Liu Junmin, Drangova Maria
Imaging Research Laboratories, Robarts Research Institute, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Canada.
Department of Medical Biophysics, Schulich School of Medicine & Dentistry, University of Western Ontario, London, Canada.
Magn Reson Med. 2015 Oct;74(4):1177-88. doi: 10.1002/mrm.25497. Epub 2014 Oct 28.
To develop and evaluate a multiecho phase-unwrapping-based B0 mapping method.
The proposed method estimates a B0 map by Non-Iterative Correction of phase-Errors (B0-NICE). The B0-NICE method generates an initial B0 map from a "pseudo in-phase" data set by introducing a bias frequency shift to the multipeak fat model, followed by correcting the phase errors using both phase and magnitude information. The performance of the B0-NICE method was evaluated with all data cases from the 2012 ISMRM Challenge.
The B0 field estimates from B0-NICE were compared with those generated by GlObally Optimal Surface Estimation (GOOSE). In the presence of large B0 inhomogeneity, the B0-NICE method was able to generate more realistic B0 maps from multiecho data, compared with GOOSE. Accurate estimation of fat-fraction (FF) map was also achieved using the proposed algorithm.
The primary finding of the present study is that accurate FF and B0 maps are achievable if magnitude data is processed independently and used to correct phase errors existing in B0 maps generated by phase unwrapping. The B0-NICE software is freely available to the scientific community.
开发并评估一种基于多回波相位解缠的B0映射方法。
所提出的方法通过相位误差的非迭代校正(B0-NICE)来估计B0图。B0-NICE方法通过向多峰脂肪模型引入偏置频率偏移,从“伪同相”数据集中生成初始B0图,然后使用相位和幅度信息校正相位误差。使用2012年ISMRM挑战赛的所有数据案例评估了B0-NICE方法的性能。
将B0-NICE得到的B0场估计值与全局最优表面估计(GOOSE)生成的估计值进行比较。在存在较大B0不均匀性的情况下,与GOOSE相比,B0-NICE方法能够从多回波数据中生成更逼真的B0图。使用所提出的算法也实现了脂肪分数(FF)图的准确估计。
本研究的主要发现是,如果独立处理幅度数据并用于校正相位解缠生成的B0图中存在的相位误差,则可以获得准确的FF和B0图。科学界可免费获得B0-NICE软件。