Cone Beam Computed Tomography Biology Essay


CBCT is a relatively recent technology. It is a medical image acquisition technique based on a divergent pyramidal- or cone-shaped source of ionizing radiation directed through the middle of the area of interest onto a two-dimensional detector area on the opposite side.

Among the first clinical application were single photon emission computerized tomography (SPECT) and angiography but more recent medical applications have included image guided radiotherapy and mammography. Imaging is accomplished by using a rotating gantry to which an x-ray source and detector are fixed. The x-ray source and detector rotate around a rotation fulcrum fixed within the center of the region of interest. During the rotation, multiple (from 150 to more than 600) sequential planar (2D) projection images of the field of view (FOV) are acquired in a complete, or sometimes partial, arc.

The images are reconstructed in a three-dimensional (3D) data set using a modification of the original cone beam algorithm described by Feldkamp et al in 1984. Because CBCT exposure incorporates the entire field of view, only one rotational sequence of the gantry is necessary to acquire enough data for image reconstruction (Scarfe & Farman, 2008; White, 2008; De Vos et al, 2009).

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It has only been since the late 1990s that computers capable of computational complexity and x-ray tubes capable of continuous exposure have enabled clinical systems to be manufactured that are inexpensive and small enough to be used in the dental office. Two additional factors have converged to make CBCT possible, which are: development of compact high-quality two-dimensional detector arrays, and refinement of approximate cone-beam algorithms (Scarfe & Farman, 2008).


Current cone-beam machines scan patients in three possible positions: (1) sitting, (2) standing, and (3) supine (FIG.XX). Equipment that requires the patient to lie supine physically occupies a larger surface area and may not be accessible for patients with physical disabilities. Standing units may not be able to be adjusted to a height to accommodate wheelchair- bound patients. Seated units are the most comfortable; however, fixed seats may not allow scanning of physically disabled or wheelchair-bound patients. Because scan times are often greater than those required for panoramic imaging, perhaps more important than patient orientation is the head restraint mechanism used. Despite patient orientation within the equipment, the principles of image production remain the same (Scarfe & Farman, 2008). Bontempi et al 2008 stated that patient positioning during CBCT scanning can significantly influence of motion, they observed that head motion is reduced when patients are lying down, even if no head restrains are used (Bontempi et al, 2008).

FIG XX A: Classic i-CAT, seated. B:Kodak 9000 3D Extraoral Imaging System, standing. C: Newtom 5G, supine.


During the scan rotation, each projection image is made by sequential, single-image capture of attenuated x-ray beams by the detector. There are two methods of exposing the patient: (1) using a constant beam of radiation during the rotation and allow the x-ray detector to sample the attenuated beam in its trajectory (2). Using x-ray beam pulsed that coincides with the detector sampling. Second alternative implies less radiation exposure, without altering image quality because continuous radiation emission does not contribute to the formation.


It is the scan volume able to be covered, and depends primarily on the detector size and shape, the beam projection geometry, and the ability to collimate the beam. The shape of the scan volume can be either cylindrical or spherical. Collimation of the primary x-ray beam limits x-radiation exposure to the region of interest (Scarfe & Farman, 2008).


Limitation of field size ensures that an optimal FOV can be selected for each patient, based on disease presentation and the region designated to be imaged. FOVs may vary from a few centimetres in height and diameter to a full head reconstruction. Several CBCT units offer a range of FOV, whilst a fixed FOV is provided by other units. Some CBCT machines offer the option to collimate the beam to the minimum size needed to image the area of interest (SEDENTEXCT, 2011).

CBCT systems can be categorized according to the available FOV or selected scan volume height as follows:

Localized region: < 5 cm.

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Single arch: 5-7 cm.

Interarch: 7-10 cm.

Maxillofacial: 10- 15 cm.

Craniofacial: > 15 cm.

Kau et al divided CBCT devices into four subcategories according to the size of view based on the need of the clinician (Kau et al 2009):

• Dentoalveolar: < 8 cm.

• Maxillo-mandibular: 8-15 cm.

• Skeletal: 15-21 cm.

• Head and neck: > 21 cm.

An extended FOV scanning incorporating the craniofacial region is difficult to incorporate into cone-beam design because of the high cost of large-area detectors. The expansion of scan volume height has been accomplished by the software addition of two rotational scans to produce a single volume with a 22-cm height. Another method for increasing the width of the FOV while using a smaller area detector, keeping manufacturing costs low, is to offset the position of the detector, collimate the beam asymmetrically, and scan only half the patient FIG XX (Scarfe & Farman, 2008).


During the scan, single exposures are made at certain degree intervals, providing individual 2D projection images, known as ''basis,'' ''frame,'' or ''raw'' images. These images are similar to lateral and posterior-anterior cephalometric radiographic images, each slightly offset from one another. The complete series of images is referred to as the ''projection data''.

The number of images comprising the projection data throughout the scan is determined by:

- the frame rate (number of images acquired per second),

- the speed of the rotation.

- the completeness of the trajectory arc, and

The number of projection scans comprising a single scan may be fixed or variable, depending on the manufacturer. More projection data provide more information to reconstruct the image, wich allows greater spatial and contrast resolution, producing ''smoother'' images, and reduction metallic artifacts but increases the signal-to-noise ratio. On the other hand, more projection data usually necessitate a longer scan time, a higher patient dose, and longer primary reconstruction time. In accordance with the ''as low as reasonably achievable'' (ALARA) principle, the number of basis images should be minimized to produce an image of diagnostic quality (Scarfe & Farman, 2008)

Frame rate and speed of rotation:

Higher frame rates provide images with fewer artifacts and better image quality. However, the greater number of projections proportionately increases the amount of radiation a patient receives. Detector pixels must be sensitive enough to capture radiation adequate to register a high signal-to-noise output and to transmit the voltage to the analog and the digital converter, all within a short arc of exposure (Scarfe & Farman, 2008).

Completeness of the trajectory arc:

Most CBCT imaging systems use a complete circular trajectory or a scan arc of 360° to acquire projection data while some models can to perform partial rotations (180°-190°) (SEDENTEXCT, 2011; Scarfe & White, 2008). This physical requirement is usually necessary to produce projection data adequate for 3D reconstruction using the FDK algorithm. However, it is possible to reduce the completeness of the scanning trajectory and still reconstruct a volumetric data set. This approach potentially reduces the scan time, is mechanically easier to perform, and reduces x-ray patient dose. It has been mentioned that images produced by this method may have greater noise and suffer from reconstruction interpolation artifacts (Scarfe & White, 2008). However, it has been showed that increasing the number of projections does not influence the linear accuracy of CBCT (Brown et al, 2009).

Have been suggested that, for certain clinical applications on specific CBCT equipment, partial rotations can be used while maintaining acceptable diagnostic accuracy and image quality (Lofthag-Hansen et al, 2010; Durack et al, 2011). Recently SEDENTXCT Project has suggested that research studies should be performed to assess further the effect of the number of projections on image quality and radiation dose (SEDENTEXCT, 2011).

The four components of CBCT image production are detailed in the figure XX:process.jpg

The image generation and detection specifications of different available systems reflect proprietary variations in these parameters.


The geometric configuration and acquisition mechanics for the cone beam technique are theoretically simple. A single partial (180°) or full rotational scan (360°) from an x-ray source takes place while a reciprocating area detector moves synchronously with the scan around a fixed fulcrum within the patient's head (FIG.XX).

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Current CBCT units can be divided into two groups, based on detector type: an image intensifier tube/charge-coupled device (IIT/CCD) combination or an amorphous silicon or complementary metal-oxide semiconductor (CMOS) flat panel imager. (Araki et al, 2004; Holberg et al, 2005).

IIT/CCD combination:

Comprises an x-ray IIT coupled to a CCD by way of a fiber optic coupling.

Image intensifiers may create geometric distortions that must be addressed in the data processing software. This disadvantage could potentially reduce the measurement accuracy of CBCT units using this configuration. II/CCD systems also introduce additional artifacts (ICRP, 1990).

Flat-panel imager:

Consists of detection of X rays using an ''indirect'' detector based on a large-area solid-state sensor panel coupled to an x-ray scintillator layer.

Flat-panel detector arrays provide a greater dynamic range and greater performance than the II/CCD technology.

Flat-panel detectors do not create geometric distortions. These CBCT systems also have limitations in their performance that are related to linearity of response to the radiation spectrum, uniformity of response throughout the area of the detector, and bad pixels. The effects of these limitations on image quality are most noticeable at lower and higher exposures. To overcome this problem, detectors are linearized piecewise and exposures that cause nonuniformity are identified and calibrated. In addition, pixel-by-pixel standard deviation assessment is used in correcting nonuniformity. Bad pixels are also examined and most often replaced by the average of the neighboring pixels (Scarfe & Farman, 2008).


It is the process of basis projection frames that have already acquired, to create the volumetric data set.

The number of individual projection frames may be from 100 to more than 600, each with more than one million pixels, with 12 to 16 bits of data assigned to each pixel. The reconstruction of the data is therefore computationally complex. To facilitate data handling, data are usually acquired by one computer (acquisition computer) and transferred by way of an Ethernet connection to a processing computer (workstation). In contrast to conventional CT, cone-beam data reconstruction is performed by personal computer rather than workstation platforms (Scarfe & Farman, 2008).

Reconstruction times vary, depending on the acquisition parameters (voxel size, FOV, number of projections), hardware (processing speed, data throughput from acquisition to workstation computer), and software (reconstruction algorithms) used. Reconstruction should be accomplished in an acceptable time (less than 3 minutes for standard resolution scans) to complement patient flow (Scarfe & Farman, 2008). The reconstruction process consists of two stages, acquisition stage, followed by reconstruction stage.


Because of the spatially varying physical properties of the photodiodes and the switching elements in the flat panel, and also because of variations in the x-ray sensitivity of the scintillator layer, raw images from CBCT detectors show spatial variations of dark image offset and pixel gain. The dark image offset (ie, the detector output signal without any x-ray exposure), and its spatial variations are mainly caused by the varying dark current of the photodiodes (Scarfe & Farman, 2008).

Gain variations are caused by the varying sensitivity of the photodiodes and by variations in the local conversion efficiency of the scintillator material caused by, for example, thickness or density variations. In addition to offset and gain variations, even high-quality detectors exhibit inherent pixel imperfections or a certain amount of defect pixels. To compensate for these inhomogeneities, raw images require systematic offset and gain calibration and a correction of defect pixels. The sequence of the required calibration steps is referred to as ''detector preprocessing''(FIG XX) and the calibration requires the acquisition of additional image sequences (Scarfe & Farman, 2008).


Once images are corrected, they must be related to each other and assembled. One method involves constructing a sinogram: a composite image relating each row of each projection image (FIG XX).

The final step in the reconstruction stage is processing the corrected sinograms. A reconstruction filter algorithm is applied to the sinogram and converts it into a complete 2D CT slice. The most widely used filtered back projection algorithm for cone beam- acquired volumetric data is the FDK algorithm (Feldkamp et al, 1984). Once all the slices have been reconstructed, they can be recombined into a single volume for visualization (FIG.XX).

FIG XX: Reconstruction Stage


The availability of CBCT technology provides the dental clinician with a great choice of image display formats. The volumetric data set is a compilation of all available voxels and, for most CBCT devices, it is presented to the clinician on screen as secondary reconstructed images in three orthogonal planes (axial, sagittal, and coronal)(FIG). (Scarfe & Farman, 2008).


FIG. XX. Axial, sagittal and coronal planes. Sky View. My Ray. Imola, Italy.