期刊
AMERICAN JOURNAL OF NEURORADIOLOGY
卷 36, 期 11, 页码 2062-2067出版社
AMER SOC NEURORADIOLOGY
DOI: 10.3174/ajnr.A4418
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资金
- Dutch MS Research Foundation [09-3586]
- US National MS Society [RG 4916A2/1]
- Noto Foundation
BACKGROUND AND PURPOSE: At 7T MR imaging, T2*-weighted gradient echo has been shown to provide high-resolution anatomic images of gray matter lesions. However, few studies have verified T2*WI lesions histopathologically or compared them with more standard techniques at ultra-high-field strength. This study aimed to determine the sensitivity of T2WI and T2*WI sequences for detecting cortical GM lesions in MS. MATERIALS AND METHODS: At 7T, 2D multiecho spin-echo T2WI and 3D gradient-echo T2*WI were acquired from 27 formalin-fixed coronal hemispheric brain sections of 15 patients and 4 healthy controls. Proteolipid-stained tissue sections (8 mu m) were matched to the corresponding MR images, and lesions were manually scored on both MR imaging sequences (blinded to histopathology) and tissue sections (blinded to MR imaging). The sensitivity of MR imaging sequences for GM lesion types and white matter lesions was calculated. An unblinded retrospective scoring was also performed. RESULTS: If all cortical GM lesions were taken into account, the T2WI sequence detected slightly more lesions than the T2*Wl sequence: 28% and 16%, respectively (P =.054). This difference disappeared when only intracortical lesions were considered. When histopathologic information (type, location) was revealed to the reader, the sensitivity went up to 84% (T2WI) and 85% (T2*WI) (not significant). Furthermore, the false-positive rate was 8.6% for the T2WI and 10.5% for the T2*WI sequence. CONCLUSIONS: There is no strong advantage of the T2*WI sequence compared with a conventional T2WI sequence in the detection of cortical lesions at 7T. Retrospectively, a high percentage of lesions could be detected with both sequences. However, many lesions are still missed prospectively. This could possibly be minimized with better a priori observer training.
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