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Abstract: diffusion tensor imaging (DTI) is a widely used imaging technique for mapping living human brain tissue’s microstructure and structural connectivity. Recently, deep learning methods have been proposed to rapidly estimate diffusion tensors (DTs) using only a small quantity of diffusion-weighted (DW) images. However, these methods typically use the DW images obtained with fixed q-space sampling schemes as the training data, limiting the application scenarios of such methods. To address this issue, we develop a new deep neural network called q-space-coordinate-guided diffusion tensor imaging (QCG-DTI), which can efficiently and correctly estimate DTs under flexible q-space sampling schemes. First, we propose a q-space-coordinate-embedded feature consistency strategy to ensure the correspondence between q-space-coordinates and their respective DW images. Second, a q-space-coordinate fusion (QCF) module is introduced which efficiently embeds q-space-coordinates into multiscale features of the corresponding DW images by linearly adjusting the feature maps along the channel dimension, thus eliminating the dependence on fixed diffusion sampling schemes. Finally, a multiscale feature residual dense (MRD) module is proposed which enhances the network's feature extraction and image reconstruction capabilities by using dual-branch convolutions with different kernel sizes to extract features at different scales. Compared to state-of-the-art methods that rely on a fixed sampling scheme, the proposed network can obtain high-quality diffusion tensors and derived parameters even using DW images acquired with flexible q-space sampling schemes. Compared to state-of-the-art deep learning methods, QCG-DTI reduces the mean absolute error by approximately 15% on fractional anisotropy and around 25% on mean diffusivity.

Q空间坐标引导的神经网络从最小数量扩散加权图像中实现高保真扩散张量估计

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