April 2014
Volume 55, Issue 13
Free
ARVO Annual Meeting Abstract  |   April 2014
Magnetic Resonance Imaging and Computerized Tomography for the Detection and Characterization of Non-metallic Intraocular Foreign Bodies
Author Affiliations & Notes
  • Elad Moisseiev
    Ophthalmology, Tel Aviv Medical Center, Tel Aviv, Israel
    Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
  • David Last
    Advanced Technology Center, Sheba Medical Center, Tel Hashomer, Israel
  • David Goez
    Advanced Technology Center, Sheba Medical Center, Tel Hashomer, Israel
  • Adiel Barak
    Ophthalmology, Tel Aviv Medical Center, Tel Aviv, Israel
    Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
  • Yael Mardor
    Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
    Advanced Technology Center, Sheba Medical Center, Tel Hashomer, Israel
  • Footnotes
    Commercial Relationships Elad Moisseiev, None; David Last, None; David Goez, None; Adiel Barak, None; Yael Mardor, None
  • Footnotes
    Support None
Investigative Ophthalmology & Visual Science April 2014, Vol.55, 5842. doi:
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    • Get Citation

      Elad Moisseiev, David Last, David Goez, Adiel Barak, Yael Mardor; Magnetic Resonance Imaging and Computerized Tomography for the Detection and Characterization of Non-metallic Intraocular Foreign Bodies. Invest. Ophthalmol. Vis. Sci. 2014;55(13):5842.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract 
 
Purpose
 

To perform a comprehensive analysis of non-metallic intraocular foreign bodies (IOFBs) using CT and MRI, and to assess the feasibility of applying these scans for identifying such IOFBs.

 
Methods
 

An ex vitro model of porcine eyes was used to study IOFBs composed of 10 different materials: plastic, eyeglass lens, bottle glass, windshield glass, porcelain, gravel stone, concrete, wood, thorn, and pencil graphite. For each material, IOFBs of 3 sizes were studied: 3, 2 and 1 mm. A total of 36 eyes were included in the study (30 with IOFBs composed of the 10 materials in 3 different sizes, and 6 controls). Each eye was scanned by CT and MRI using wrist coil for optimal resolution and head coil for simulation of realistic clinical conditions. MRI included T1, T2 and GE sequences. Images were analyzed using a 3D-viewing software in order to determine distinguishing characteristics for each material, and the artifact volume was also calculated. Images were also reviewed by three specialists who were masked to the presence and composition of the IOFBs, to assess the detectability of IOFBs using CT and MRI scans by clinicians.

 
Results
 

Analysis of wrist coil MRI and CT scans yielded distinguishing characteristics for each of the ten materials (Figure 1), and the information provided by these scans was integrated into a clinical algorithm which enables distinction of IOFB material (Figure 2). More materials were identified by MRI than by CT, and smaller IOFB size was shown to make detection more difficult. Head coil MRI scans had a lower resolution than wrist coil scans, but still enabled IOFB detection. Review of CT and head coil MRI scans by masked specialists yielded a 95% agreement rate and allowed detection of most IOFBs.

 
Conclusions
 

MRI was found to be superior to CT in IOFB detection. Using CT and MRI, a set of distinguishing characteristics has been established for the identification of the ten studied materials. We recommend MRI to be part of the evaluation of patients with a suspected IOFB and negative CT, and in cases where determination of the IOFB composition is important.

 
 
A description of each material's appearance on T1-, T2- and GE-MRI and CT scans, along with volume ratios between the MRI sequences, and a summary of the distinguishing characteristics for its detection.
 
A description of each material's appearance on T1-, T2- and GE-MRI and CT scans, along with volume ratios between the MRI sequences, and a summary of the distinguishing characteristics for its detection.
 
 
A flowchart for distinguishing IOFB composition based on MRI and CT imaging.
 
A flowchart for distinguishing IOFB composition based on MRI and CT imaging.
 
Keywords: 550 imaging/image analysis: clinical • 742 trauma  
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