Dimov Alexey V, Liu Tian, Spincemaille Pascal, Ecanow Jacob S, Tan Huan, Edelman Robert R, Wang Yi
Department of Biomedical Engineering, Cornell University, Ithaca, New York, New York, USA.
Department of Radiology, Weill Cornell Medical College, New York, New York, USA.
Magn Reson Med. 2015 Jun;73(6):2100-10. doi: 10.1002/mrm.25328. Epub 2014 Jun 19.
The purpose of this work is to address the unsolved problem of quantitative susceptibility mapping (QSM) of tissue with fat where both fat and susceptibility change the MR signal phase.
The chemical shift of fat was treated as an additional unknown and was estimated jointly with susceptibility to provide the best data fitting using an automated and iterative algorithm. A simplified susceptibility model was used to calculate an updated value of the chemical shift based on the local magnetic field in each iteration. Numerical simulation, phantom experiments and in vivo imaging were performed. Artifacts were assessed by measuring the susceptibility variance in uniform regions. Accuracy was assessed by comparison with ground truth in simulation, and using a susceptibility matching approach in phantom.
Using the proposed method, artifacts on the QSM image were markedly suppressed in all tested datasets compared with results generated using fixed chemical shifts. Accuracy of the estimated susceptibility was also improved in numerical simulation and phantom experiments.
A joint estimation of fat content and magnetic susceptibility using an iterative chemical shift update was shown to improve image quality and accuracy on QSM images.
本研究旨在解决脂肪组织定量磁化率成像（QSM）中尚未解决的问题，即脂肪和磁化率均会改变磁共振信号相位。
将脂肪的化学位移视为一个额外的未知量，并与磁化率联合估计，以使用自动迭代算法实现最佳数据拟合。在每次迭代中，使用简化的磁化率模型基于局部磁场计算化学位移的更新值。进行了数值模拟、体模实验和体内成像。通过测量均匀区域的磁化率方差评估伪影。在模拟中与真实值比较，并在体模中使用磁化率匹配方法评估准确性。
与使用固定化学位移生成的结果相比，在所提出的方法下，所有测试数据集中QSM图像上的伪影均得到显著抑制。在数值模拟和体模实验中，估计磁化率的准确性也有所提高。
使用迭代化学位移更新对脂肪含量和磁化率进行联合估计可提高QSM图像的质量和准确性。
