The evolving role of MRI in dentomaxillofacial diagnostics: a comprehensive review

Abstract

Magnetic Resonance Imaging (MRI) has emerged as a pivotal diagnostic tool in dentomaxillofacial radiology, surpassing conventional imaging techniques by offering superior contrast resolution for soft tissue lesions without the use of ionizing radiation. This comprehensive review explores the expanding applications of MRI in dentistry, highlighting its integration into routine diagnostic protocols and its significance in the evaluation of oral and maxillofacial structures. The article delves into the physics of MRI, detailing the various sequences such as Spin Echo (SE), Gradient Echo (GRE), and Short-Tau Inversion Recovery (STIR), each tailored for specific diagnostic needs. Advanced techniques like Dynamic Contrast-Enhanced MRI and Diffusion-Weighted Imaging (DWI) are discussed for their roles in assessing tissue perfusion and differentiating between benign and malignant lesions. The review emphasizes the necessity of appropriate coil selection and parameter optimization to enhance image quality, particularly in dental applications where artifacts from restorative and prosthetic materials can pose challenges. Furthermore, the article addresses the utility of MRI in visualizing dental hard tissues, the temporomandibular joint, and neurovascular structures, providing a comprehensive overview of its diagnostic capabilities. The integration of MRI into global health systems and the role of Personal Electronic Health Records in reducing redundant imaging are also examined. Conclusively, the review underscores the transformative impact of MRI on dentomaxillofacial diagnostics, advocating for its broader adoption in clinical practice to facilitate accurate diagnosis and effective treatment planning.

Keywords

• Dental imaging
• magnetic resonance imaging
• radiology
• dentomaxillofacial diagnostics

References

1. Lurie AG. Doses, Benefits, Safety, and Risks in Oral and Maxillofacial Diagnostic Imaging. Health Phys 2019;116:163-9. doi: 10.1097/HP.0000000000001030. google scholar
2. Ekprachayakoon I, Miyamoto JJ, Inoue-Arai MS, Honda EI, Takada JI, Kurabayashi T, Moriyama K. New application of dynamic magnetic resonance imaging for the assessment of deglutitive tongue movement. Prog Orthod 2018;19:45. doi: 10.1186/s40510-018-0245-x. google scholar
3. Mendes S, Rinne CA, Schmidt JC, Dagassan-Berndt D, Walter C. Evaluation of magnetic resonance imaging for diagnostic purposes in operative dentistry-a systematic review. Clin Oral Investig 2020;24:547-57. doi: 10.1007/s00784-019-03103-8. google scholar
4. Xu J, Wang D, Yang C, Wang F, Wang M. Reconstructed magnetic resonance image-based effusion volume assessment for temporomandibular joint arthralgia. J Oral Rehabil 2023;50:1202-10. doi: 10.1111/joor.13551. google scholar
5. Pykett IL, Newhouse JH, Buonanno FS, Brady TJ, Goldman MR, Kistler JP, Pohost GM. Principles of nuclear magnetic resonance imaging. Radiology 1982;143:157-68. doi: 10.1148/radiology.143.1.7038763. google scholar
6. Lam EW, Hannam AG, Wood WW, Fache JS, Watanabe M. Imaging orofacial tissues by magnetic resonance. Oral Surg Oral Med Oral Pathol 1989;68:2-8. doi: 10.1016/0030-4220(89)90106-0. google scholar
7. Hisatomi M, Asaumi J, Konouchi H, Shigehara H, Yanagi Y, Kishi K. MR imaging of epithelial cysts of the oral and maxillofacial region. Eur J Radiol 2003;48:178-82. doi: 10.1016/S0720-048X(02)00218-8. google scholar
8. Terra GT, Oliveira JX, Hernandez A, Lourenço SV, Arita ES, Cortes AR. Diffusion-weighted MRI for differentiation between sialadenitis and pleomorphic adenoma. Dentomaxillofac Radiol 2017;46:20160257. doi: 10.1259/dmfr.20160257. google scholar

9. Al-Haj Husain A, Schönegg D, Valdec S, Stadlinger B, Piccirelli M, Winklhofer S. Appearance of nasopalatine duct cysts on dental magnetic resonance imaging using a mandibular coil: Two case reports with a literature review. Imaging Sci Dent 2023;53:161-8. doi: 10.5624/isd.20220215. google scholar
10. Burian E, Probst FA, Weidlich D, Cornelius CP, Maier L, Robl T, Zimmer C, Karampinos DC, Ritschl /LM, Probst M. MRI of the inferior alveolar nerve and lingual nerve-anatomical variation and morphometric benchmark values of nerve diameters in healthy subjects. Clin Oral Investig 2020;24:2625-34. doi: 10.1007/s00784-019-03120-7. google scholar
11. Li M, Yuan Z, Tang Z. The accuracy of magnetic resonance imaging to measure the depth of invasion in oral tongue cancer: a systematic review and meta-analysis. Int J Oral Maxillofac Surg 2022;51:431-40. doi: 10.1016/j.ijom.2021.07.010. google scholar
12. Kennerley AJ, Mitchell DA, Sebald A, Watson I. Real-time magnetic resonance imaging: mechanics of oral and facial function. Br J Oral Maxillofac Surg 2022;60:596-603. doi: 10.1016/j.bjoms.2021.10.008. google scholar
13. Al-Haj Husain A, Stadlinger B, Özcan M, Schönegg D, Winklhofer S, Al-Haj Husain N, Piccirelli M, Valdec S. Buccal bone thickness assessment for immediate anterior dental implant planning: A pilot study comparing cone-beam computed tomography and 3D double-echo steady-state MRI. Clin Implant Dent Relat Res 2023;25:35-45. doi: 10.1111/cid.13160. google scholar
14. Reda R, Zanza A, Mazzoni A, Cicconetti A, Testarelli L, Di Nardo D An Update of the Possible Applications of Magnetic Resonance Imaging (MRI) in Dentistry: A Literature Review J Imaging 2021; 7:75 doi: 10 3390/jimaging7050075 google scholar
15. Calle D, Navarro T Basic Pulse Sequences in Magnetic Resonance Imaging Methods Mol Biol 2018;1718:21-37 doi: 10 1007/978-1-4939-7531-0_2 google scholar
16. Pooley RA Fundamental physics of MR imaging RadioGraphics 2005;25,1087-99 doi:10 1148/rg 254055027 google scholar
17. Jackson EF, Ginsberg LE, Schomer DF, Leeds NE . A review of MRI pulse sequences and techniques in neuroimaging. SurgNeurol 1997;47:185-99. doi: 10 .1016/s0090-3019(96)00375-8 . google scholar
18. Berger A. Magnetic resonance imaging. BMJ 2002;5:324-35. doi: 10.1136/bmj.324.7328.35. google scholar
19. Tourais J, Coletti C, Weingartner S. Chapter 1 - Brief Introduction to MRI Physics. In: Akçakaya M, Doneva M, Prieto C, editors. Advances in Magnetic Resonance Technology and Applications. Academic Press 2022;3-36. https://doi.org/10.1016/B978-0-12-822726-8.00010-5. google scholar
20. Minami M, Kaneda T, Ozawa K, Yamamoto H, Itai Y, Ozawa M, Yoshikawa K, Sasaki Y. Cystic lesions of the maxillomandibular region: MR imaging distinction of odontogenic keratocysts and ameloblastomas from other cysts. AJR Am J Roentgenol 1996;(4),943-9. https://doi.org/10.2214/ajr.166.4.8610578 google scholar
21. Probst FA, Probst M, Pautke Ch, Kaltsi E, Otto S, Schiel S, Troeltzsch M, Ehrenfeld M, Cornelius CP, Müller-Lisse UG. Magnetic resonance imaging: a useful tool to distinguish between keratocystic odontogenic tumours and odontogenic cysts. Br J Oral Maxillofac Surg 2015;53:217-22. doi: 10.1016/j.bjoms.2014.10.014 google scholar
22. Geibel MA, Schreiber ES, Bracher AK, Hell E, Ulrici J, Sailer LK, Ozpeynirci Y, Rasche V. Assessment of apical periodontitis by MRI: A feasibility study. Rofo 2015;187:269-75. doi: 10.1055/s-0034-1385808. google scholar
23. Kramer M, Schwab SA, Nkenke E, Eller A, Kammerer F, May M, Baigger JF, Uder M, Lell M. Whole body magnetic resonance angiography and computed tomography angiography in the vascular mapping of head and neck: an intraindividual comparison. Head Face Med 2014;10:16. doi: 10.1186/1746-160X-10-16. google scholar
24. Yang G, Wilson TD, Lehman MN, Cui D. Comparison of Magnetic Resonance Angiography and Computed Tomography Angiography Stereoscopic Cerebral Vascular Models. Adv Exp Med Biol 2019;1205:1-9. doi: 10.1007/978-3-030-31904-5_1. google scholar
25. Widmann G, Henninger B, Kremser C, Jaschke W. MRI Sequences in Head & Neck Radiology - State of the Art. Rofo 2017;189:413-22. English. doi: 10.1055/s-0043-103280. google scholar
26. Timme M, Borkert J, Nagelmann N, Schmeling A. Evaluation of secondary dentin formation for forensic age assessment by means of semi-automatic segmented ultrahigh field 9.4 T UTE MRI datasets. Int J Legal Med2020;134:2283-8. doi: 10.1007/s00414-020-02425-7. google scholar
27. Grover VP, Tognarelli JM, Crossey MM, Cox IJ, Taylor-Robinson SD, McPhail MJ. Magnetic Resonance Imaging: Principles and Techniques: Lessons for Clinicians. J Clin Exp Hepatol 2015;5:246-55. doi: 10.1016/j.jceh.2015.08.001. google scholar
28. Norris CD, Quick SE, Parker JG, Koontz NA. Diffusion MR Imaging in the Head and Neck: Principles and Applications. Neuroimaging Clin N Am 2020;30:261-82. doi: 10.1016/j.nic.2020.04.001. google scholar
29. Koontz NA, Wiggins RH 3rd. Differentiation of Benign and Malignant Head and Neck Lesions With Diffusion Tensor Imaging and DWI. AJR Am J Roentgenol 2017;208:1110-15. doi: 10.2214/AJR.16.16486. google scholar
30. Bao D, Zhao Y, Wu W, Zhong H, Yuan M, Li L, Lin M, Zhao X, Luo D. Added value of histogram analysis of ADC in predicting radiation-induced temporal lobe injury of patients with nasopharyngeal carcinoma treated by intensity-modulated radiotherapy. Insights Imaging 2022;13:197. doi: 10.1186/s13244-022-01338-w. google scholar
31. Li S, Cheng J, Zhang Y, Zhang Z. Differentiation of benign and malignant lesions of the tongue by using diffusion-weighted MRI at 3.0 T. Dentomaxillofac Radiol 2015;44:20140325. doi: 10.1259/dmfr.20140325. google scholar
32. Vidiri A, Minosse S, Piludu F, Curione D, Pichi B, Spriano G, Marzi S. Feasibility study of reduced field of view diffusion-weighted magnetic resonance imaging in head and neck tumors. Acta Radiol 2017;58:292-300. doi: 10.1177/0284185116652014. google scholar
33. Baba A, Kurokawa R, Kurokawa M, Ota Y, Srinivasan A. Dynamic Contrast-Enhanced MRI Parameters and Normalized ADC Values Could Aid Differentiation of Skull Base Osteomyelitis from Nasopharyngeal Cancer. AJNR Am J Neuroradiol 2023;44:74-8. doi: 10.3174/ajnr.A7740. google scholar
34. Baba A, Kurokawa R, Kurokawa M, Srinivasan A. Dynamic contrast-enhanced MRI parameters and apparent diffusion coefficient as treatment response markers of skull base osteomyelitis: a preliminary study. Pol J Radiol 2023;88:319-24. doi: 10.5114/pjr.2023.130383. google scholar
35. Panyaping T, Tepkidakarn N, Kiatthanabumrung S, Wattanatranon D, Tritanon O. Usefulness of apparent diffusion coefficient values for distinguishing between squamous cell carcinoma and malignant salivary gland tumor of the head and neck. Neuroradiol J 2023;36:548-54. doi: 10.1177/19714009231163561. google scholar
36. Vest JR, Kaushal R, Silver MD, Hentel K, Kern LM. Health information exchange and the frequency of repeat medical imaging. Am J Manag Care 2014;20:16-24 google scholar
37. N0hr C, Parv L, Kink P, Cummings E, Almond H, N0rgaard JR, Turner P. Nationwide citizen access to their health data: analysing and comparing experiences in Denmark, Estonia and Australia. BMC Health Serv Res 2017;17:534. doi: 10.1186/s12913-017-2482-y. google scholar
38. Birinci Ş. A Digital Opportunity for Patients to Manage Their Health: Turkey National Personal Health Record System (The e-Nabız). Balkan Med J 2023;40:215-21. doi: 10.4274/balkanmedj.galenos.2023.2023-2-77. Epub 2023 Apr 28. Erratum in: Balkan Med J 2023;40:307. google scholar
39. Kreutner J, Hopfgartner A, Weber D, Boldt J, Rottner K, Richter E, Jakob PM, Haddad D. High isotropic resolution magnetic resonance imaging of the mandibular canal at 1.5 T: a comparison of gradient and spin echo sequences. Dentomaxillofac Radiol 2017;46:20160268. doi: 10.1259/dmfr.20160268. google scholar
40. Kozlov M, Kalloch B, Horner M, Bazin PL, Weiskopf N, Möller HE. Patient-Specific RF Safety Assessment in MRI: Progress in Creating Surface-Based Human Head and Shoulder Models. 2019;28. In: Makarov S, Horner M, Noetscher G, editors. Brain and Human Body Modeling: Computational Human Modeling at EMBC 2018 [Internet]. Cham (CH): Springer 2019. Chapter 13. google scholar
41. Al-Haj Husain A, Sekerci E, Schönegg D, Bosshard FA, Stadlinger B, Winklhofer S, Piccirelli M, Valdec S. Dental MRI of Oral Soft-Tissue Tumors-Optimized Use of Black Bone MRI Sequences and a 15-Channel Mandibular Coil. J Imaging 2022;8:146. doi: 10.3390/jimaging8050146. google scholar
42. Özen AC, Ilbey S, Jia F, Idiyatullin D, Garwood M, Nixdorf DR, Bock M. An improved intraoral transverse loop coil design for high-resolution dental MRI. Magn Reson Med 2023;90:1728-37. doi: 10.1002/mrm.29744. google scholar
43. Tymofiyeva O, Rottner K, Jakob PM et al. Three-dimensional localization of impacted teeth using magnetic resonance imaging. Clin Oral Investig 2010;14:169-176. google scholar
44. Gruber B, Froeling M, Leiner T, Klomp DWJ. RF coils: A practical guide for nonphysicists. J Magn Reson Imaging 2018;48:590-604. doi: 10.1002/jmri.26187. google scholar
45. Grandoch A, Peterke N, Hokamp NG, Zöller JE, Lichenstein T, Neugebauer J. 1.5 T MRI with a Dedicated Dental Signal-Amplification Coil as Noninvasive, Radiation-Free Alternative to CBCT in Presurgical Implant Planning Procedures. Int J Oral Maxillofac Implants 2021;36:1211-8. doi: 10.11607/jomi.8103. google scholar
46. Di Nardo D, Gambarini G, Capuani S, Testarelli L. Nuclear Magnetic Resonance Imaging in Endodontics: A Review. J Endod 2018;44:536-42. doi: 10.1016/j.joen.2018.01.001. google scholar
47. Demirturk Kocasarac H, Kursun-Cakmak ES, Ustaoglu G, Bayrak S, Orhan K, Noujeim M. Assessment of signal-to-noise ratio and contrast-to-noise ratio in 3 T magnetic resonance imaging in the presence of zirconium, titanium, and titanium-zirconium alloy implants. Oral Surg Oral Med Oral Pathol Oral Radiol 2020;129:80-6. doi: 10.1016/j.oooo.2019.08.020. Epub 2019 Sep 16. PMID: 31628073. google scholar
48. American Collage of Radiology 2020. https://www.acr.org/Clinical-Resources/Radiology-Safety/MR-Safety google scholar
49. 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;51:20210450. doi: 10.1259/dmfr.20210450. google scholar
50. Juerchott A, Roser CJ, Saleem MA, Nittka M, Lux CJ, Heiland S, Bendszus M, Hilgenfeld T. Diagnostic compatibility of various fixed orthodontic retainers for head/neck MRI and dental MRI. Clin Oral Investig 2023;27:2375-84. doi: 10.1007/s00784-023-04861-2. google scholar
51. Hilgenfeld T, Prager M, Heil A, Schwindling FS, Nittka M, Grodzki D, Rammelsberg P, Bendszus M, Heiland S. PETRA, MSVAT-SPACE and SEMAC sequences for metal artefact reduction in dental MR imaging. Eur Radiol 2017;27:5104-12. doi: 10.1007/s00330-017-4901-1. google scholar
52. Duttenhoefer F, Mertens ME, Vizkelety J, Gremse F, Stadelmann VA, Sauerbier S. Magnetic resonance imaging in zirconia-based dental implantology. Clin Oral Implants Res 2015;26:1195-202. doi: 10.1111/clr.12430. google scholar
53. Bender IB, Seltzer S. Roentgenographic and direct observation of experimental lesions in bone: I. 1961. J Endod 2003;29:702-6; discussion 701. doi: 10.1097/00004770-200311000-00005. google scholar
54. Bracher AK, Hofmann C, Bornstedt A, Boujraf S, Hell E, Ulrici J, Spahr A, Haller B, Rasche V. Feasibility of ultra-short echo time (UTE) magnetic resonance imaging for identification of carious lesions. Magn Reson Med 201;66:538-45. doi: 10.1002/mrm.22828. google scholar
55. Bracher AK, Hofmann C, Bornstedt A, Boujraf S, Hell E, Ulrici J, Spahr A, Haller B, Rasche V. Feasibility of ultra-short echo time (UTE) magnetic resonance imaging for identification of carious lesions. Magn Reson Med 2011;66:538-45. doi: 10.1002/mrm.22828. google scholar
56. Assaf AT, Zrnc TA, Remus CC, Khokale A, Habermann CR, Schulze D, Fiehler J, Heiland M, Sedlacik J, Friedrich RE. Early detection of pulp necrosis and dental vitality after traumatic dental injuries in children and adolescents by 3-Tesla magnetic resonance imaging. J Craniomaxillofac Surg 2015;43:1088-93. doi: 10.1016/j.jcms.2015.06.010. google scholar
57. Kress B, Buhl Y, Hahnel S, Eggers G, Sartor K, Schmitter M. Age- and tooth-related pulp cavity signal intensity changes in healthy teeth: a comparative magnetic resonance imaging analysis. Oral Surg Oral Med Oral Pathol Oral Radiol Endod 2007;103:134-7. doi: 10.1016/j.tripleo.2006.04.007. google scholar
58. Macrae PR, Jones RD, Myall DJ, Melzer TR, Huckabee ML. Cross-sectional area of the anterior belly of the digastric muscle: comparison of MRI and ultrasound measures. Dysphagia 2013;28:375-80. doi: 10.1007/s00455-012-9443-8. Epub 2013 Jan 20. Erratum in: Dysphagia 2013;28:381. google scholar
59. Otonari-Yamamoto M, Nakajima K, Tsuji Y, Otonari T, Curtin HD, Okano T, Sano T. Imaging of the mylohyoid muscle: separation of submandibular and sublingual spaces. AJR Am J Roentgenol 2010;194:431-8. doi: 10.2214/AJR.09.3516. google scholar
60. Fujii H, Fujita A, Yang A, Kanazawa H, Buch K, Sakai O, Sugimoto H. Visualization of the Peripheral Branches of the Mandibular Division of the Trigeminal Nerve on 3D Double-Echo Steady-State with Water Excitation Sequence. AJNR Am J Neuroradiol 2015;36:1333-7. doi: 10.3174/ajnr.A4288. google scholar
61. Mazza D, Di Girolamo M, Cecchetti F, Baggi L. Appearance of normal MRI anatomy of the lingual nerve using steady-state free precession sequences at 3-T. J Biol Regul Homeost Agents 2020;34:19-26. /DENTAL SUPPLEMENT. google scholar
62. Al-Haj Husain A, Solomons M, Stadlinger B, Pejicic R, Winklhofer S, Piccirelli M, Valdec S. Visualization of the Inferior Alveolar Nerve and Lingual Nerve Using MRI in Oral and Maxillofacial Surgery: A Systematic Review. Diagnostics (Basel) 2021;11:1657. doi: 10.3390/diagnostics11091657. google scholar
63. Borges A, Casselman J. Imaging the trigeminal nerve. Eur J Radiol 2010;74:323-40. doi: 10.1016/j.ejrad.2010.02.006. google scholar
64. Miloro M, Kolokythas A. Inferior alveolar and lingual nerve imaging. Atlas Oral Maxillofac Surg Clin North Am 2011;19:35-46. doi: 10.1016/j.cxom.2010.11.003. google scholar
65. Grandoch A, Oeser J, Zöller JE, GroBe Hokamp N, Lichtenstein T, Neugebauer J. Morphological Studies to Identify the Nasopalatine and Inferior Alveolar Nerve Using a Special Head and Neck MRI Coil. J Craniofac Surg 2023;34:1351-6. doi: 10.1097/SCS.0000000000009219. google scholar
66. Müller S, Khadhraoui E, Khanafer A, Psychogios M, Rohde V, Tanrikulu L. Differentiation of arterial and venous neurovascular conflicts estimates the clinical outcome after microvascular decompression in trigeminal neuralgia. BMC Neurol 2020;20:279. doi: 10.1186/s12883-020-01860-8. google scholar
67. Xiong X, Ye Z, Tang H, Wei Y, Nie L, Wei X, Liu Y, Song B. MRI of Temporomandibular Joint Disorders: Recent Advances and Future Directions. J Magn Reson Imaging 2021;54:1039-52. doi: 10.1002/jmri.27338. google scholar
68. Jeon KJ, Choi YJ, Lee C, Kim HS, Han SS. Evaluation of masticatory muscles in temporomandibular joint disorder patients using quantitative MRI fat fraction analysis-Could it be a biomarker? PLoS One 2024;19:0296769. doi: 10.1371/journal.pone.0296769. google scholar