论文标题
MRI信号处理的一种新方法，用于检测大脑的轴突组织
A New Approach in MRI Signal Processing for Detecting the Axonal Organization of the Brain
论文作者
论文摘要
本文介绍了一种新的方法，用于重建扩散MRI中大脑的白质纤维途径。通常，信号强度值在较高的扩散率方向上会较小。提出的方法选择了信号强度小的扩散灵敏度梯度方向（DSGD）。考虑到这些作为最大扩散率的方向，我们生成的方向均匀分布在挑选的DSGD周围。这些新计算的统一间隔方向被认为是重建过程中使用的梯度方向。诸如统一梯度方向（UGD）之类的最先进的方案在梯度方向上具有冗余，并且自适应梯度方向（AGD）的限制是每个Voxel两次求解线性系统。这两个局限性在本研究中拒绝了。用建议的方案估算梯度方向在多室混合模型中用于计算纤维方向。对实际数据的仿真和实验评估了所提出的方法的可行性。
This article introduces a new methodology for reconstructing the white matter fiber pathways of brain in diffusion MRI. Usually, the signal intensity values will be lesser in the direction of higher diffusivity. The proposed approach picks the diffusion sensitivity gradient directions (dSGD), where the signal intensities are diminutive. Considering these as the directions of maximum diffusivity, we generate directions uniformly distributed around the picked dSGD. These newly computed uniformly spaced directions are considered gradient directions used in the reconstruction process. The state-of-art schemes like uniform gradient direction (UGD) have redundancy in the gradient direction, and adaptive gradient direction (AGD) has a constraint of solving linear system twice per voxel. These two limitations are turned down in this study simultaneously. Estimating gradient directions with the proposed scheme is employed in the multi-compartmental mixture models for calculating the fiber orientations. Simulation and experiments on the real data evaluate the feasibility of the proposed method.
