Medical Image Computing and Signal Processing Laboratory, Indian Institute of Information Technology and Management, Thiruvananthapuram, Kerala, India.
High-Field Magnetic Resonance, Max-Planck-Institute for Biological Cybernetics, Tübingen, Germany.
Magn Reson Med. 2021 Oct;86(4):2220-2233. doi: 10.1002/mrm.28841. Epub 2021 May 24.
To develop a spatio-temporal approach to accurately unwrap multi-echo gradient-recalled echo phase in the presence of high-field gradients.
Using the virtual echo-based Nyquist sampled (VENyS) algorithm, the temporal unwrapping procedure is modified by introduction of one or more virtual echoes between the first lower and the immediate higher echo, so as to reinstate the Nyquist condition at locations with high-field gradients. An iterative extension of the VENyS algorithm maintains spatial continuity by adjusting the phase rotations to make the neighborhood phase differences less than π. The algorithm is evaluated using simulated data, Gadolinium contrast-doped phantom, and in vivo brain, abdomen, and chest data sets acquired at 3 T and 9.4 T. The unwrapping performance is compared with the standard temporal unwrapping algorithm used in the morphology-enabled dipole inversion-QSM pipeline as a benchmark for validation.
Quantitative evaluation using numerical phantom showed significant reduction in unwrapping errors in regions of large field gradients, and the unwrapped phase revealed an exact match with the linear concentration profile of vials in a gadolinium contrast-doped phantom data acquired at 9.4 T. Without the need for additional spatial unwrapping, the iterative VENyS algorithm was able to generate spatially continuous phase images. Application to in vivo data resulted in better unwrapping performance, especially in regions with large susceptibility changes such as the air/tissue interface.
The iterative VENyS algorithm serves as a robust unwrapping method for multi-echo gradient-recalled echo phase in the presence of high-field gradients.
开发一种时空方法，以在存在高磁场梯度的情况下准确解缠多回波梯度回波相位。
使用基于虚拟回波的奈奎斯特采样（VENyS）算法，通过在第一个较低回波和紧接着的较高回波之间引入一个或多个虚拟回波，对时间解缠过程进行修改，从而在具有高磁场梯度的位置恢复奈奎斯特条件。VENyS 算法的迭代扩展通过调整相位旋转以使邻域相位差小于π，从而保持空间连续性。该算法使用模拟数据、钆掺杂体模和在 3T 和 9.4T 采集的活体脑、腹部和胸部数据集进行评估。将解缠性能与形态学启用偶极子反演-QSM 管道中使用的标准时间解缠算法进行比较，作为验证的基准。
使用数值体模进行定量评估显示，在大磁场梯度区域的解缠误差显著降低，并且解缠相位与在 9.4T 采集的钆掺杂体模数据中管瓶的线性浓度分布精确匹配。无需额外的空间解缠，迭代 VENyS 算法能够生成空间连续的相位图像。在活体数据中的应用导致了更好的解缠性能，特别是在具有大磁化率变化的区域，例如空气/组织界面。
迭代 VENyS 算法是一种在存在高磁场梯度的情况下用于多回波梯度回波相位的稳健解缠方法。
