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

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

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

Validation of an Algorithm for Non-metallic Intraocular

Foreign Bodies’ Composition Identification Based on

Computed Tomography and Magnetic Resonance Imaging

Study Questionnaire

Page 2: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

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.

Page 3: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

ExampleT1 T2

CTGE

Page 4: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

Solution:

• CT = detectable IOFB• T1/T2 = signal void with surrounding

hyperintensityTherefore: Stone material• GE = void with surrounding white ring; artifact

enlarged compared to T1/T2• Bright signal on CTTherefore: Porcelain

Page 5: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#1T1 T2

CT GE

Page 6: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#2T1 T2

CTGE

Page 7: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#3T1 T2

CTGE

Page 8: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#4T1

CTGE

T2

Page 9: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#5T2T1

CT GE

Page 10: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#6T1 T2

CT GE

Page 11: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#7T1 T2

CT GE

Page 12: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#8T1 T2

CT GE

Page 13: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#9T1 T2

CTGE

Page 14: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

#10T2

CT

T1

GE

Page 15: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

THANK YOU!

Page 16: Validation of an Algorithm for Non-metallic Intraocular Foreign Bodies’ Composition Identification Based on Computed Tomography and Magnetic Resonance

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