Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images
Abstract
1. Introduction
2. Materials and Methods
2.1. Subjects
2.2. MRI
2.3. Image Analysis
2.4. Image Processing
2.5. Texture Analysis
- Null hypothesis: it is not possible to differentiate between texture parameter(s) of images of the articular disc extracted from MRI slices of patients with effusion and those without it.
- Alternative hypothesis: it is possible to differentiate between texture parameter(s) of images of the articular disc extracted from the MRI slices of patients with effusion and those without it.
2.6. Statistical Analysis
3. Results
- Average between all directions for each of the parameters;
- Direction S(1,0).
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
- Gaa, U.; Hüls, A. Biophysical properties of the articular disc tissue and their functional evaluation. Dtsch. Zahnarztl. Z. 1989, 44, S75–S78. [Google Scholar]
- Ito, S.; Mine, Y.; Yoshimi, Y.; Takeda, S.; Tanaka, A.; Onishi, A.; Peng, T.-Y.; Nakamoto, T.; Nagasaki, T.; Kakimoto, N. Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning. Sci. Rep. 2022, 12, 221. [Google Scholar] [CrossRef]
- Behzadi, F.; Mandell, J.C.; Smith, S.E.; Guenette, J.P. Temporomandibular joint imaging: Current clinical applications, biochemical comparison with the intervertebral disc and knee meniscus, and opportunities for advancement. Skelet. Radiol. 2020, 49, 1183–1193. [Google Scholar] [CrossRef]
- Tanaka, E.; Detamore, M.; Mercuri, L. Degenerative disorders of the temporomandibular joint: Etiology, diagnosis, and treatment. J. Dent. Res. 2008, 87, 296–307. [Google Scholar] [CrossRef]
- Detamore, M.S.; Athanasiou, K.A. Structure and function of the temporomandibular joint disc: Implications for tissue engineering. J. Oral Maxillofac. Surg. 2003, 61, 494–506. [Google Scholar] [CrossRef]
- Melo, V.; Monteiro, L.; Orge, C.; Sales, M.; Melo, J.; Rodrigues, B.; Melo, A. Prevalence of temporomandibular disorders in the Brazilian population: A systematic review and meta-analysis. CRANIO® 2023, 1–8, online ahead of print. [Google Scholar] [CrossRef]
- Roh, H.-S.; Kim, W.; Kim, Y.-K.; Lee, J.-Y. Relationships between disk displacement, joint effusion, and degenerative changes of the TMJ in TMD patients based on MRI findings. J. Cranio-Maxillofac. Surg. 2012, 40, 283–286. [Google Scholar] [CrossRef]
- Westesson, P.-L. Reliability and validity of imaging diagnosis of temporomandibular joint disorder. Adv. Dent. Res. 1993, 7, 137–151. [Google Scholar] [CrossRef]
- Mizuhashi, F.; Ogura, I.; Mizuhashi, R.; Watarai, Y.; Oohashi, M.; Suzuki, T.; Saegusa, H. Examination for the Factors Involving to Joint Effusion in Patients with Temporomandibular Disorders Using Magnetic Resonance Imaging. J. Imaging 2023, 9, 101. [Google Scholar] [CrossRef]
- Jacqmot, O.; Van Thielen, B.; Hespel, A.M.; Luijten, P.R.; de Mey, J.; Van Binst, A.; Provyn, S.; Tresignie, J. T2-weighted turbo spin-echo magnetic resonance imaging of canine brain anatomy at 1. 5T, 3T, and 7T field strengths. Anat. Rec. 2022, 305, 222–233. [Google Scholar] [CrossRef]
- Obusez, E.C.; Lowe, M.; Oh, S.-H.; Wang, I.; Bullen, J.; Ruggieri, P.; Hill, V.; Lockwood, D.; Emch, T.; Moon, D. 7T MR of intracranial pathology: Preliminary observations and comparisons to 3T and 1.5 T. Neuroimage 2018, 168, 459–476. [Google Scholar] [CrossRef]
- Platt, T.; Ladd, M.E.; Paech, D. 7 Tesla and beyond: Advanced methods and clinical applications in magnetic resonance imaging. Investig. Radiol. 2021, 56, 705. [Google Scholar] [CrossRef] [PubMed]
- Feinberg, D.A.; Beckett, A.J.; Vu, A.T.; Stockmann, J.; Huber, L.; Ma, S.; Ahn, S.; Setsompop, K.; Cao, X.; Park, S. Next-generation MRI scanner designed for ultra-high-resolution human brain imaging at 7 Tesla. Nat. Methods 2023, 20, 2048–2057. [Google Scholar] [CrossRef] [PubMed]
- Whyte, A.; Boeddinghaus, R.; Bartley, A.; Vijeyaendra, R. Imaging of the temporomandibular joint. Clin. Radiol. 2021, 76, 76.e21–76.e35. [Google Scholar] [CrossRef] [PubMed]
- Westesson, P.; Brooks, S. Temporomandibular joint: Relationship between MR evidence of effusion and the presence of pain and disk displacement. AJR Am. J. Roentgenol. 1992, 159, 559–563. [Google Scholar] [CrossRef] [PubMed]
- Farook, T.H.; Dudley, J. Automation and deep (machine) learning in temporomandibular joint disorder radiomics: A systematic review. J. Oral Rehabil. 2023, 50, 501–521. [Google Scholar] [CrossRef] [PubMed]
- Min, Z.; Li, Y.; Xiong, Y.; Wang, H.; Jiang, N. Specific tissue engineering for temporomandibular joint disc perforation. Cytotherapy 2023, 26, 231–241. [Google Scholar] [CrossRef] [PubMed]
- Preethi, G.; Sornagopal, V. MRI image classification using GLCM texture features. In Proceedings of the 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE), Coimbatore, India, 6–8 March 2014; pp. 1–6. [Google Scholar]
- Ghalati, M.K.; Nunes, A.; Ferreira, H.; Serranho, P.; Bernardes, R. Texture analysis and its applications in biomedical imaging: A survey. IEEE Rev. Biomed. Eng. 2021, 15, 222–246. [Google Scholar] [CrossRef]
- Ramola, A.; Shakya, A.K.; Van Pham, D. Study of statistical methods for texture analysis and their modern evolutions. Eng. Rep. 2020, 2, e12149. [Google Scholar] [CrossRef]
- Zhou, M.; Scott, J.; Chaudhury, B.; Hall, L.; Goldgof, D.; Yeom, K.W.; Iv, M.; Ou, Y.; Kalpathy-Cramer, J.; Napel, S. Radiomics in brain tumor: Image assessment, quantitative feature descriptors, and machine-learning approaches. Am. J. Neuroradiol. 2018, 39, 208–216. [Google Scholar] [CrossRef]
- Liu, M.Q.; Zhang, X.W.; Fan, W.P.; He, S.L.; Wang, Y.Y.; Chen, Z.Y. Functional changes of the lateral pterygoid muscle in patients with temporomandibular disorders: A pilot magnetic resonance images texture study. Chin. Med. J. 2020, 133, 530–536. [Google Scholar] [CrossRef]
- Willard, V.P.; Kalpakci, K.N.; Reimer, A.J.; Athanasiou, K.A. The regional contribution of glycosaminoglycans to temporomandibular joint disc compressive properties. J. Biomech. Eng. 2012, 134, 011011. [Google Scholar] [CrossRef]
- Ahmad, M.; Hollender, L.; Anderson, Q.; Kartha, K.; Ohrbach, R.; Truelove, E.L.; John, M.T.; Schiffman, E.L. Research diagnostic criteria for temporomandibular disorders (RDC/TMD): Development of image analysis criteria and examiner reliability for image analysis. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endodontol. 2009, 107, 844–860. [Google Scholar] [CrossRef] [PubMed]
- Moen, K.; Hellem, S.; Geitung, J.T.; Skartveit, L. A practical approach to interpretation of MRI of the temporomandibular joint. Acta Radiol. 2010, 51, 1021–1027. [Google Scholar] [CrossRef] [PubMed]
- Costa, A.; D’Abreu, A.; Cendes, F. Temporomandibular joint internal derangement: Association with headache, joint effusion, bruxism, and joint pain. J. Contemp. Dent. Pract. 2008, 9, 9–16. [Google Scholar] [CrossRef]
- Scapino, R.P.; Obrez, A.; Greising, D. Organization and function of the collagen fiber system in the human temporomandibular joint disk and its attachments. Cells Tissues Organs 2006, 182, 201–225. [Google Scholar] [CrossRef] [PubMed]
- Drace, J.E.; Enzmann, D.R. Defining the normal temporomandibular joint: Closed-, partially open-, and open-mouth MR imaging of asymptomatic subjects. Radiology 1990, 177, 67–71. [Google Scholar] [CrossRef]
- De Rosa, C.S.; Bergamini, M.L.; Palmieri, M.; Sarmento, D.J.S.; de Carvalho, M.O.; Ricardo, A.L.F.; Hasseus, B.; Jonasson, P.; Braz-Silva, P.H.; Ferreira Costa, A.L. Differentiation of periapical granuloma from radicular cyst using cone beam computed tomography images texture analysis. Heliyon 2020, 6, e05194. [Google Scholar] [CrossRef]
- Cohen, J. A power primer. Psychol. Bull. 1992, 112, 155–159. [Google Scholar] [CrossRef]
- Tasaki, M.M.; Westesson, P.-L.; Isberg, A.M.; Ren, Y.-F.; Tallents, R.H. Classification and prevalence of temporomandibular joint disk displacement in patients and symptom-free volunteers. Am. J. Orthod. Dentofac. Orthop. 1996, 109, 249–262. [Google Scholar] [CrossRef]
- Styles, C.; Whyte, A. MRI in the assessment of internal derangement and pain within the temporomandibular joint: A pictorial essay. Br. J. Oral Maxillofac. Surg. 2002, 40, 220–228. [Google Scholar] [CrossRef]
- Larheim, T.A. Role of magnetic resonance imaging in the clinical diagnosis of the temporomandibular joint. Cells Tissues Organs 2005, 180, 6–21. [Google Scholar] [CrossRef]
- Knezevic, M.J.; Knezevic, A.; Boban, J.; Maletin, A.; Milekic, B.; Koprivica, D.D.; Puskar, T.; Semnic, R. High-Field Magnetic Resonance Imaging of the Temporomandibular Joint Low Agreement with Clinical Diagnosis in Asymptomatic Females. Diagnostics 2023, 13, 1986. [Google Scholar] [CrossRef]
- Tasaki, M.M.; Westesson, P.-L. Temporomandibular joint: Diagnostic accuracy with sagittal and coronal MR imaging. Radiology 1993, 186, 723–729. [Google Scholar] [CrossRef]
- Emshoff, R.; Brandlmaier, I.; Bertram, S.; Rudisch, A. Relative odds of temporomandibular joint pain as a function of magnetic resonance imaging findings of internal derangement, osteoarthrosis, effusion, and bone marrow edema. Oral Surg. Oral Med. Oral Pathol. Oral Radiol. Endodontol. 2003, 95, 437–445. [Google Scholar] [CrossRef]
- Thomas, N.; Harper, D.; Aronovich, S. Do signs of an effusion of the temporomandibular joint on magnetic resonance imaging correlate with signs and symptoms of temporomandibular joint disease? Br. J. Oral Maxillofac. Surg. 2018, 56, 96–100. [Google Scholar] [CrossRef]
- Lin, W.-C.; Lo, C.-P.; Chiang, I.-C.; Hsu, C.-C.; Hsu, W.-L.; Liu, D.-W.; Juan, Y.-H.; Liu, G.-C. The use of pseudo-dynamic magnetic resonance imaging for evaluating the relationship between temporomandibular joint anterior disc displacement and joint pain. Int. J. Oral Maxillofac. Surg. 2012, 41, 1501–1504. [Google Scholar] [CrossRef] [PubMed]
- de Souza Pinto, G.N.; Grossmann, E.; Iwaki Filho, L.; Groppo, F.C.; Poluha, R.L.; Muntean, S.A.; Iwaki, L.C.V. Correlation between joint effusion and morphology of the articular disc within the temporomandibular joint as viewed in the sagittal plane in patients with chronic disc displacement with reduction: A retrospective analytical study from magnetic resonance imaging. CRANIO® 2019, 39, 119–124. [Google Scholar]
- Materka, A. Texture analysis methodologies for magnetic resonance imaging. Dialogues Clin. Neurosci. 2022, 6, 243–250. [Google Scholar] [CrossRef] [PubMed]
- Juras, V.; Toegel, S.; Hager, B.; Schreiner, M.; Janacova, V.; Heule, R.; Laurent, D.; Saxer, F.; Bieri, O.; Raithel, E. Histological Validation of the Textural Features from Quantitative MRI For Determination of Cartilage Degeneration. Osteoarthr. Cartil. 2023, 31, S40–S42. [Google Scholar] [CrossRef]
Texture Parameter | Abbreviation | Description |
---|---|---|
Angular second moment | AngScMom | Measurement of image uniformity |
Contrast | Contrast | Represents the amount of local variation in gray level |
Correlation | Correlat | Linear measure dependence of gray level between neighboring pixels |
Sum of squares | SumOfSqs | Measurement of the dispersion (related to average) of gray-level distribution |
Inverse difference moment | InvDfMom | Homogeneity of the distribution of gray level on the image |
Sum of average | SumAverg | Mean of the distribution of the sum of gray-level values |
Sum of variance | SumVarnc | Dispersion around the mean of the sum distribution of gray level |
Sum of entropy | SumEntrp | Disorganization of the sum distribution of gray level |
Entropy | Entropy | Degree of disorder between pixels in the image |
Difference of variance | DifVarnc | Dispersion of the gray-level difference |
Difference of entropy | DifEntrp | Disorganization of the gray-level difference |
Parameter | Group | Average | Maximum | p-Value | Effect Size |
---|---|---|---|---|---|
AngScMom | Effusion | 0.111 | 0.247 | 0.604 | 0.03 |
No effusion | 0.121 | 1.000 | |||
Contrast | Effusion | 2.820 | 11.400 | 0.891 | 0.01 |
No effusion | 2.950 | 22.100 | |||
Correlat | Effusion | 0.560 | 0.970 | 0.102 | 0.11 |
No effusion | 0.625 | 0.985 | |||
SumOfSqs | Effusion | 5.43 | 40.50 | 0.067 | 0.12 |
No effusion | 6.91 | 65.80 | |||
InvDfMom | Effusion | 0.574 | 0.767 | 0.725 | 0.02 |
No effusion | 0.580 | 1.000 | |||
SumAverg | Effusion | 17.4 | 32.2 | 0.137 | 0.10 |
No effusion | 19.7 | 53.5 | |||
SumVarnc | Effusion | 18.9 | 159.0 | 0.06 | 0.13 |
No effusion | 24.7 | 250.0 | |||
SumEntrp | Effusion | 0.839 | 1.060 | 0.822 | 0.02 |
No effusion | 0.841 | 1.170 | |||
Entropy | Effusion | 1.100 | 1.380 | 0.729 | 0.02 |
No effusion | 1.090 | 1.520 | |||
DifVarnc | Effusion | 0.903 | 4.780 | 0.995 | 0.00 |
No effusion | 0.920 | 7.700 | |||
DifEntrp | Effusion | 0.441 | 0.712 | 0.816 | 0.02 |
No effusion | 0.434 | 0.787 |
Parameter | Group | Average | p-Value | Effect Size |
---|---|---|---|---|
AngScMom | Effusion | 0.122 | 0.678 | 0.03 |
No effusion | 0.132 | |||
Contrast | Effusion | 0.572 | 0.981 | 0.00 |
No effusion | 0.627 | |||
Correlat | Effusion | 0.883 | 0.205 | 0.09 |
No effusion | 0.893 | |||
SumOfSqs | Effusion | 5.47 | 0.070 | 0.12 |
No effusion | 6.95 | |||
InvDfMom | Effusion | 0.762 | 0.848 | 0.01 |
No effusion | 0.763 | |||
SumAverg | Effusion | 17.4 | 0.139 | 0.10 |
No effusion | 19.8 | |||
SumVarnc | Effusion | 21.3 | 0.075 | 0.12 |
No effusion | 27.2 | |||
SumEntrp | Effusion | 0.939 | 0.867 | 0.01 |
No effusion | 0.939 | |||
Entropy | Effusion | 1.090 | 0.963 | 0.00 |
No effusion | 1.090 | |||
DifVarnc | Effusion | 0.291 | 0.982 | 0.00 |
No effusion | 0.311 | |||
DifEntrp | Effusion | 0.321 | 0.969 | 0.00 |
No effusion | 0.318 |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Girondi, C.M.; de Castro Lopes, S.L.P.; Ogawa, C.M.; Braz-Silva, P.H.; Costa, A.L.F. Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images. Dent. J. 2024, 12, 82. https://doi.org/10.3390/dj12030082
Girondi CM, de Castro Lopes SLP, Ogawa CM, Braz-Silva PH, Costa ALF. Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images. Dentistry Journal. 2024; 12(3):82. https://doi.org/10.3390/dj12030082
Chicago/Turabian StyleGirondi, Camila Miorelli, Sérgio Lúcio Pereira de Castro Lopes, Celso Massahiro Ogawa, Paulo Henrique Braz-Silva, and Andre Luiz Ferreira Costa. 2024. "Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images" Dentistry Journal 12, no. 3: 82. https://doi.org/10.3390/dj12030082
APA StyleGirondi, C. M., de Castro Lopes, S. L. P., Ogawa, C. M., Braz-Silva, P. H., & Costa, A. L. F. (2024). Texture Analysis of Temporomandibular Joint Disc Changes Associated with Effusion Using Magnetic Resonance Images. Dentistry Journal, 12(3), 82. https://doi.org/10.3390/dj12030082