MR imaging of the temporomandibular joint: comparison between acquisitions at 7.0 T using dielectric pads and 3.0 T

Kuhn, Félix P.; Spinner, Georg; Grande, Filippo Del; Wyss, Michael; Piccirelli, Marco; Erni, Stefan; Pfister, Pascal; Ho, Michael; Sah, Bert Ram; Filli, Lukas; Ettlin, Dominik A; Gallo, Luigi M.; Andreisek, Gustav; Manoliu, Andrei

doi:10.1259/dmfr.20160280

Cited by 12 publications

(8 citation statements)

References 40 publications

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“…With the increase of the contrast-to-noise ratio (CNR) and SNR, the quality of MR images improves [39]. Kuhn et al [40] analyzed imaging at 7.0 T vs. 3.0 T and found that although both methods had similar SNR, the visibility of articular disc was significantly better at 7.0 T imaging. Furthermore, Sun et al [39] compared two different types of coils in TMJ MR imaging: a 15-channel phased array head coil and 6-channel dS Flex M surface coil.…”

Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Interdisciplinary Approach to the Temporomandibular Joint Osteoarthritis—Review of the Literature

2020

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Background and objectives: There is an increasing number of patients applying for dental treatment who suffer from temporomandibular joint osteoarthritis (TMJOA). Osteoarthritis may be the cause of the pain in the area of temporomandibular joints, but its course may also be absolutely asymptomatic. The aim of this study was to present an interdisciplinary approach to TMJOA, including current diagnostics and treatment modalities on the basis of the available literature. Materials and Methods: PubMed and Scopus databases were analyzed using the keywords: ((temporomandibular joint AND osteoarthritis) AND imaging) and ((temporomandibular joint AND osteoarthritis) AND treatment). The bibliography was supplemented with books related to the temporomandibular joint. After screening 2450 results, the work was based in total on 98 publications. Results and Conclusions: Osteoarthritis is an inflammatory, age-related, chronic and progressive degenerative joint disease. Magnetic resonance imaging (MRI) and cone-beam computed tomography (CBCT), together with clinical symptoms, play significant roles in TMJOA diagnosis. Current MRI techniques seem to be clinically useful for assessment of bony changes in temporomandibular joint (TMJ) disorders. Treatment of TMJOA requires a complex, interdisciplinary approach. TMJOA treatment includes the cooperation of physiotherapists, rheumatologists, gnathologists, orthodontists and quite often also maxillofacial surgeons and prosthodontists. Sometimes additional pharmacotherapy is indicated. Thorough examination of TMJ function and morphology is necessary at the beginning of any orthodontic or dental treatment. Undiagnosed TMJ dysfunction may cause further problems with the entire masticatory system, including joints, muscles and teeth.

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Section: Magnetic Resonance Imaging (Mri)mentioning

confidence: 99%

Interdisciplinary Approach to the Temporomandibular Joint Osteoarthritis—Review of the Literature

2020

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“…In addition to this, MRI interpretations are still falling short of showing a clear association with reported symptoms [ 7 ]. Furthermore, the impact of potential imaging findings on treatment choice and clinical outcome is still controversial, suggesting that the depiction of TMJ in clinical routine is still unsatisfactory and may benefit from further optimization [ 8 ]. Moreover, it was also indicated that, often, there is still an unclear correlation between clinical signs and symptoms and imaging findings in all TMD patient groups [ 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies

Orhan

Driesen

Shujaat

et al. 2021

BioMed Research International

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The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance (MR) images. This retrospective cohort study included 214 TMJs from 107 patients with TMJ signs and symptoms. A radiomics platform was used to extract (Huiying Medical Technology Co., Ltd., China) imaging features of TMJ pathologies, condylar bone changes, and disc displacements. Thereafter, different machine learning (ML) algorithms and logistic regression were implemented on radiomic features for feature selection, classification, and prediction. The following radiomic features included first-order statistics, shape, texture, gray-level cooccurrence matrix (GLCM), gray-level run length matrix (GLRLM), and gray-level size zone matrix (GLSZM). Six classifiers, including logistic regression (LR), random forest (RF), decision tree (DT), k -nearest neighbors (KNN), XGBoost, and support vector machine (SVM) were used for model building which could predict the TMJ pathologies. The performance of models was evaluated by sensitivity, specificity, and ROC curve. KNN and RF classifiers were found to be the most optimal machine learning model for the prediction of TMJ pathologies. The AUC, sensitivity, and specificity for the training set were 0.89 and 1, while those for the testing set were 0.77 and 0.74, respectively, for condylar changes and disc displacement, respectively. For TMJ condylar bone changes Large-Area High-Gray-Level Emphasis, Gray-Level Nonuniformity, Long-Run Emphasis Long-Run High-Gray-Level Emphasis, Flatness, and Volume features, while for TMJ disc displacements Average Intensity, Sum Average, Spherical Disproportion, and Entropy features, were selected. This study has proposed a machine learning model by KNN and RF analysis on TMJ MR images, which can be used to classify condylar changes and TMJ disc displacements.

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“…RF coils can be divided into orthogonal, phased array, surface flexible, intraoperative, and array types. 25 The selection and reasonable application of the RF coil are the most important components. The phased array coils are composed of multiple single coils, which can provide more detailed MRI information for the inspected position.…”

Section: Discussionmentioning

confidence: 99%

“…The soft surface coil is used to cover a limited area of the body during MRI, which can use the structure’s characteristics to bring the coil as close to the detected position as possible to improve image quality. 16,24 In our study, the head coil that consists of a basilar coil and its upper coil is a phased array coil 25,26 ; it is equipped with a supporting platform with a built-in rear coil for the head examination (30 cm, 15 channels). The dS Flex surface coil, which is mainly used for imaging small joints and limited regions, is a loop coil with a pedestal on the bottom and an annular integrated flex coil.…”

Section: Discussionmentioning

confidence: 99%

Selection and application of coils in temporomandibular joint MRI

Sun

Dong

Tao

et al. 2020

Dentomaxillofacial Radiology

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Objective: To compare and evaluate the signal-to-noise ratio (SNR) and the contrast-to-noise ratio (CNR) values between a 15-channel phased array head coil and 6-channel dS Flex M surface coil in the MRI of temporomandibular joint. Methods: 300 patients were randomly assigned to two groups: 150 patients were examined by using a 15-channel phased array head coil and the other 150 patients were scanned by using a 6-channel dS Flex M surface coil. All of the data were set in the same 6 regions of interest including the temporal lobe, condyle neck, lateral pterygoid muscle, parotid gland, the adipose area and an area of the background noise）. SNR and CNR values were measured respectively. Results: The numerical variation law of SNR and CNR values measured in regionsof interest of each group was similar, although different coils were used. There were statistically significant differences of SNR values in all of the oblique sagittal (OSag) proton density-weighted imaging, the part of OSag T 2 weighted image (T 2WI) except for SNR4 and SNR5. and oblique coronal (OCor) T 2WI sequence except for SNR2. On the contrary, SNR4 and SNR5 values in the OCor T 2WI and SNR5 values in OSag T 2WI sequences by using the surface coil were higher than those by using the head coil. There were no statistically significant intergroup differences of CNR values in OSag proton density-weighted imaging sequence except CNR1 and in OSag T 2WI sequence except CNR5. But, statistically significant differences of all the values in the OCor T 2WI sequence except for CNR1 were observed. Conclusion: Both the phased array head coil and dS Flex M surface coil can be used for temporomandibular joint MRI.

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MR imaging of the temporomandibular joint: comparison between acquisitions at 7.0 T using dielectric pads and 3.0 T

Cited by 12 publications

References 40 publications

Interdisciplinary Approach to the Temporomandibular Joint Osteoarthritis—Review of the Literature

Interdisciplinary Approach to the Temporomandibular Joint Osteoarthritis—Review of the Literature

Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies

Selection and application of coils in temporomandibular joint MRI

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