published in IEEE Transactions in Medical Imaging [1]. In the setting of Nuclear Medicine, it was originally called MAP (Maximum Activity Projection). Additional information can be found in other articles by the same author [2], [3].
Use of depth weighting during production of rotating cines of MIP images can avoid the problem of difficulty of distinguishing right from left, and clockwise vs anti-clockwise rotation. MIP imaging is used routinely by physicians in interpreting Positron Emission Tomography (PET) or Magnetic Resonance Angiography studies.

Maximum intensity projection (MIP) is a volume rendering technique which is used to extract high-intensity structures from volumetric scalar data. At each pixel the highest data value encountered along the corresponding viewing ray is determined. MIP is commonly used to extract vascular structures from medical MRI data sets, i.e., angiography. The usual way to compensate for the loss of spatial and occlusion information in MIP images is to view the data from different view points by rotating them. As the generation of a MIP is usually non-interactive, this is done by calculating multiple images offline and playing them back as an animation. In this paper a novel algorithm is proposed which is capable of interactively generating Maximum Intensity Projection images even on low-end hardware using parallel projection. Two methods for preprocessing data and removing voxels which will due to their neighborhood never contribute to a MIP are discussed. The remaining voxels are stored in a way which guarantees optimal cache coherency regardless of the viewing direction. For use on low-end hardware, a preview-mode is included which renders only the more significant parts of the volume during user interaction. Furthermore, we demonstrate the usability of our data structure for extensions of the MIP technique like MIP with depth-shading and local maximum intensity projection (LMIP).

The maximum intensity projection (MIP) is a popularly used algorithm for display of MRA images, but its performance has not been rigorously analyzed before. In this paper, four measures are proposed for the performance of the MIP algorithm and the quality of images projected from three-dimensional (3-D) data, which are vessel voxel projection probability, vessel detection probability, false vessel probability, and vessel-tissue contrast-to-noise ratio (CNR). As side products, vessel-missing probability, vessel receiver operating characteristics (ROC's), and mean number of false vessels are also studied. Based on the assumptions that the intensities of vessel, tissue, and noise along a projection path are independent Gaussian, these measures are derived and obtained all in closed forms. All the measures are functions of explicit parameters: vessel-to-tissue noise ratio (VTNR) and CNR of 3 D data prior to the MIP, vessel diameter, and projection length. It is shown that the MIP algorithm increases the CNR of large vessels whose CNR prior to the MIP is high and whose diameters are large. The increase in CNR increases with projection path length. On the other hand, all the proposed measures indicate that the small vessels that have low CNR prior to the MIP and small diameters suffer from the MIP. The performance gets worse as projection path length increases. All measures demonstrate a better performance when the vessel diameter is larger. Other properties and possible applications of the derived measures are also discussed.

Purpose of bolus tracking in contrast administration,

Bolus tracking is a technique used in computed tomography imaging, to visualise vessels more clearly. A bolus of radio-opaque contrast media is injected into a patient via a peripheral intravenous cannula. Depending on the vessel being imaged, the volume of contrast is tracked using a region of interest at a certain level and then followed by the CT scanner once it reaches this level. Images are acquired at a rate as fast as the contrast moving through the blood vessels.
This method of imaging is used primarily to produce images of arteries, such as the aorta, pulmonary artery, cerebral and carotid arteries. The image shown illustrates this technique on a sagittal MPR (multi planar reformat). The image is demonstrating the blood flow through an abdominal aortic aneurysm or AAA. The bright white on the image is the contrast. You can see the lumen of the aorta in which the contrast is contained, surrounded by a grey 'sack', which is the aneurysm. Images acquired from a bolus track, can be manipulated into a MIP (maximum intensity projection) or a volume rendered image.

I have enjoyed reading recent published works on contrast material enhancement by Dr Cademartiri and colleagues. As an advocate for the bolus-tracking technique at computed tomographic (CT) angiography, I welcome the finding in their article in the December 2004 issue of Radiology (1) that bolus tracking provides more homogeneous enhancement than does the test-bolus technique. However, I am concerned that the conclusion statement in this article is overly simplified and thus may be misinterpreted and misused in practice. I believe that their valuable work will be correctly used and implemented if we clarify and address key factors that were not described in the conclusion but were crucial in determining the quality of contrast enhancement between the two groups in their study.
The main reason in their study why bolus tracking provides more homogeneous enhancement than the test-bolus technique is that the scan delay was longer with the former than with the latter. I strongly agree with their statement in the discussion section (page 821) that the geometry of contrast enhancement differed in the two groups mainly because the calculated scan delay was 6 seconds later in the bolus-tracking group than in the test-bolus group. The bolus-tracking method "fortuitously" resulted in a longer and more desirable scan delay than the test-bolus method because there was an intrinsic built-in delay in the bolus-tracking method between the trigger and the start of the diagnostic scan. It is highly likely that, if an additional delay of 6 seconds had been included in the test-bolus group or if the bolus-tracking technique had been triggered earlier or much later, the test-bolus method would have provided better contrast enhancement than the bolus-tracking method. I need to emphasize that the main determinant of contrast enhancement was an additional delay, not the test-bolus versus the bolus-tracking methods.
The time determined by the test-bolus or bolus-tracking techniques simply represents the time of contrast material arrival or bolus transit of the contrast material (2). This time should not be lackadaisically assumed to serve as the scan delay, but rather as a means of individualizing the scan delay relative to it by including an "additional delay" (2,3). This requirement of an additional delay for optimizing the scan delay for fast CT was discussed and reported previously (3). The magnitude of the additional delay depends on the injection duration, scan duration, and location of the target organ (2).
To achieve adequate contrast enhancement throughout the scan duration, we have to consider the injection duration in the computation of the additional delay (4). Time to peak aortic enhancement is determined by the relative contributions of injection duration and contrast medium traveling time. In the current study, the injection duration was 25 seconds. The time to peak aortic enhancement, which is shortly after the completion of injection (4,5), would be around 30 seconds. Although it was not specified in the article, I estimate from the given CT scan parameters and scan range that the scan duration is approximately 15–20 seconds. By using a common strategy of setting the center of the scan to coincide with the time to peak aortic enhancement, the scan delay is calculated as 20.0–22.5 seconds (30 seconds minus a half of 15–20 seconds). This scan delay matches more closely with the scan delay estimated by the bolus-tracking method (mean, 20.6 seconds) than that with the test-bolus method (mean, 14.6 seconds) in the study. As a result, more favorable contrast enhancement was achieved with the bolus-tracking method. However, we have to keep in mind that when we use different injection and scan protocols, the scan delay should be adjusted accordingly. For a longer injection, a longer scan delay would be preferred. With a faster CT scan, such as when 64–detector row CT is used, the scan duration is shorter, and thus, a longer scan delay is desirable to scan during maximal contrast enhancement.
Other minor points include that, although the test-bolus technique used in their study referred to the approach of setting the CT scan delay at the time to peak test-bolus contrast enhancement (6,7), there were other variations of determining the scan delay from the time to peak enhancement in the test-bolus technique (8,9). On page 818, 180-gauge should be 18-gauge.
In summary, the time of contrast material arrival can be estimated either with a test-bolus or bolus-tracking method, although the bolus-tracking method is preferred because of its efficiency and practicality. It is critical to include an appropriate additional delay, which is related to the scanning speed and injection duration, to determine an optimal scan delay from the estimated time of contrast material arrival. This consideration is particularly important with faster CT. An inadvertently applied bolus-tracking approach may fare no better than a properly designed test-bolus approach.

Below is the MDCT Venogram. 130mls of contrast into the median cubital vein. 130sec delay, bolus tracking with the roi on IVC; had to window this to see the IVC. Then scan from diaphragm to ankles.