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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) only using 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 developed 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. On this basis, a q-space-coordinate fusion module (QCF) is introduced, which efficiently embeds q-space coordinates into multiscale features of the corresponding DW images by linearly adjusting the feature maps, thus eliminating the dependence on fixed diffusion sampling schemes. Finally, a multiscale feature residual dense module (MRD) 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 sample 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 reduced the mean absolute error by approximately 15% on fractional anisotropy and around 25% on mean diffusivity.