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

从小型无参考数据中稳健而高效地学习高分辨率脑 MRI 重建

• 批准号: 10584324
• 负责人: Mehmet Akcakaya
• 金额: $ 53.06万
• 依托单位: UNIVERSITY OF MINNESOTA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584324
• 关键词: 3-Dimensional; Acceleration; Affect; Algorithms; Anatomy; Area; Artificial Intelligence; Attention; BRAIN initiative; Behavior Disorders; Behavioral; Brain; Brain Mapping; Brain imaging; Brain scan; Clinical Protocols; Data; Data Set; Databases; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Evaluation; Face; Four-dimensional; Functional Magnetic Resonance Imaging; Future; Health Care Costs; Healthcare; Healthcare Systems; Human; Image; Individual; Learning; MRI Scans; Magnetic Resonance Imaging; Maps; Measures; Memory; Mental disorders; Methodology; Methods; Minnesota; Modeling; Morphologic artifacts; Neurologic; Neurons; Pathology; Patients; Performance; Physics; Play; Process; Protocols documentation; Psyche structure; Recovery; Research; Resolution; Role; Sampling; Scanning; Series; Speed; Structure; Supervision; Symptoms; Techniques; Technology; Time; Training; Translations; Uncertainty; United States; Universities; Validation; Work; brain magnetic resonance imaging; connectome; data space; deep learning; denoising; diagnostic value; disability; disability-adjusted life years; image processing; image reconstruction; imaging modality; improved; learning strategy; millimeter; nervous system disorder; neural network; neuropsychiatry; novel; palliative; rapid technique; reconstruction; spatiotemporal; supervised learning; technology development; theories; tool; years of life lost; 3-Dimensional; Acceleration; Affect; Algorithms; Anatomy; Area; Artificial Intelligence; Attention; BRAIN initiative; Behavior Disorders; Behavioral; Brain; Brain Mapping; Brain imaging; Brain scan; Clinical Protocols; Data; Data Set; Databases; Development; Diagnosis; Diffusion; Diffusion Magnetic Resonance Imaging; Dimensions; Disease; Evaluation; Face; Four-dimensional; Functional Magnetic Resonance Imaging; Future; Health Care Costs; Healthcare; Healthcare Systems; Human; Image; Individual; Learning; MRI Scans; Magnetic Resonance Imaging; Maps; Measures; Memory; Mental disorders; Methodology; Methods; Minnesota; Modeling; Morphologic artifacts; Neurologic; Neurons; Pathology; Patients; Performance; Physics; Play; Process; Protocols documentation; Psyche structure; Recovery; Research; Resolution; Role; Sampling; Scanning; Series; Speed; Structure; Supervision; Symptoms; Techniques; Technology; Time; Training; Translations; Uncertainty; United States; Universities; Validation; Work; brain magnetic resonance imaging; connectome; data space; deep learning; denoising; diagnostic value; disability; disability-adjusted life years; image processing; image reconstruction; imaging modality; improved; learning strategy; millimeter; nervous system disorder; neural network; neuropsychiatry; novel; palliative; rapid technique; reconstruction; spatiotemporal; supervised learning; technology development; theories; tool; years of life lost

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.

项目摘要/摘要 神经精神病学（心理，行为和神经系统疾病）越来越多地主导着负担 美国医疗保健。然而，我们对这种疾病的理解在很大程度上仅限于症状的描述，以及 治疗仍然是姑息治疗的。几项大规模的努力，包括人类连接项目（HCP） 大脑倡议要求开发技术来绘制脑电路以改善我们的 了解大脑功能。磁共振成像（MRI）在这些计划中起着核心作用 强大的非侵入性方法研究人脑，包括解剖，功能和扩散 成像。但是，MRI方法对可实现的决议和获取速度有重大限制。这些 影响两个高分辨率的全脑习得，旨在成像仅包含少量体素的体量 千位神经元，以提高对大脑的理解，以及更常见的研究和 临床方案。反过来，这需要改进的重建方法以促进更快的收购。 已经提出了一些改进MRI数据重建的策略。最近，深度学习（DL） 已成为加速MRI的替代方法，显示出比常规方法提高的质量。 但是，它也面临着阻碍其实用性的挑战，尤其是在高分辨率的大脑MRI中，包括需求 对于用于培训的参考数据的大量数据库，关于概括而不是看不见病理的关注 在培训数据集，与恢复精细结构相关的鲁棒性问题以及困难方面有很好的代表 用于处理多维图像系列的培训网络。在此提案中，我们将开发和验证 高分辨率大脑DL MRI重建的强大而有效的学习策略，没有大数据库 参考数据。我们将开发自我监督的学习方法，以培训小型无引用 数据库或特定于扫描的方式。我们将通过不确定性指导的培训策略来扩大这些 改善不确定性高的区域的恢复，协同结合随机矩阵理论的方法 基于DL重建和记忆效率的分布式学习技术，以处理大型 图像系列。我们的发展将使加速度至少提高到现有的加速度至少两倍 协议，以及更高的分辨率。他们将在HCP风格的收购中得到验证 多种分辨率的解剖学，功能和微观结构评估。最后，我们将整体策划 大脑亚毫米计HCP风格的数据库，用于研究水平皮质的功能和结构连接性 层和列，同时还为新建模，图像处理和 重建算法。该项目的成功完成有可能改变量表 可以使用MRI成像，提高现有协议的质量和/或大大减少扫描时间， 导致降低医疗保健成本，改善诊断和/或增加患者吞吐量。