Metal and motion artifacts by cone beam computed tomography (CBCT) in dental and maxillofacial study

Nardi, Cosimo; Borri, Claudio; Regini, Francesco; Calistri, Linda; Castellani, A.; Lorini, Chiara; Colagrande, Stefano

doi:10.1007/s11547-015-0496-2

Cited by 73 publications

(78 citation statements)

References 35 publications

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“…In fact, approximately 21-42% of the in vivo examinations exhibit motion artefacts. 2,3 Recently, several works have been published in the literature about movements in dental and maxillofacial CBCT, [2][3][4][5][6][7][8][9][10] but correlation between the various possible movements and the resulting different kinds of artefacts, which may be induced, is lacking.…”

Section: Introductionmentioning

confidence: 99%

Motion artefacts in cone beam CT: anin vitrostudy about the effects on the images

Nardi¹,

Molteni²,

Lorini³

et al. 2016

BJR

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Section: Introductionmentioning

confidence: 99%

Motion artefacts in cone beam CT: anin vitrostudy about the effects on the images

Nardi¹,

Molteni²,

Lorini³

et al. 2016

BJR

Self Cite

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“…12 It is well known that patient motion artefacts influence the obtainable resolution of the final CBCT reconstructed image, rather than the resolution of the unit's flat panel detector. 12 Very few studies 13,14,16 in the literature have assessed the image quality and visual characteristics of CBCT examination for the presence of artefacts supposedly related to patient movement. These studies reported that between 4.5% and 41.5% of CBCT examinations show artefacts suggestive of patient movement.…”

Section: Discussionmentioning

confidence: 99%

“…These studies reported that between 4.5% and 41.5% of CBCT examinations show artefacts suggestive of patient movement. 13,14,16 But, although some studies 7,8 report on the possible impact of motion artefacts on image quality, knowledge of which types and magnitudes of movements may considerably hamper the diagnostic outcome of a CBCT examination is still a key question. 8 Moreover, it is unclear whether observed artefacts in some studies 13,14,16 were in fact due to patient motion.…”

Section: Discussionmentioning

confidence: 99%

“…Being a child is logically one of the main factors related to movement during radiographic examination. 13,14,17 Considering the fact that children and young adults (between 10 and 25 years) have a larger radiation effect multiplication factor than the older population, 6 one can understand why this group of patients should be further studied in the issue of patient motion artefacts. We have previously suggested that, when movement is seen during the initial seconds in the examination, which go without radiation, the operator should terminate the examination and re-instruct the patient, avoiding a reexamination.…”

Section: Discussionmentioning

confidence: 99%

“…[9][10][11][12] According to the literature, patient motion artefacts in CBCT imaging appear mainly as stripe-and ring-like patterns, double bony contours and overall unsharpness. [13][14][15] The impact of patient motion artefacts on the various diagnostic tasks performed based on CBCT imaging is yet unknown. 16,17 If patient motion artefacts are present in an image, leading to compromised image quality, a re-exposure of the patient may be necessary.…”

Section: Introductionmentioning

confidence: 99%

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Movement characteristics in young patients and the impact on CBCT image quality

Spin‐Neto¹,

Matzen²,

Schropp³

et al. 2016

Dentomaxillofacial Radiology

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Objectives: To assess patient movement characteristics in children and young adults and the impact on CBCT image quality. Methods: During 33 CBCT examinations, the patients (age: average, 14 years; range, 9-25 years) who had moved were identified by video observation [movement group (MG)]. The CBCT data sets were matched with those of 33 non-moving patients according to age, diagnostic task, examined region, field of view and voxel resolution [non-movement group (N-MG)]. Three observers scored the videos of MG, regarding motional state second by second (moving/non-moving), and movement characteristics: duration (in seconds), complexity (uniplanar or multiplanar) and distance (,3/$3 # 10/.10 mm). The observers blindly assessed axial sections of the 66 examinations individually, categorizing the image quality (appropriate/acceptable/inappropriate). Next, the observers blindly assessed axial sections of the matched-pairs images simultaneously, deciding which image in the pair had the highest image quality or if it was impossible to decide. The relationship between image quality and movement/movement characteristics was evaluated. Results: When the 66 CBCT images were evaluated individually, no relationship between image quality and movement was found. However, based on the matched-pairs assessment, accumulated number (#2 vs $3, p 5 0.039), duration (#5 s vs $6 s, p 5 0.024) and complexity (uniplanar vs multiplanar, p 5 0.046) of movements had an impact on image quality; the more severe the movement, the more often the image quality was assessed lower in the MG. Conclusions: Axial CBCT images of young patients who moved during examination did not always present lower quality than images originating from non-moving patients. Image quality was, however, significantly lower in the moving patients when movement occurred several times, had a long duration or was multiplanar.

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Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer

et al. 2021

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Purpose: Radiomic features of cone-beam CT (CBCT) images have potential as biomarkers to predict treatment response and prognosis for patients of prostate cancer. Previous studies of radiomic feature analysis for prostate cancer were assessed in a variety of imaging modalities, including MRI, PET, and CT, but usually limited to a pretreatment setting. However, CBCT images may provide an opportunity to capture early morphological changes to the tumor during treatment that could lead to timely treatment adaptation. This work investigated the quality of CBCT-based radiomic features and their relationship with reconstruction methods applied to the CBCT projections and the preprocessing methods used in feature extraction. Moreover, CBCT features were correlated with planning CT (pCT) features to further assess the viability of CBCT radiomic features. Methods: The quality of 42 CBCT-based radiomic features was assessed according to their repeatability and reproducibility. Repeatability was quantified by correlating radiomic features between 20 CBCT scans that also had repeated scans within 15 minutes. Reproducibility was quantified by correlating radiomic features between the planning CT (pCT) and the first fraction CBCT for 20 patients. Concordance correlation coefficients (CCC) of radiomic features were used to estimate the repeatability and reproducibility of radiomic features. The same patient dataset was assessed using different reconstruction methods applied to the CBCT projections. CBCT images were generated using 18 reconstruction methods using iterative (iCBCT) and standard (sCBCT) reconstructions, three convolution filters, and five noise suppression filters. Eighteen preprocessing settings were also considered. Results: Overall, CBCT radiomic features were more repeatable than reproducible. Five radiomic features are repeatable in > 97% of the reconstruction and preprocessing methods, and come from the gray-level size zone matrix (GLSZM), neighborhood gray-tone difference matrix (NGTDM), and gray-level-run length matrix (GLRLM) radiomic feature classes. These radiomic features were reproducible in > 9.8% of the reconstruction and preprocessing methods. Noise suppression and convolution filter smoothing increased radiomic features repeatability, but decreased reproducibility. The top-repeatable iCBCT method (iCBCT-Sharp-VeryHigh) is more repeatable than the top-repeatable sCBCT method (sCBCT-Smooth) in 64% of the radiomic features. Conclusion: Methods for reconstruction and preprocessing that improve CBCT radiomic feature repeatability often decrease reproducibility. The best approach may be to use methods that strike a balance repeatability and reproducibility such as iCBCT-Sharp-VeryLow-1-Lloyd-256 that has 17 repeatable and eight reproducible radiomic features. Previous radiomic studies that only used pCT radiomic features have generated prognostic models of prostate cancer outcome. Since our study indicates that CBCT radiomic features correlated well with a subset of pCT radiomic features, one may expect ...

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Metal and motion artifacts by cone beam computed tomography (CBCT) in dental and maxillofacial study

Abstract: In our series Cone-Beam-CT diagnostic evaluation was never inhibited by metal artifacts and only in 1.9% of the cases by motion artifacts, always with a very low CT-dose-index.

Cited by 73 publications

References 35 publications

Motion artefacts in cone beam CT: anin vitrostudy about the effects on the images

Motion artefacts in cone beam CT: anin vitrostudy about the effects on the images

Movement characteristics in young patients and the impact on CBCT image quality

Repeatability of CBCT radiomic features and their correlation with CT radiomic features for prostate cancer

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