■扩散加权成像(DWI)是一种非侵入性方法,用于研究大脑的微观结构特性。然而,在临床实践中,分辨率和扫描时间之间存在权衡。超分辨率已用于增强自然图像中的空间分辨率,但其在高维和非欧几里得DWI上的应用仍然具有挑战性。
■本研究旨在开发端到端的深度学习网络,以通过后处理增强DWI的空间分辨率。
■我们提出了一种空间定制的深度学习方法,该方法将卷积神经网络(CNN)用于网格结构域(x空间),将图CNN(GCNN)用于扩散梯度域(q空间)。此外,我们使用q空间中高斯核定义的相关性将CNN的输出表示为图形,以弥合CNN和GCNN特征格式之间的差距。
■我们的模型在HumanConnectome项目中进行了评估,用我们提出的方法证明了DWI质量的有效提高。扩展实验还强调了其在下游任务中的优势。
■混合卷积神经网络在增强DWI扫描的空间分辨率以用于异构空间数据的特征学习方面表现出明显的优势。
UNASSIGNED: Diffusion-weighted imaging (DWI) is a noninvasive method used for investigating the microstructural properties of the brain. However, a tradeoff exists between resolution and scanning time in clinical practice. Super-resolution has been employed to enhance spatial resolution in natural images, but its application on high-dimensional and non-Euclidean DWI remains challenging.
UNASSIGNED: This study aimed to develop an end-to-end deep learning network for enhancing the spatial resolution of DWI through post-processing.
UNASSIGNED: We proposed a space-customized deep learning approach that leveraged convolutional neural networks (CNNs) for the grid structural domain (x-space) and graph CNNs (GCNNs) for the diffusion gradient domain (q-space). Moreover, we represented the output of CNN as a graph using correlations defined by a Gaussian kernel in q-space to bridge the gap between CNN and GCNN feature formats.
UNASSIGNED: Our model was evaluated on the Human Connectome Project, demonstrating the effective improvement of DWI quality using our proposed method. Extended experiments also highlighted its advantages in downstream tasks.
UNASSIGNED: The hybrid convolutional neural network exhibited distinct advantages in enhancing the spatial resolution of DWI scans for the feature learning of heterogeneous spatial data.