MRI and additive manufacturing of nasal alar constructs for patient-specific reconstruction

  • D.O. Visscher
  • , M. van Eijnatten
  • , N.P.T.J. Liberton
  • , J. Wolff
  • , M.B.M. Hofman
  • , M.N. Helder
  • , J.P.W. Don Griot
  • , P.P.M. van Zuijlen

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

Surgical reconstruction of cartilaginous defects remains a major challenge. In the current study, we aimed to identify an imaging strategy for the development of patient-specific constructs that aid in the reconstruction of nasal deformities. Magnetic Resonance Imaging (MRI) was performed on a human cadaver head to find the optimal MRI sequence for nasal cartilage. This sequence was subsequently used on a volunteer. Images of both were assessed by three independent researchers to determine measurement error and total segmentation time. Three dimensionally (3D) reconstructed alar cartilage was then additively manufactured. Validity was assessed by comparing manually segmented MR images to the gold standard (micro-CT). Manual segmentation allowed delineation of the nasal cartilages. Inter- and intra-observer agreement was acceptable in the cadaver (coefficient of variation 4.6-12.5%), but less in the volunteer (coefficient of variation 0.6-21.9%). Segmentation times did not differ between observers (cadaver P = 0.36; volunteer P = 0.6). The lateral crus of the alar cartilage was consistently identified by all observers, whereas part of the medial crus was consistently missed. This study suggests that MRI is a feasible imaging modality for the development of 3D alar constructs for patient-specific reconstruction.

Original languageEnglish
Article number10021
Number of pages8
JournalScientific Reports
Volume7
Issue number1
DOIs
Publication statusPublished - 30 Aug 2017

Bibliographical note

With supplementary table

Funding

The authors would like to thank the donor and her family who enabled this research, as well as Dr. J.P.A. Kuijer (Medical Physicist) for MRI support. The authors also acknowledge Dr. L.B. Mokkink from the VUmc clinimetrics group for methodological support. This work was supported by the Dutch Burn Foundation (project number 15.107).

FundersFunder number
Dutch Burn Foundation15.107

    UN SDGs

    This output contributes to the following UN Sustainable Development Goals (SDGs)

    1. SDG 3 - Good Health and Well-being
      SDG 3 Good Health and Well-being

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