MRI for Dental Applications

Advances in digital radiography for both intraoral and panoramic imaging and cone-beam computed tomography have led the way to an increase in diagnostic capabilities for the dental care profession. In this article, the authors provide information on 4 emerging technologies with promise.

The authors feature the following: artificial intelligence in the form of deep learning using convolutional neural networks, dental magnetic resonance imaging, stationary intraoral tomosynthesis, and second-generation cone-beam computed tomography sources based on carbon nanotube technology and multispectral imaging. The authors review and summarize articles featuring these technologies.

The history and background of these emerging technologies are previewed along with their development and potential impact on the practice of dental diagnostic imaging. The authors conclude that these emerging technologies have the potential to have a substantial influence on the practice of dentistry as these systems mature. The degree of influence most likely will vary, with artificial intelligence being the most influential of the 4.

The readers are informed about these emerging technologies and the potential effects on their practice going forward, giving them information on which to base decisions on adopting 1 or more of these technologies. The 4 technologies reviewed in this article have the potential to improve imaging diagnostics in dentistry thereby leading to better patient care and heightened professional satisfaction.

Bohner L, Hanisch M, Sesma N, Blanck-Lubarsch M, Kleinheinz J. Artifacts in magnetic resonance imaging caused by dental materials: a systematic review. Dentomaxillofac Radiol (2022) 10.1259/dmfr.20210450.

Non-profit, Foundations: Open access funding enabled by Projekt DEAL, Germany.

Systematic review.

This proof-of-principle study aimed to demonstrate that magnetic resonance imaging (MRI) is sufficiently accurate for the detection of root canals using guided endodontics.

One hundred extracted human teeth (anterior and premolar) were mounted onto 5 mandibular and 5 maxillary models, fitted with splints designed to accommodate a thin layer of aqueous gel for indirect imaging, and scanned by MRI. After MRI and intraoral scans were aligned using planning software, access cavities were planned virtually, and templates were manufactured with computer-aided design/computer-aided manufacturing, the access cavities were prepared. Cone-beam computed tomographic scans were performed and matched with the virtual preoperative planning data to determine the accuracy of access cavity preparation in terms of deviation between planned and prepared cavities in the mesiodistal and buccolingual dimensions and angle. Descriptive statistical analysis was performed, and the mean values were compared using the t test.

Ninety-one of 100 root canals were successfully scouted after MRI-guided access cavity preparation. The mean angle deviation was 1.82°. The mean deviation ranged from 0.21–0.31 mm at the base of the bur and from 0.28–0.44 mm at the tip of the bur. Preparation in the buccolingual dimension was significantly more precise in mandibular compared with maxillary teeth, and accuracy in the mesiodistal dimension was more precise in anterior teeth compared with premolars.

This in vitro study demonstrated the suitability of MRI for guided endodontic access cavity preparation.

Magnetic resonance imaging (MRI) is a radiographic technique without any ionizing radiation and has huge potential in dentistry with multiple applications. Since there is no specialized MRI machine dedicated to dentistry, dental hospitals, and clinics have to be selective in order to get the best device for their needs. Unless a decision-making system is used, evaluating and comparing every single criteria of an MRI device will cause loss of time for the physicians and hospital material administrators. Moreover, it is possible that the evaluation of human beings will not be as precise as the algorithm's evaluation. This chapter discusses the use of fuzzy PROMETHEE, a decision-making algorithm, for the comparison of MRI devices with multiple criteria.