标题： 空间角度表示学习用于扩散MRI中的高保真连续超分辨率 显示英文标题

标题： Spatial-Angular Representation Learning for High-Fidelity Continuous Super-Resolution in Diffusion MRI

摘要： 扩散磁共振成像（dMRI）由于成像硬件和系统噪声的固有限制，通常面临空间和角度分辨率低的问题，这会不利地影响微结构参数的准确估计，特别是精细解剖细节。基于深度学习的超分辨率技术在不增加采集时间的情况下显示出增强dMRI分辨率的潜力。然而，大多数现有方法仅限于空间或角度超分辨率，限制了它们在捕捉详细微结构特征方面的有效性。此外，传统的像素级损失函数难以恢复高分辨率重建中必要的复杂图像细节。为了解决这些挑战，我们提出了SARL-dMRI，一种用于dMRI高保真连续超分辨率的新颖空间-角度表示学习框架。SARL-dMRI探索隐式神经表示和球面谐波来建模连续的空间和角度表示，在同时增强空间和角度分辨率的同时提高微结构参数估计的准确性。为了进一步保持图像保真度，引入了一个数据保真模块和基于小波的频率损失，确保超分辨率图像与原始输入保持一致并保留细粒度细节。大量实验表明，与另外五种最先进的方法相比，我们的方法显著提高了dMRI数据的分辨率，提高了微结构参数估计的准确性，并提供了更好的泛化能力。它在45$\times$的下采样因子下仍保持稳定的性能。 显示英文摘要

摘要： Diffusion magnetic resonance imaging (dMRI) often suffers from low spatial and angular resolution due to inherent limitations in imaging hardware and system noise, adversely affecting the accurate estimation of microstructural parameters with fine anatomical details. Deep learning-based super-resolution techniques have shown promise in enhancing dMRI resolution without increasing acquisition time. However, most existing methods are confined to either spatial or angular super-resolution, limiting their effectiveness in capturing detailed microstructural features. Furthermore, traditional pixel-wise loss functions struggle to recover intricate image details essential for high-resolution reconstruction. To address these challenges, we propose SARL-dMRI, a novel Spatial-Angular Representation Learning framework for high-fidelity, continuous super-resolution in dMRI. SARL-dMRI explores implicit neural representations and spherical harmonics to model continuous spatial and angular representations, simultaneously enhancing both spatial and angular resolution while improving microstructural parameter estimation accuracy. To further preserve image fidelity, a data-fidelity module and wavelet-based frequency loss are introduced, ensuring the super-resolved images remain consistent with the original input and retain fine details. Extensive experiments demonstrate that, compared to five other state-of-the-art methods, our method significantly enhances dMRI data resolution, improves the accuracy of microstructural parameter estimation, and provides better generalization capabilities. It maintains stable performance even under a 45$\times$ downsampling factor.