Bone quantitative susceptibility mapping using a chemical species-specific R2* signal model with ultrashort and conventional echo data
Bone quantitative susceptibility mapping using a chemical species-specific R2* signal model with ultrashort and conventional echo data
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DOI：
10.1002/mrm.26648
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发表时间：
2018-01-01
影响因子：
3.3
通讯作者：
Wang, Yi
中科院分区：
文献类型：
--
作者：
Dimov, Alexey V.;Liu, Zhe;Wang, Yi
目的利用超短回波时间(UTE)梯度回波(GRE)序列和骨特异性有效横向弛豫率(R2*)模拟水-脂肪磁共振信号进行场映射，建立骨的定量磁化率图(QSM)。方法在3Tesla扫描仪上采集三维径向UTE数据(回声时间40s)，并与骨特异性信号模型拟合，绘制化学成分和磁感场。实验在猪蹄上进行，在活体内在健康人身上进行(n=7)。对于水-脂肪分离，将R2*衰变主要归因于水的骨骼特定模型与指定脂肪和水的相同衰变的标准模型进行了比较。结果与标准模型相比，骨骼特异性R2*方法显著减少了所有测试数据集中皮质骨内脂肪含量的误差，从而减少了QSM中的伪影。猪蹄的骨CT值与QSM值有很好的相关性(R2=0.77)。所有受试者均成功地生成了骨QSM。结论利用UTE结合常规回波时间GRE采集和骨特异性R2*信号模型进行骨QSM是可行的。Magn Reson Med 79：121-128,2018。(C)2017国际医学磁共振学会。
PurposeTo develop quantitative susceptibility mapping (QSM) of bone using an ultrashort echo time (UTE) gradient echo (GRE) sequence for signal acquisition and a bone-specific effective transverse relaxation rate ( R2*) to model water-fat MR signals for field mapping.MethodsThree-dimensional radial UTE data (echo times 40s) was acquired on a 3 Tesla scanner and fitted with a bone-specific signal model to map the chemical species and susceptibility field. Experiments were performed ex vivo on a porcine hoof and in vivo on healthy human subjects (n=7). For water-fat separation, a bone-specific model assigning R2* decay mostly to water was compared with the standard models that assigned the same decay for both fat and water. In the ex vivo experiment, bone QSM was correlated with CT.ResultsCompared with standard models, the bone-specific R2* method significantly reduced errors in the fat fraction within the cortical bone in all tested data sets, leading to reduced artifacts in QSM. Good correlation was found between bone CT and QSM values in the porcine hoof (R-2=0.77). Bone QSM was successfully generated in all subjects.ConclusionsThe QSM of bone is feasible using UTE with a conventional echo time GRE acquisition and a bone-specific R2* signal model. Magn Reson Med 79:121-128, 2018. (c) 2017 International Society for Magnetic Resonance in Medicine.