Kreher B W, Schneider J F, Mader I, Martin E, Hennig J, Il'yasov K A
Medical Physics, Department of Diagnostic Radiology, University Hospital, Freiburg, Germany.
Magn Reson Med. 2005 Nov;54(5):1216-25. doi: 10.1002/mrm.20670.
A multidiffusion-tensor model (MDT) is presented containing two anisotropic and one isotropic diffusion tensors. This approach has the ability to detect areas of fiber crossings and resolve the direction of crossing fibers. The mean diffusivity and the ratio of the tensor compartments were merged to one independent parameter by fitting MDT to the diffusion-weighted intensities of a two-point data acquisition scheme. By an F-test between the errors of the standard single diffusion tensor and the more complex MDT, fiber crossings were detected and the more accurate model was chosen voxel by voxel. The performance of crossing detection was compared with the spherical harmonics approach in simulations as well as in vivo. Similar results were found in both methods. The MDT model, however, did not only detect crossings but also yielded the single fiber directions. The FACT algorithm and a probabilistic connectivity algorithm were extended to support the MDT model. For example, a mean angular error smaller than 10 degrees was found for the MDT model in a simulated fiber crossing with an SNR of 80. By tracking the corticospinal tract the MDT-based tracks reached a significantly greater area of the gyrus precentralis.
提出了一种包含两个各向异性和一个各向同性扩散张量的多扩散张量模型（MDT）。这种方法能够检测纤维交叉区域并解析交叉纤维的方向。通过将MDT拟合到两点数据采集方案的扩散加权强度，将平均扩散率和张量分量的比率合并为一个独立参数。通过对标准单扩散张量和更复杂的MDT的误差进行F检验，逐体素检测纤维交叉并选择更准确的模型。在模拟以及体内实验中，将交叉检测的性能与球谐函数方法进行了比较。两种方法得到了相似的结果。然而，MDT模型不仅能检测交叉，还能得出单纤维方向。扩展了FACT算法和概率连接算法以支持MDT模型。例如，在信噪比为80的模拟纤维交叉中，MDT模型的平均角度误差小于10度。通过追踪皮质脊髓束，基于MDT的轨迹到达中央前回的显著更大区域。
