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Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance.

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Presentation on theme: "Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance."— Presentation transcript:

1 Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance Imaging Study Questionnaire

2 Instructions: Use the flowchart (Figure 1) to identify the IOFB material. All eyes have an IOFB in them. The best representative scan has been chosen. All MRI scans are identical in localization. Analyze by CT, then T1/T2, then GE. Enlarged on GE = compared to T1/T2. Any IOFB may be included more than once. Correct IOFB identifications are on the last slide.

3 Example T1 T2 CT GE

4 Solution: CT = detectable IOFB T1/T2 = signal void with surrounding hyperintensity Therefore: Stone material GE = void with surrounding white ring; artifact enlarged compared to T1/T2 Bright signal on CT Therefore: Porcelain

5 #1 T1 T2 CT GE

6 #2 T1 T2 CT GE

7 #3 T1 T2 CT GE

8 #4 T1 CT GE T2

9 #5 T2T1 CT GE

10 #6 T1 T2 CT GE

11 #7 T1 T2 CT GE

12 #8 T1 T2 CT GE

13 #9 T1 T2 CT GE

14 #10 T2 CT T1 GE

15 THANK YOU!

16 Correct IOFB identifications: #1 – Gravel #2 – Plastic #3 – Windshield glass #4 – Porcelain #5 - Wood #6 – Pencil graphite #7 – CR39 #8 – Thorn #9 – Concrete #10 – Bottle glass


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