Robust and Efficient Learning of High-Resolution Brain MRI Reconstruction from Small Referenceless Data

项目总结/摘要 神经精神（精神，行为和神经）障碍越来越多地占主导地位的负担 美国医疗保健然而，我们对这种疾病的理解在很大程度上限于对症状的描述， 治疗仍然是姑息性的。几项大规模的努力，包括人类连接组计划（HCP） 和大脑倡议呼吁开发技术来绘制大脑回路，以改善我们的大脑。 了解大脑功能。磁共振成像（MRI）在这些举措中发挥着核心作用， 强大的非侵入性方法来研究人脑，包括解剖，功能和扩散 显像然而，MRI方法在可实现的分辨率和采集速度上具有主要限制。这些 影响高分辨率全脑采集， 上千个神经元，以提高对大脑的理解，也是更常用的研究和 临床方案。这反过来又需要改进重建方法，以促进更快的采集。 已经提出了几种策略来改进MRI数据的重建。最近，深度学习（DL） 已经成为加速MRI的替代方案，显示出比传统方法更高的质量。 然而，它也面临着阻碍其实用性的挑战，特别是在高分辨率脑MRI中，包括需要 对于用于训练的参考数据的大型数据库，对泛化到看不见的病理的关注不 在训练数据集中表现良好，与精细结构恢复相关的鲁棒性问题，以及 用于处理多维图像序列的训练网络。在本提案中，我们将开发和验证 用于高分辨率脑DL MRI重建的强大而有效的学习策略，无需大型数据库 的参考数据。我们将开发自监督学习方法，用于小规模无参考的训练 数据库或以扫描特定的方式。我们将通过不确定性指导的训练策略来增强这些能力， 具有高不确定性的区域的改进的恢复、用于协同地组合随机矩阵理论的方法 基于DL重建的去噪，以及内存高效的分布式学习技术， 图像系列。我们的发展将使加速率至少比现有的提高两倍 协议和更高的分辨率。它们将在HCP风格的收购中得到验证， 以多种分辨率进行解剖、功能和显微结构评价。最后，我们将策划一个整体 用于研究皮层水平功能和结构连接的脑亚毫米HCP型数据库 层和列，同时也促进了新的建模，图像处理和 重建算法该项目的成功完成有可能改变 可以用MRI成像，提高现有协议的质量和/或显著减少扫描时间， 从而导致医疗保健成本的降低、诊断的改善和/或患者吞吐量的增加。

PROJECT SUMMARY/ABSTRACT Neuropsychiatric (mental, behavioral and neurological) disorders are increasingly dominating the burden on US healthcare. Yet, our understanding of such disorders is largely restricted to a description of symptoms, and the treatments remain palliative. Several large-scale efforts, including the Human Connectome Project (HCP) and the BRAIN Initiative call for the development of technologies to map brain circuits to improve our understanding of brain function. Magnetic resonance imaging (MRI) plays a central role in these initiatives as a powerful non-invasive methodology to study the human brain, including anatomical, functional and diffusion imaging. Yet, MRI methods have major limitations on achievable resolutions and acquisition speed. These affect both high resolution whole brain acquisitions that aim to image voxel volumes that contain only a few thousand neurons for improved understanding of the brain, and also the more commonly utilized research and clinical protocols. This, in turn, necessitates improved reconstruction methods to facilitate faster acquisitions. Several strategies have been proposed for improved reconstruction of MRI data. Recently, deep learning (DL) has emerged as an alternative for accelerated MRI showing improved quality over conventional approaches. However, it also faces challenges that hinder its utility, especially in high-resolution brain MRI, including need for large databases of reference data for training, concerns about generalization to unseen pathologies not well-represented in training datasets, robustness issues related to recovery of fine structures, and difficulties in training networks for processing multi-dimensional image series. In this proposal, we will develop and validate robust and efficient learning strategies for high-resolution brain DL MRI reconstruction without large databases of reference data. We will develop self-supervised learning methods for training with small referenceless databases or in a scan-specific manner. We will augment these with uncertainty-guided training strategies for improved recovery of areas with high uncertainty, methods for synergistically combining random matrix theory based denoising with DL reconstruction, and memory-efficient distributed learning techniques to process large image series. Our developments will enable at least a two-fold improvement in acceleration rates over existing protocols, and at higher resolutions. They will be validated on HCP-style acquisitions with extensive anatomical, functional and microstructural evaluation at multiple resolutions. Finally, we will curate a whole brain sub-millimeter HCP-style database for studying functional and structural connectivity at the level cortical layers and columns, while also facilitating technical developments for new modeling, image processing and reconstruction algorithms. Successful completion of this project has the potential to transform the scales that can be imaged with MRI, improve the quality of existing protocols and/or significantly reduce scan times, leading to reductions in healthcare costs, improved diagnosis and/or increased patient throughput.