Abstract：
Objective. Most deep neural network-based diffusion tensor imaging methods require the diffusion gradients\' number and directions in the data to be reconstructed to match those in the training data. This work aims to develop and evaluate a novel dynamic-convolution-based method called FlexDTI for highly efficient diffusion tensor reconstruction with flexible diffusion encoding gradient scheme.Approach. FlexDTI was developed to achieve high-quality DTI parametric mapping with flexible number and directions of diffusion encoding gradients. The method used dynamic convolution kernels to embed diffusion gradient direction information into feature maps of the corresponding diffusion signal. Furthermore, it realized the generalization of a flexible number of diffusion gradient directions by setting the maximum number of input channels of the network. The network was trained and tested using datasets from the Human Connectome Project and local hospitals. Results from FlexDTI and other advanced tensor parameter estimation methods were compared.Main results. Compared to other methods, FlexDTI successfully achieves high-quality diffusion tensor-derived parameters even if the number and directions of diffusion encoding gradients change. It reduces normalized root mean squared error by about 50% on fractional anisotropy and 15% on mean diffusivity, compared with the state-of-the-art deep learning method with flexible diffusion encoding gradient scheme.Significance. FlexDTI can well learn diffusion gradient direction information to achieve generalized DTI reconstruction with flexible diffusion gradient scheme. Both flexibility and reconstruction quality can be taken into account in this network.
摘要：
目的：＆＃xD;大多数基于深度神经网络的扩散张量成像方法需要重建数据中的扩散梯度\'数量和方向以匹配训练数据中的那些。这项工作旨在开发和评估一种新颖的基于动态卷积的方法，称为FlexDTI，用于使用灵活的扩散编码梯度方案进行高效的扩散张量重建。＆＃xD;方法：＆＃xD;FlexDTI的开发是为了实现具有灵活数量和方向的扩散编码梯度的高质量DTI参数映射。该方法使用动态卷积核将扩散梯度方向信息嵌入到相应扩散信号的特征图中。此外，它通过设置网络的最大输入通道数，实现了对多个灵活扩散梯度方向的泛化。该网络使用HumanConnectome项目和当地医院的数据集进行了培训和测试。比较了FlexDTI和其他高级张量参数估计方法的结果。
主要结果:
与其他方法相比,即使扩散编码梯度的数量和方向发生变化，FlexDTI也成功地实现了高质量的扩散张量导出参数。它减少了归一化均方根误差（NRMSE）约50％的分数各向异性（FA）和15％的平均扩散率（MD），与具有灵活扩散编码梯度方案的最先进的深度学习方法相比。＆＃xD;意义：＆＃xD;FlexDTI可以很好地学习扩散梯度方向信息，以灵活的扩散梯度方案实现广义DTI重建。在该网络中可以考虑灵活性和重建质量。
.
