发表机构
Hawkes Institute; University College London（霍克斯研究所; 伦敦大学学院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一种融入b-值依赖的超网络控制SCNN架构，实现b-值与b-向量泛化及旋转等变性，提升了脑组织微结构估计的鲁棒性与临床适用性。
AI 中文摘要
利用机器学习进行脑组织微结构估计，比传统拟合方法具有更高的计算效率，但机器学习仍存在阻碍其临床应用的重要局限：当前模型通常缺乏对扩散MRI采集协议的泛化能力，且每当b-向量或b-值变化时都需要重新训练；近期为解决协议泛化问题开发的机器学习方法缺乏旋转等变性。球形卷积神经网络（SCNN）是适用于dMRI参数估计的几何深度学习模型，可保证旋转等变性和b-向量泛化，但该架构目前未考虑b-值。因此，获得兼具协议泛化和旋转等变性的模型仍是开放性挑战。本文通过超网络将显式的b-值依赖关系融入SCNN架构，以NODDI作为前向模型示例估计脑组织微结构。为评估b-值泛化能力，将原始SCNN和新提出的SCNN架构在合成数据上训练，在不同b-值对的合成数据和真实数据上测试。结果显示，所提方法在合成数据上的RMSE和偏差更低，在真实数据上与传统NODDI拟合的一致性更高，表明其对未见b-值的鲁棒性提升，且降低了重新训练的需求。该框架结合了b-向量泛化、b-值泛化和旋转等变性，提升了深度学习在临床扩散MRI参数估计中的适用性，代码可在https URL_generalisedSCNN获取。
英文摘要
Brain tissue microstructure estimation with machine learning provides higher computational efficiency than conventional fitting. However, machine learning still presents important limitations that hamper its clinical utility. Specifically, current models typically lack generalisation across diffusion MRI acquisition protocols and require retraining whenever b-vectors or b-values change. Moreover, the recent machine learning methods that were developed to address protocol generalisation lack rotational equivariance. Particularly suitable for dMRI parameter estimation is a geometric deep learning model known as spherical convolutional neural network (SCNN), which guarantees rotational equivariance and b-vector generalisation. However, this architecture currently does not account for b-values. Therefore, obtaining a model that combines protocol generalisation and rotational equivariance remains an open challenge. In this paper, we directly address this issue by incorporating explicit b-value dependence into an SCNN architecture via a hypernetwork. This new approach is illustrated using NODDI as an example forward model for estimating brain tissue microstructure. To evaluate b-value generalisation, the original and newly proposed SCNN architectures are trained on synthetic data and tested on both synthetic and real data across different b-value pairs. Results demonstrate that the proposed method achieves reduced RMSE and bias on synthetic data, as well as higher agreement with conventional NODDI fitting on real data, indicating improved robustness to unseen b-values and a reduced need for retraining. By combining generalisation across b-values with generalisation across b-vectors and rotational equivariance, the proposed framework enhances the applicability of deep learning to clinical diffusion MRI parameter estimation. Code available at https://github.com/aerdnairo/arXiv\_generalisedSCNN.
