Cortical bone ultrashort TE study with inversion recovery preparation
Yanchun Zhu; Shuo Li; Jie Yang; Nan Fu; Shaode Yu; Rongmao Li; Jing Xiong; Yaoqin Xie
刊名BMC Medical Imaging
2016
英文摘要Aims:Efficient improving contrast of cortical bone from surrounding long T2 tissues is important in ultarshort echo time (UTE) imaging.Methods:UTE acquisition prepared by adiabatic inversion recovery were developed for this purpose. The effect of TI on cortical bone imaging was evaluated on mature bovine tibial mid-shafts using a 3-T clinical MR scanner. The imaging parameters were: TE/TR = 10 μs/300 ms, TI = 80, 90, 100, 110, 120, 130, and 140ms, FA =45°, Bandwidth = ±62.5 kHz, FOV = 8cm, slice thickness = 7mm, NEX = 2, single slice.Results:With TI = 90ms, excellent suppression of long T2 signals was achieved with the CNRcortical-muscle value of 13.49 ± 0.67, and the CNRcortical-marrrow value of 12.26 ± 0.86. Due to different T1s of muscle and fat, some residual signals from fat were presented. Therefore, the CNRmarrow-muscle value was 1.24 ± 0.35. Furthermore, approximate 80% signals from muscle and fat were suppressed.Conclusions:The 2D adiabatic inversion UTE sequence with a TI of 90 ms provided excellent contrast depiction of bovine cortical bone. Due to the T1 difference, muscle and fat longitudinal magnetizations cannot arrive to the null point at the same time. Therefore, simultaneous reduction of long T2 signals is complicated.
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原文出处https://bmcmedimaging.biomedcentral.com/articles/10.1186/s12880-016-0164-6
语种英语
内容类型期刊论文
源URL[http://ir.siat.ac.cn:8080/handle/172644/10425]  
专题深圳先进技术研究院_医工所
作者单位BMC Medical Imaging
推荐引用方式
GB/T 7714
Yanchun Zhu,Shuo Li,Jie Yang,et al. Cortical bone ultrashort TE study with inversion recovery preparation[J]. BMC Medical Imaging,2016.
APA Yanchun Zhu.,Shuo Li.,Jie Yang.,Nan Fu.,Shaode Yu.,...&Yaoqin Xie.(2016).Cortical bone ultrashort TE study with inversion recovery preparation.BMC Medical Imaging.
MLA Yanchun Zhu,et al."Cortical bone ultrashort TE study with inversion recovery preparation".BMC Medical Imaging (2016).
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