We propose an image-conditioned diffusion model to estimate high angular resolution diffusion weighted imaging (DWI) from a low angular resolution acquisition. Our model, which we call QID$^2$, takes as input a set of low angular resolution DWI data and uses this information to estimate the DWI data associated with a target gradient direction. We leverage a U-Net architecture with cross-attention to preserve the positional information of the reference images, further guiding the target image generation. We train and evaluate QID$^2$ on single-shell DWI samples curated from the Human Connectome Project (HCP) dataset. Specifically, we sub-sample the HCP gradient directions to produce low angular resolution DWI data and train QID$^2$ to reconstruct the missing high angular resolution samples. We compare QID$^2$ with two state-of-the-art GAN models. Our results demonstrate that QID$^2$ not only achieves higher-quality generated images, but it consistently outperforms the GAN models in downstream tensor estimation across multiple metrics. Taken together, this study highlights the potential of diffusion models, and QID$^2$ in particular, for q-space up-sampling, thus offering a promising toolkit for clinical and research applications.

本研究解决了低角分辨率扩散加权成像（DWI）数据上采样的问题，提出了一种名为QID$^2$的图像条件扩散模型。通过利用U-Net架构和交叉注意机制，该模型能够估计出目标梯度方向下的高角分辨率DWI数据。实验结果表明，QID$^2$在生成图像质量和后续张量估计指标上均优于现有的两种GAN模型，展示了扩散模型在Q空间上采样中的潜力。

QID$^2$: 一种用于DWI数据Q空间上采样的图像条件扩散模型