Artefacts in cone beam CT

Jaju, Prashant; Jain, Mayuri; Singh, Ajita; Gupta, Akanksha

doi:10.4236/ojst.2013.35049

Cited by 34 publications

(31 citation statements)

References 26 publications

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“…CBCT imaging is vulnerable to motion artefacts due to long scanning times [7,21,25] (here, the CBCT took 18 seconds, while the comparative MSCT took less than 1 second). Additionally, CBCT scanners have been reported to be particularly susceptible to motion artefacts due to their high spatial resolution [55,56]. The differences in scanning times are due to the relatively slow cesium iodide scintillators in flat-panel detectors compared to the faster ceramic detectors in MSCT detectors [9,40].…”

Section: Discussionmentioning

confidence: 99%

A retrospective, semi-quantitative image quality analysis of cone beam computed tomography (CBCT) and MSCT in the diagnosis of distal radius fractures

et al. 2016

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Section: Discussionmentioning

confidence: 99%

A retrospective, semi-quantitative image quality analysis of cone beam computed tomography (CBCT) and MSCT in the diagnosis of distal radius fractures

et al. 2016

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“…Cone‐beam computed tomography (CBCT) is routinely used in clinic to provide accurate volumetric imaging of the treatment position for patient setup and adaptive therapy . However, the CT number in CBCT is not accurate enough for soft tissue‐based patient setup due to cupping and scattering artifacts caused by the large illumination field . The inaccuracy of the CT number in CBCT also prevents its further quantitative applications such as dose calculation and adaptive treatment planning.…”

Section: Introductionmentioning

confidence: 99%

“…2 However, the CT number in CBCT is not accurate enough for soft tissue-based patient setup due to cupping and scattering artifacts caused by the large illumination field. 3,4 The inaccuracy of the CT number in CBCT also prevents its further quantitative applications such as dose calculation and adaptive treatment planning. While CT numbers in CBCT can be potentially restored by deforming the planning CT (pCT) through deformable image registration (DIR), 5 imaging content change between pCT and CBCT (e.g., gas and stool change at pelvis region) as well as scatter related artifacts in CBCT interfere with accurate DIR.…”

Section: Introductionmentioning

confidence: 99%

Synthetic CT generation from CBCT images via deep learning

et al. 2020

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Purpose Cone‐beam computed tomography (CBCT) scanning is used daily or weekly (i.e., on‐treatment CBCT) for accurate patient setup in image‐guided radiotherapy. However, inaccuracy of CT numbers prevents CBCT from performing advanced tasks such as dose calculation and treatment planning. Motivated by the promising performance of deep learning in medical imaging, we propose a deep U‐net‐based approach that synthesizes CT‐like images with accurate numbers from planning CT, while keeping the same anatomical structure as on‐treatment CBCT. Methods We formulated the CT synthesis problem under a deep learning framework, where a deep U‐net architecture was used to take advantage of the anatomical structure of on‐treatment CBCT and image intensity information of planning CT. U‐net was chosen because it exploits both global and local features in the image spatial domain, matching our task to suppress global scattering artifacts and local artifacts such as noise in CBCT. To train the synthetic CT generation U‐net (sCTU‐net), we include on‐treatment CBCT and initial planning CT of 37 patients (30 for training, seven for validation) as the input. Additional replanning CT images acquired on the same day as CBCT after deformable registration are utilized as the corresponding reference. To demonstrate the effectiveness of the proposed sCTU‐net, we use another seven independent patient cases (560 slices) for testing. Results We quantitatively compared the resulting synthetic CT (sCT) with the original CBCT image using deformed same‐day pCT images as reference. The averaged accuracy measured by mean absolute error (MAE) between sCT and reference CT (rCT) on testing data is 18.98 HU, while MAE between CBCT and rCT is 44.38 HU. Conclusions The proposed sCTU‐net can synthesize CT‐quality images with accurate CT numbers from on‐treatment CBCT and planning CT. This potentially enables advanced CBCT applications for adaptive treatment planning.

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“…Metal objects lead to artifacts in CBCT images related to beam hardening because absorption of x-ray beam is greater for metal objects than human tissues and cupping, as well as streaking artifacts may occur. Cupping artifacts are seen as distortion around the metallic objects and streaking artifacts are seen as dark and bright bands between two objects with high density ( 17 ). Because of their low attenuation values, the use of composite resins has been suggested to reduce artifacts before CBCT examination ( 18 ).…”

Section: Introductionmentioning

confidence: 99%

Diagnostic Accuracy of Cone-Beam Computed Tomography in Detecting Secondary Caries Under Composite Fillings: An in Vitro Study

Keriş¹,

Demirel²,

Özdede³

et al. 2017

J Istanbul Univ Fac Dent

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Purpose:The aim of this in vitro study was to assess the diagnostic performance of cone-beam computed tomography (CBCT) in the detection of secondary carious lesions under composite resin fillings applied to different types of cavities.Materials and Methods:Occlusal cavities (O) (n=18), occlusal cavities with mesial or distal component (MO/DO) (n=30), and mesial–occlusal–distal cavities (MOD) (n=30) were prepared in seventy eight extracted human posterior teeth. In half of the cavities in each group, artificial secondary caries lesions were simulated. All cavities were restored by using composite resin. All specimens were embedded in silicone and they were positioned to have approximal contacts. CBCT imaging was done and data were evaluated two times with two week interval by two observers, using a five-point confidence scale. Intra- and inter-observer agreements were calculated with Kappa statistics (κ). The area under (Az) the receiver operating characteristic (ROC) curve was used to evaluate the diagnostic accuracy.Results:Intra- (κ =0.89) and inter-observer (κ = 0.79) agreements were found to be excellent. Az values were highest for the O restorations which is followed by the MOD and DO/MO restorations. Az values for MOD and DO/MO restorations were very low and no statistically significant difference was found. Sensitivity for DO/MO restorations and specificity for MOD restorations were found to be the lowest values.Conclusion:Diagnostic performance of CBCT was higher in O composite restorations than MOD and DO/MO restorations for secondary caries detection. The use of alternative imaging methods rather than CBCT may be useful for evaluating secondary caries under composite MOD and DO/MO restorations.

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Artefacts in cone beam CT

Cited by 34 publications

References 26 publications

A retrospective, semi-quantitative image quality analysis of cone beam computed tomography (CBCT) and MSCT in the diagnosis of distal radius fractures

A retrospective, semi-quantitative image quality analysis of cone beam computed tomography (CBCT) and MSCT in the diagnosis of distal radius fractures

Synthetic CT generation from CBCT images via deep learning

Diagnostic Accuracy of Cone-Beam Computed Tomography in Detecting Secondary Caries Under Composite Fillings: An in Vitro Study

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