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Constructing a Panoramic Radiograph Image Based on Magnetic Resonance Imaging Data

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The Latest Developments and Challenges in Biomedical Engineering (PCBEE 2023)

Abstract

Panoramic radiograph images are one of the basic examinations used during dental and orthodontic treatment. Their creation is based on ionizing radiation, to which the patient is therefore exposed. The adverse effects of this radiation on the patient’s health is a topic that has been increasingly raised in numerous publications and journals. Therefore, the motivation for this study was to develop a mechanism that reconstructs panoramic radiograph images from MRI data, which is a harmless method. To achieve this, an algorithm described in a previous article is used to create a three-dimensional bone and skin tissue model from MRI data derived from T1-weighted and T2-weighted sequences. Subsequently, the craniofacial part of this model is projected onto a plane to form a panoramic radiograph-like image. Different approaches to model projection are presented. Despite the noise which comes from the high degree of interpolation in the model used, the generated results are the first step in the generation of panoramic craniofacial images without the use of ionizing radiation.

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References

  1. Broyden, C.G.: The convergence of a class of double-rank minimization algorithms 1. General considerations. IMA J. Appl. Math. 6(1), 76–90 (1970). https://doi.org/10.1093/imamat/6.1.76

  2. Cenda, P., Obuchowicz, R., Piórkowski, A.: Construction of a cephalometric image based on magnetic resonance imaging data. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Information Technology in Biomedicine, pp. 143–154. Springer International Publishing, Cham (2022)

    Google Scholar 

  3. Cieślak, A., Piórkowski, A., Obuchowicz, R.: Comparison of interpolation methods for MRI Images Acquired with Different Matrix Sizes. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds.) Information Technology in Biomedicine, pp. 119–131. Springer International Publishing, Cham (2022)

    Google Scholar 

  4. Claus, E.B., Calvocoressi, L., Bondy, M.L., Schildkraut, J.M., Wiemels, J.L., Wrensch, M.: Dental x-rays and risk of meningioma. Cancer 118(18), 4530–4537 (2012)

    Article  Google Scholar 

  5. Cung, W., Freedman, L.A., Khan, N.E., Romberg, E., Gardner, P.J., Bassim, C.W., Baldwin, A.M., Widemann, B.C., Stewart, D.R.: Cephalometry in adults and children with neurofibromatosis type 1: implications for the pathogenesis of sphenoid wing dysplasia and the “NF1 facies’’. Eur. J. Med. Genet. 58(11), 584–590 (2015)

    Article  Google Scholar 

  6. Dogdas, B., Shattuck, D.W., Leahy, R.M.: Segmentation of skull and scalp in 3-D human MRI using mathematical morphology. Hum. Brain Mapp. 26(4), 273–285 (2005)

    Article  Google Scholar 

  7. Domeshek, L.F., Mukundan, S., Yoshizumi, T., Marcus, J.R.: Increasing concern regarding computed tomography irradiation in craniofacial surgery. Plast. Reconstr. Surg. 123(4), 1313–1320 (2009)

    Article  Google Scholar 

  8. Eley, K.A., Delso, G.: Automated 3D MRI rendering of the craniofacial skeleton: using ZTE to drive the segmentation of black bone and FIESTA-C images. Neuroradiology 63(1), 91–98 (2021)

    Article  Google Scholar 

  9. Hwang, S.Y., Choi, E.S., Kim, Y.S., Gim, B.E., Ha, M., Kim, H.Y.: Health effects from exposure to dental diagnostic X-ray. Environ. Health Toxicol. 33(4), e2018,017 (2018)

    Google Scholar 

  10. Krille, L., Dreger, S., Schindel, R., Albrecht, T., Asmussen, M., Barkhausen, J., Berthold, J.D., Chavan, A., Claussen, C., Forsting, M., Gianicolo, E.A.L., Jablonka, K., Jahnen, A., Langer, M., Laniado, M., Lotz, J., Mentzel, H.J., Queißer-Wahrendorf, A., Rompel, O., Schlick, I., Schneider, K., Schumacher, M., Seidenbusch, M., Spix, C., Spors, B., Staatz, G., Vogl, T., Wagner, J., Weisser, G., Zeeb, H., Blettner, M.: Risk of cancer incidence before the age of 15 years after exposure to ionising radiation from computed tomography: results from a German cohort study. Radiat. Environ. Biophys. 54(1), 1–12 (2015)

    Google Scholar 

  11. Maillie, H.D., Gilda, J.E.: Radiation-induced cancer risk in radiographic cephalometry. Oral Surg. Oral Med. Oral Pathol. 75(5), 631–637 (1993)

    Article  Google Scholar 

  12. Pflugbeil, S., Pflugbeil, C., Schmitz-Feuerhake, I.: Risk estimates for meningiomas and other late effects after diagnostic X-ray exposure of the skull. Radiat. Prot. Dosim. 147(1–2), 305–309 (2011)

    Article  Google Scholar 

  13. Smith-Bindman, R., Lipson, J., Marcus, R., Kim, K.P., Mahesh, M., Gould, R., Berrington de González, A., Miglioretti, D.L.: Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Arch. Intern. Med. 169(22), 2078–2086 (2009)

    Google Scholar 

  14. Willemink, M.J., Noël, P.B.: The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence. Eur. Radiol. 29, 2185–2195 (2019)

    Article  Google Scholar 

  15. Zhang, R., Lee, H., Zhao, X., Song, H.K., Vossough, A., Wehrli, F.W., Bartlett, S.P.: Bone-Selective MRI as a Nonradiative Alternative to CT for Craniofacial Imaging. Acad. Radiol. 27(11), 1515–1522 (2020)

    Article  Google Scholar 

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Acknowledgements

This work was financed by the AGH University of Science and Technology, Faculty of EAIIB, KBIB on 16.16.120.773 and the grant “Studenckie koła tworza̧ innowacje”—II edition, project no. SKN/SP/535131/2022.

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Correspondence to Piotr Cenda .

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Cenda, P., Cieślak, A., Pociask, E., Obuchowicz, R., Piórkowski, A. (2024). Constructing a Panoramic Radiograph Image Based on Magnetic Resonance Imaging Data. In: Strumiłło, P., Klepaczko, A., Strzelecki, M., Bociąga, D. (eds) The Latest Developments and Challenges in Biomedical Engineering. PCBEE 2023. Lecture Notes in Networks and Systems, vol 746. Springer, Cham. https://doi.org/10.1007/978-3-031-38430-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-38430-1_10

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