Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR
White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR)...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2014/239123 |
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| _version_ | 1832559106758541312 |
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| author | Yi Zhong David Utriainen Ying Wang Yan Kang E. Mark Haacke |
| author_facet | Yi Zhong David Utriainen Ying Wang Yan Kang E. Mark Haacke |
| author_sort | Yi Zhong |
| collection | DOAJ |
| description | White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to be Y=1.04X+1.74 (R2=0.96). The automated algorithm estimates the number, volume, and category of WMH. |
| format | Article |
| id | doaj-art-e8c1bbeb44de4e519ea7355f9d10e5d9 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-e8c1bbeb44de4e519ea7355f9d10e5d92025-02-03T01:30:56ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962014-01-01201410.1155/2014/239123239123Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIRYi Zhong0David Utriainen1Ying Wang2Yan Kang3E. Mark Haacke4School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, Liaoning 110004, ChinaMagnetic Resonance Innovations Inc., 440 E. Ferry Street, Detroit, MI 48202, USADepartment of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USASchool of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, Liaoning 110004, ChinaSchool of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, Liaoning 110004, ChinaWhite matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to be Y=1.04X+1.74 (R2=0.96). The automated algorithm estimates the number, volume, and category of WMH.http://dx.doi.org/10.1155/2014/239123 |
| spellingShingle | Yi Zhong David Utriainen Ying Wang Yan Kang E. Mark Haacke Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR International Journal of Biomedical Imaging |
| title | Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR |
| title_full | Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR |
| title_fullStr | Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR |
| title_full_unstemmed | Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR |
| title_short | Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR |
| title_sort | automated white matter hyperintensity detection in multiple sclerosis using 3d t2 flair |
| url | http://dx.doi.org/10.1155/2014/239123 |
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