用扩散模型从低分辨率DWI数据重建高精度脑部成像。
QID$^2$: An Image-Conditioned Diffusion Model for Q-space Up-sampling of DWI Data
- 基于U-Net与交叉注意力，利用参考图像引导高角分辨DWI生成
- 在HCP数据集上重建效果优于两个SOTA GAN模型，提升张量估计精度
- 适合需要高质量DWI数据的临床与神经科学研究者
我们提出一种图像条件扩散模型QID²，用于从低角分辨率采集数据中估计高角分辨率扩散加权成像（DWI）。该模型以一组低角分辨率DWI数据为输入，利用其信息估计目标梯度方向对应的高角分辨率DWI数据。采用带交叉注意力的U-Net架构，以保留参考图像的位置信息，进一步指导目标图像生成。我们在人类连接组计划（HCP）数据集上对单壳层DWI样本进行训练与评估，具体通过子采样HCP梯度方向生成低角分辨率数据，并训练QID²以重建缺失的高角分辨率样本。与两种最先进的GAN模型相比，实验结果表明，QID²不仅生成图像质量更高，且在多个指标下持续优于GAN模型，提升了下游张量估计性能。本研究凸显了扩散模型在q空间上采样中的潜力，尤其展示了QID²在临床与科研应用中的前景。
原文摘要 · Abstract (English)
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.
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