标题： 一种可调节的深度网络用于无模型扩散MRI配准 显示英文标题

标题： A Steerable Deep Network for Model-Free Diffusion MRI Registration

摘要： 非刚性配准对于医学图像分析至关重要，但由于扩散磁共振成像（dMRI）的高维和方向依赖性，仍然具有挑战性。 虽然经典方法准确，但计算成本高，而深度神经网络虽然高效，但在非刚性dMRI配准方面相比结构成像研究较少。 我们提出了一种新颖的深度学习框架，用于原始扩散MRI数据的无模型非刚性配准，不需要显式重新定向。 与之前依赖于衍生表示的方法（如扩散张量或纤维方向分布函数）不同，在我们的方法中，我们将配准公式化为位置和方向空间的等变微分同胚。 我们方法的核心是一个$\mathsf{SE}(3)$-等变UNet，它生成速度场的同时保持原始dMRI域的几何特性。 我们引入了一种基于傅里叶空间中最大均值差异的新损失函数，隐式地在图像之间匹配集合平均传播器。 在人类连接组计划dMRI数据上的实验结果表明，与最先进的方法相比具有竞争力，并且额外优势是避免了估计衍生表示的开销。 这项工作为在采集空间中直接进行数据驱动、几何感知的dMRI配准奠定了基础。 显示英文摘要

摘要： Nonrigid registration is vital to medical image analysis but remains challenging for diffusion MRI (dMRI) due to its high-dimensional, orientation-dependent nature. While classical methods are accurate, they are computationally demanding, and deep neural networks, though efficient, have been underexplored for nonrigid dMRI registration compared to structural imaging. We present a novel, deep learning framework for model-free, nonrigid registration of raw diffusion MRI data that does not require explicit reorientation. Unlike previous methods relying on derived representations such as diffusion tensors or fiber orientation distribution functions, in our approach, we formulate the registration as an equivariant diffeomorphism of position-and-orientation space. Central to our method is an $\mathsf{SE}(3)$-equivariant UNet that generates velocity fields while preserving the geometric properties of a raw dMRI's domain. We introduce a new loss function based on the maximum mean discrepancy in Fourier space, implicitly matching ensemble average propagators across images. Experimental results on Human Connectome Project dMRI data demonstrate competitive performance compared to state-of-the-art approaches, with the added advantage of bypassing the overhead for estimating derived representations. This work establishes a foundation for data-driven, geometry-aware dMRI registration directly in the acquisition space.