Magnetic resonance imaging for preoperative diagnosis in third molar surgery: a systematic review

Magnetic resonance imaging for jawbone assessment: a systematic review

PURPOSE

To evaluate the accuracy of magnetic resonance imaging (MRI) for jawbone assessment compared to reference-standard measurements in the literature.

MATERIALS AND METHODS

An electronic database search was conducted in PubMed, EMBASE, Scopus, Web of Science, and the Cochrane Library in June 2022, and updated in August 2023. Studies evaluating the accuracy of MRI for jawbone assessment compared with reference-standard measurements (histology, physical measurements, or computed tomography) were included. The outcome measures included bone histomorphometry and linear measurements. The risk of bias was assessed by the Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS-2). The review was registered in the PROSPERO database (CRD42022342697).

RESULTS

From 63 studies selected for full-text analysis, nine manuscripts were considered eligible for this review. The studies included assessments of 54 participants, 35 cadavers, and one phantom. A linear measurement error ranging from 0.03 to 3.11 mm was shown. The accuracy of bone histomorphometry varies among studies. Limitations of the evidence included heterogeneity of MRI protocols and the methodology of the included studies.

CONCLUSION

Few studies have suggested the feasibility of MRI for jawbone assessment, as MRI provides comparable results to those of standard reference tests. However, further advancements and optimizations are needed to increase the applicability, validate the efficacy, and establish clinical utility of these methods.

A novel approach for gingiva thickness measurements around lower anterior teeth by means of dental magnetic resonance imaging

OBJECTIVE

This diagnostic accuracy study aims to present the first measurements of gingiva thickness around lower anterior teeth using dental magnetic resonance imaging (MRI) and to compare these measurements with two established methods: (1) gingival phenotype assessment via periodontal probing, and (2) the superimposition of cone-beam computed tomography (CBCT) scans with intraoral scans of teeth and gums.

MATERIALS AND METHODS

Ten patients with substantial orthodontic treatment need and anterior mandibular crowding were consecutively included in this clinical case series. After periodontal probing, each patient underwent a CBCT scan, an intraoral scan of the mandible, and an MRI investigation using a novel mandibula 15-channel dental coil.

RESULTS

The mean gingiva thickness was 0.72 mm measured on MRI and 0.97 mm measured on CBCT, with a mean difference between the measurement methods of 0.17 ± 0.27 mm (p < 0.001). Measurement agreement between the index tests (MRI and CBCT) and the clinical reference standard (probing) yielded an overall percent agreement of 64.94% and 47.02% for MRI and CBCT, respectively. Teeth with thin phenotypes were associated with lower soft tissue dimensions in both free (MRI: 0.56 mm vs. CBCT: 0.79 mm) and supracrestal gingiva (MRI: 0.75 mm vs. CBCT: 1.03 mm) when compared to those with thick phenotypes. However, only the measurements obtained from MRI scans showed statistically significant differences between the two phenotypes.

CONCLUSION

Dental MRI successfully visualizes delicate structures like the gingiva in the anterior mandible and achieves a high correlation with superimposed CBCT scans, with clinically acceptable deviations.

CLINICAL RELEVANCE

The present study helps to establish dental MRI as a radiation-free alternative to conventional radiographic methods.

MR-orthopantomography in operative dentistry and oral and maxillofacial surgery: a proof of concept study

This prospective study aimed to present, compare, and evaluate the suitability of five different magnetic resonance imaging (MRI) protocols (3D double-echo steady-state (DESS), 3D fast spin echo short-tau inversion recovery (SPACE-STIR), 3D fast spin echo spectral attenuated inversion recovery (SPACE-SPAIR), volumetric interpolated breath-hold examination (T1-VIBE-Dixon), and ultrashort echo time (UTE)) and for orthopantomogram (OPG)-like MRI reconstructions using a novel mandibular coil. Three readers assessed MR-OPGs of 21 volunteers regarding technical image quality (4, excellent; 0, severely reduced), susceptibility to artifacts (3, absence; 0, massive), and visualization of anatomical structures in the oral cavity and surrounding skeletal structures (4, fine details visible; 0, no structures visible). Average image quality was good (3.29 ± 0.83) for all MRI protocols, with UTE providing the best image quality (3.52 ± 0.62) and no to minor artifacts (2.56 ± 0.6). Full diagnostic interpretability of the osseous structures is best in VIBE-Dixon and UTE MR-OPGs. DESS provided excellent visualization of the finest details of the nervous tissue (3.95 ± 0.22). Intra-reader and inter-reader agreement between the readers was good to excellent for all protocols (ICCs 0.812-0.957). MR-OPGs provide indication-specific accurate imaging of the oral cavity and could contribute to the early detection of pathologies, staging, and radiological follow-up of oral and maxillofacial diseases.

Deep learning model for analyzing the relationship between mandibular third molar and inferior alveolar nerve in panoramic radiography

In this study, the accuracy of the positional relationship of the contact between the inferior alveolar canal and mandibular third molar was evaluated using deep learning. In contact analysis, we investigated the diagnostic performance of the presence or absence of contact between the mandibular third molar and inferior alveolar canal. We also evaluated the diagnostic performance of bone continuity diagnosed based on computed tomography as a continuity analysis. A dataset of 1279 images of mandibular third molars from digital radiographs taken at the Department of Oral and Maxillofacial Surgery at a general hospital (2014-2021) was used for the validation. The deep learning models were ResNet50 and ResNet50v2, with stochastic gradient descent and sharpness-aware minimization (SAM) as optimizers. The performance metrics were accuracy, precision, recall, specificity, F1 score, and area under the receiver operating characteristic curve (AUC). The results indicated that ResNet50v2 using SAM performed excellently in the contact and continuity analyses. The accuracy and AUC were 0.860 and 0.890 for the contact analyses and 0.766 and 0.843 for the continuity analyses. In the contact analysis, SAM and the deep learning model performed effectively. However, in the continuity analysis, none of the deep learning models demonstrated significant classification performance.

Dental MRI of Oral Soft-Tissue Tumors—Optimized Use of Black Bone MRI Sequences and a 15-Channel Mandibular Coil

Soft-tissue lesions in the oral cavity, one of the most common sites for tumors and tumor-like lesions, can be challenging to diagnose and treat due to the wide spectrum from benign indolent to invasive malignant lesions. We report an abnormally large, rapidly growing hyperplastic lesion originating from the buccal mucosa in a 28-year-old male patient. Clinical examination revealed a well-circumscribed, smooth-surfaced, pinkish nodular lesion measuring 2.3 × 2 cm, which suggested the differential diagnosis of irritation fibroma, pyogenic granuloma, oral lipoma, and other benign or malignant neoplasms such as hemangioma, non-Hodgkin’s lymphoma, or metastases to the oral cavity. Dental MRI using a 15-channel mandibular coil was performed to improve perioperative radiological and surgical management, avoiding adverse intraoperative events and misdiagnosis of vascular malformations, especially hemangiomas. Black bone MRI protocols such as STIR (short-tau inversion recovery) and DESS (double-echo steady-state) were used for high-resolution radiation-free imaging. Radiologic findings supported the suspected diagnosis of an irritation fibroma and ruled out any further head and neck lesions, therefore complete surgical resection was performed. Histology confirmed the tentative diagnosis. This article evaluates the use of this novel technique for MR diagnosis in the perioperative management of soft-tissue tumors in oral and maxillofacial surgery.