Bibliographic Details
| Title: |
Evaluation of MRI artifacts at 3 Tesla for 38 commonly used cosmetics |
| Authors: |
Escher, Kirin1, Shellock, Frank G.2 frank.shellock@mrisafety.com |
| Source: |
Magnetic Resonance Imaging (0730725X). Jun2013, Vol. 31 Issue 5, p778-782. 5p. |
| Subjects: |
Magnetic resonance imaging, Cosmetics, Imaging phantoms, Contrast media, Baldness, Eye care |
| Abstract: |
Abstract: Purpose: To evaluate MRI artifacts at 3-Tesla for 38 commonly used cosmetics. Materials and Methods: Thirty-eight cosmetics (16, nail polishes; 5, eyeliners; 3, mascaras; 10, eye shadows; 1, lip gloss; 1, body lotion; 1, body glitter, and 1, hair loss concealer) underwent evaluation for MRI artifacts at 3-Tesla. The cosmetics were applied a copper-sulfate-filled, phantom and initially assessed using a “screening” gradient echo (GRE) pulse sequence. Of the 38 different cosmetics, 14 (37%) exhibited artifacts. For these 14 cosmetics, additional characterization of artifacts was performed using a GRE pulse sequence. A qualitative scale was applied to characterize the artifact size. Results: Artifacts were observed, as follows: 2, nail polishes; 5, eyeliners; 3, mascaras; 3, eye shadows; 1, hair loss concealer. Artifact size ranged from small (eye shadow) to very large (hair loss concealer) and tended to be associated with the presence of iron oxide or other metal-based ingredient. Conclusions: Commonly used cosmetics caused artifacts that may create issues if the area of interest is the same as where the cosmetic was applied or if its presence was unknown, thus, potentially causing it to be construed as pathology. Therefore, these findings have important implications for patients referred for MRI examinations. [Copyright &y& Elsevier] |
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Copyright of Magnetic Resonance Imaging (0730725X) is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |