The advantage of cone-beam computerized tomography over panoramic radiography and temporomandibular joint quadruple radiography in assessing temporomandibular joint osseous degenerative changes

Tsai, Chih-Mong; Wu, Feitong; Chai, Jyh‐Wen; Chen, Mu-Hsiung; Kao, Chih-Ting

doi:10.1016/j.jds.2020.03.004

Cited by 14 publications

(26 citation statements)

References 27 publications

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“…Our previous study demonstrated fair to good inter-examiner agreement and reliability and acceptable sensitivity and specificity when the 2 examiners used CBCT to identify TMJ osseous degenerative features. 21 …”

Section: Methodsmentioning

confidence: 99%

“… 16 , 17 , 18 Cone-beam computerized tomography (CBCT) can reveal more detailed changes in the TMJ bony structures than conventional radiographic modalities, which represents a unique advantage for TMJ DJD diagnosis. 19 , 20 , 21 CBCT images reveal the precise location and extent of osseous degenerative changes in the mandibular and temporal components of the TMJ. 22 , 23 The usefulness of CBCT is extensively discussed in the literature, 22 , 24 and the use of CBCT for reliable diagnosis of pathologies in osseous structures is highly recommended.…”

Section: Introductionmentioning

confidence: 99%

“… 22 , 23 The usefulness of CBCT is extensively discussed in the literature, 22 , 24 and the use of CBCT for reliable diagnosis of pathologies in osseous structures is highly recommended. 16 , 21 …”

Section: Introductionmentioning

confidence: 99%

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Differences between the temporal and mandibular components of the temporomandibular joint in topographic distribution of osseous degenerative features on cone-beam computerized tomography

Tsai

Chai

et al. 2021

Journal of Dental Sciences

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Background/purpose Temporomandibular joint osteoarthritis (TMJOA) pathology is characterized by degenerative changes of the subchondral bone. The topographic distribution of osseous degenerative changes in TMJ is not clear. This study aimed to evaluate the topographic distribution of osseous degenerative features in the TMJ by using cone-beam computerized tomography (CBCT). Materials and methods The CBCT images of 26 female patients diagnosed to have TMJOA were retrieved from the database of the National Taiwan University Hospital. The images of left and right TMJs were evaluated independently by 2 examiners. The evaluated degenerative features included surface erosion, subcortical cysts, subcortical sclerosis, and osteophytes in the mandibular condyle and temporal component of the TMJ. The topographic distribution at different portions in the mandibular condyle and temporal component of the TMJ was statistically analyzed. Results Significant differences in the topographic distribution of the osseous degenerative features were observed (a) between the mandibular condyle and the temporal component and (b) between the anterior/central portion and posterior portion of the temporal component. No significant differences were observed in the topographic distribution of the TMJOA features in the condyle, except for surface erosion between the central and lateral portion of the condyle. Conclusion The results suggest that the mandibular condyle and temporal component react differently in TMJ osseous degeneration, with the condyle being more vulnerable than the temporal component. Mandibular activities that require the mandibular condyle to function outside the fossa may be more destructive to the health and integrity of the TMJ.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Differences between the temporal and mandibular components of the temporomandibular joint in topographic distribution of osseous degenerative features on cone-beam computerized tomography

Tsai

Chai

et al. 2021

Journal of Dental Sciences

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“…Such discrepancies may be derived from various factors including differences in the the definition of TMJ DJD and imaging modality on which such diagnoses were made. Studies show that the diagnosis of TMJ DJD based on plain radiography may result in poor reliability with low sensitivity and specificity due to superimposition of surrounding structures and low resolution of bony margins; hence, the application of CBCT is recommended for indeterminate cases 28 . Based on such descriptions, CBCT results were applied in patient grouping for our study and the diagnosis of TMJ DJD was done following standardised procedures established for research by well‐calibrated specialists which is also reflected in the high interclass correlation coefficient, allowing the results to be generalisable.…”

Section: Discussionmentioning

confidence: 99%

Limited implication of initial bone scintigraphy on long‐term condylar bone change in temporomandibular disorders—Comparison with cone beam computed tomography at 1 year

Bae

Gil

et al. 2021

J of Oral Rehabilitation

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Background The current diagnostic criteria for temporomandibular disorders (TMD) do not require imaging for the diagnosis of degenerative joint disease (DJD) of the temporomandibular joint (TMJ) condyle, and there is a lack of data investigating the effectiveness of imaging modalities in predicting long‐term TMJ DJD prognosis. Objectives To verify the association between initial bone scintigraphy results and long‐term DJD bone changes occurring in the TMJ condyle on cone beam computed tomography (CBCT). Methods Initial bone scintigraphy, panoramic radiography and CBCT results were analysed in relation to long‐term (12 months) TMJ DJD bone change on CBCTs in 55 TMD patients (110 joints). Clinical and radiographic indices were statistically analysed among three groups (improved, no change, and worsened) based on long‐term TMJ DJD prognosis calculated by destructive change index (DCI). Results Neither the uptake ratio nor visual assessment results from initial bone scintigraphy showed a significant difference according to long‐term condylar bone change groups. The cut‐off value of bone scintigraphy uptake ratio was 2.53 for long‐term worsening of TMJ DJD. Worsening of TMJ DJD was significantly associated with the diagnosis based on panoramic radiography (p = .011) and CBCT (p < .001). Initial DCI (β = −.291, p = .046) had a significant association with long‐term worsening of TMJ DJD. Conclusion Initial bone scintigraphy results did not show sufficiently close associations with long‐term TMJ DJD prognosis. This should be considered in the selection process of imaging modalities for TMJ DJD patients. Future studies are needed to develop prognostic indices that comprise both clinical and imaging contents for improved predictive ability.

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“…Cone-beam computer tomography (CBCT) has become one of the important imaging methods for oral and maxillofacial disease diagnosis because of the advantages of high resolution, low radiation dose, and reliable twodimensional (2D) and three-dimensional (3D) anatomical structure information. [7][8][9] A popular IAC segmentation method based on CBCT is that the MF and mandibular foramen are segmented layer-by-layer in sagittal images. Using matching technology and threshold method to locate the small holes and determine the seed points could improve the segmentation accuracy of the level set method.…”

Section: Introductionmentioning

confidence: 99%

Inferior alveolar canal segmentation based on cone‐beam computed tomography

Wei

Wang

2021

Medical Physics

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Purpose The shape and position of the inferior alveolar canal (IAC) are analyzed to effectively reduce the risk of iatrogenic injury based on cone‐beam computer tomography (CBCT). To assist dental clinicians to make better use of the IAC information, we propose an IAC segmentation method based on CBCT images. Methods In this paper, CBCT images are first preprocessed by the Hounsfield unit values clipping and gray normalization. Secondly, based on the multi‐plane reconstruction (MPR) and curved surface reconstruction, the curved MPR image sets are generated by the smooth dental arch curve with a sampling distance of 1.00 pixels. Then, the K‐means clustering algorithm is used to cluster the texture parameters of the gray level‐gradient co‐occurrence matrix enhanced by the gradient directions to improve the image contrast of the IAC. Finally, the IAC edges are roughly segmented by the 2D line‐tracking method, and smoothed by the fourth‐order polynomial to obtain the final segmentation result. Results Twenty‐one real clinical dental CBCT datasets were used to test the proposed method. The manual segmentation results of two specialized dental clinicians were used as quantitative evaluation criteria. The dice similarity index (DSI), average symmetric surface distance (ASSD), and mean curve distance (MCD) of the left IAC are 0.93 (SD = 0.01), 0.16 mm (SD = 0.05 mm), and 1.59 mm (SD = 0.25 mm), respectively; the DSI, ASSD, and MCD of the right IAC are 0.93 (SD = 0.02), 0.16 mm (SD = 0.05 mm), and 1.60 mm (SD = 0.30 mm), respectively. Conclusions The proposed method provides an effective image enhancement and segmentation solution to analyze the shape and position of the IAC. Experimental results show that the relationships between the IAC and other structures can be accurately reflected in the panoramic images without superimposition and geometric distortion, and the smooth edges of the IAC can be segmented.

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The advantage of cone-beam computerized tomography over panoramic radiography and temporomandibular joint quadruple radiography in assessing temporomandibular joint osseous degenerative changes

Cited by 14 publications

References 27 publications

Differences between the temporal and mandibular components of the temporomandibular joint in topographic distribution of osseous degenerative features on cone-beam computerized tomography

Differences between the temporal and mandibular components of the temporomandibular joint in topographic distribution of osseous degenerative features on cone-beam computerized tomography

Limited implication of initial bone scintigraphy on long‐term condylar bone change in temporomandibular disorders—Comparison with cone beam computed tomography at 1 year

Inferior alveolar canal segmentation based on cone‐beam computed tomography

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