School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada.
Magn Reson Med. 2013 May;69(5):1396-407. doi: 10.1002/mrm.24384. Epub 2012 Jun 26.
To improve susceptibility quantification, a threshold-based k-space/image domain iterative approach that uses geometric information from the susceptibility map itself as a constraint to overcome the ill-posed nature of the inverse filter is introduced. Simulations were used to study the accuracy of the method and its robustness in the presence of noise. In vivo data were processed and analyzed using this method. Both simulations and in vivo results show that most streaking artifacts inside the susceptibility map caused by the ill-defined inverse filter were suppressed by the iterative approach. In simulated data, the bias toward lower mean susceptibility values inside vessels has been shown to decrease from around 10% to 2% when choosing an appropriate threshold value for the proposed iterative method. Typically, three iterations are sufficient for this approach to converge and this process takes less than 30 s to process a 512×512×256 dataset. This iterative method improves quantification of susceptibility inside vessels and reduces streaking artifacts throughout the brain for data collected from a single-orientation acquisition. This approach has been applied to vessels alone as well as to vessels and other structures with lower susceptibility to generate whole brain susceptibility maps with significantly reduced streaking artifacts.
为了提高磁化率定量的准确性，引入了一种基于阈值的 k 空间/图像域迭代方法，该方法利用磁化率图本身的几何信息作为约束条件，以克服逆滤波器的不适定性。通过模拟研究了该方法的准确性及其在噪声存在下的鲁棒性。使用该方法对体内数据进行了处理和分析。模拟和体内结果均表明，迭代方法抑制了由逆滤波器定义不明确引起的磁化率图内的大部分条纹伪影。在模拟数据中，当为所提出的迭代方法选择适当的阈值时，血管内平均磁化率值的偏差从约 10%降低到 2%。通常，该方法只需三次迭代即可收敛，处理 512×512×256 数据集的过程不到 30 秒。该迭代方法提高了血管内磁化率的定量准确性，并减少了单方向采集数据中整个大脑的条纹伪影。该方法已应用于仅血管以及磁化率较低的血管和其他结构，以生成具有明显减少条纹伪影的整个大脑磁化率图。
