Optimizing 3D FLAIR to detect MS lesions: pushing past factory settings for precise results View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-08-01

AUTHORS

Augustin Lecler, C. Bouzad, R. Deschamps, F. Maizeroi, J. C. Sadik, A. Gueguen, O. Gout, H. Picard, J. Savatovsky

ABSTRACT

BackgroundTo assess the diagnostic value of three 3D FLAIR sequences with differing repetition-times (TR) at 3-Tesla when detecting multiple sclerosis (MS) lesions.MethodsIn this prospective study, approved by the institutional review board, 27 patients with confirmed MS were prospectively included. One radiologist performed manual segmentations of all high-signal intensity lesions using three 3D FLAIR data sets with different TR of 4800 ms (“FLAIR4800”), 8000 ms (“FLAIR8000”) and 10,000 ms (“FLAIR10,000”) and two radiologists double-checked it. The main judgment criterion was the overall number of lesions; secondary objectives were the assessment of lesion location, as well as measuring contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR). A non-parametric Wilcoxon’s test was used to compare the differing FLAIR.ResultsThe FLAIR8000 and FLAIR10,000 detected significantly more overall lesions per patient as compared with the FLAIR4800 [116.1 (± 61.7) (p = 0.02) and 115.8 (± 56.3) (p = 0.03) versus 99.2 (± 66.9), respectively]. The FLAIR8000 and FLAIR10,000 detected four and eight times more cortical or juxta-cortical lesions per patient as compared with FLAIR4800 [1.6 (± 2.2) (p = 0.001) and 4.1 (± 5.9) (p = 6 × 10–5) versus 0.4 (± 1.1), respectively]. CNR was significantly correlated to the TR value. It was significantly higher with FLAIR10,000 than it was with FLAIR8000 and FLAIR4800 [16.3 (± 3.5) versus 15 (± 2.4) (p = 0.01) and 12 (± 2.2) (p = 2 × 10–6), respectively]ConclusionAn optimized 3D FLAIR with a long TR significantly improved both overall lesion detection and CNR in MS patients as compared to a 3D FLAIR with factory settings. More... »

PAGES

2786-2795

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00415-019-09490-y

DOI

http://dx.doi.org/10.1007/s00415-019-09490-y

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1120022070

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/31372735


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