Towards Motion-Robust and Efficient Functional MRI Using Implicit Function Learning

使用内隐功能学习实现运动稳健且高效的功能 MRI

• 批准号: EP/Y002016/1
• 负责人: Chen Qin
• 金额: $ 20.75万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FY002016%2F1
• 关键词: Towards; Motion; Robust; Efficient; Functional

Functional magnetic resonance imaging (fMRI) is a leading modality to measure brain activity and connectivity. Clinically, it is starting to be used in pre-surgical planning and in assessment of brain function in vegetative state patients. It also recently shows promise in infant cognition research, which holds the key to understanding the origins and functions of the human brain. However, one of the main challenges that constrain the clinical applications of fMRI is its sensitivity to motion, where head movement causes highly deleterious artefacts in fMRI data and can be a major source of error in functional connectivity analysis. This is particularly challenging on infants between the ages of 2 and 48 months, where in many cases half of the data are discarded due to head movement, leading to significant delays and cost for repeated scans. The sustained increase in demand for it would lead to further increased pressures on hospital resources and reduced efficiency of the imaging workflows. Therefore, it is urgent to have techniques and tools to eliminate or reduce the motion effects on fMRI scans.Recently, machine learning (ML) and deep learning (DL) techniques have shown promise to alleviate motion corruption by learning from data to retrospectively correct the motion and artefacts. However, most of these learning-based methods do not specifically focus on fMRI and most existing motion correction approaches for static and structural MRI are not directly applicable to fMRI due to the high memory requirement and application-specific motion artefacts in fMRI. Therefore, there is still a lack of a robust and reliable technique for the problem. With the increasing need and availability of fMRI data and the growing cost for repeated scans due to motion, demand for motion-robust and efficient fMRI are becoming essential.We aim to fill the gap in fMRI research in this project by proposing to investigate motion-robust and efficient fMRI based on novel implicit function learning techniques. The proposed research will integrate and advance state-of-the-art research in machine learning and medical imaging. We will particularly consider motion correction of infant motion trajectories in this study, as infant motion causes substantial data loss in fMRI and represents the most necessary and urgent need. However, as there is not much low-motion infant data available and as they also cannot easily provide motion-free control for validation, we propose to use adult fMRIs for the initial feasibility study, where more data of low motion and better 'ground truth' control can be obtained. The project will create a novel implicit function learning method to learn a prior space for resolution-agnostic motion-free fMRI, investigate integration of the data-driven prior with instance-specific slice-to-volume registration for fast and adaptable motion correction in motion scenarios mimicking infant movement, and evaluate and validate the created approach on data of adult brain fMRI scans with and without infant-like motion. This will conduce to creation of a novel method that enables high-precision, memory-efficient and robust fMRI motion correction with resolution-agnostic volumetric reconstruction. Future research will be extended to infant fMRI given a promising outcome of the project.The project will contribute to knowledge in machine learning, medical imaging and computer vision, by advancing state-of-the-art in both the fundamental and applied research in the multi-disciplinary field. The project will also benefit clinicians and medical image processing researchers especially on fMRI and infant, offering them a fast and reliable motion correction tool that addresses the drawbacks of current techniques. The patients, the healthcare industry and the society will also benefit from the development in medical imaging technologies, with an improved healthcare system and economics resulting from it.

功能磁共振成像 (fMRI) 是测量大脑活动和连接性的主要方法。在临床上，它开始用于手术前计划和植物人状态患者的脑功能评估。最近它在婴儿认知研究中也显示出了前景，婴儿认知研究掌握着理解人脑起源和功能的关键。然而，限制 fMRI 临床应用的主要挑战之一是其对运动的敏感性，其中头部运动会导致 fMRI 数据中高度有害的伪影，并且可能是功能连接分析中的主要错误来源。这对于 2 至 48 个月大的婴儿来说尤其具有挑战性，在许多情况下，一半的数据由于头部运动而被丢弃，导致重复扫描的显着延迟和成本。需求的持续增长将导致医院资源压力进一步增大，影像工作流程效率降低。因此，迫切需要有技术和工具来消除或减少运动对功能磁共振成像扫描的影响。最近，机器学习（ML）和深度学习（DL）技术已显示出通过从数据中学习来回顾性纠正运动损坏的前景。运动和人工制品。然而，大多数基于学习的方法并不专门关注 fMRI，并且由于 fMRI 中的高内存要求和特定于应用的运动伪影，大多数现有的静态和结构 MRI 运动校正方法不能直接适用于 fMRI。因此，仍然缺乏解决该问题的稳健可靠的技术。随着功能磁共振成像数据的需求和可用性不断增加，以及由于运动而导致重复扫描的成本不断增加，对运动稳健和高效的功能磁共振成像的需求变得至关重要。我们的目标是通过提出研究运动功能磁共振成像来填补该项目中功能磁共振成像研究的空白。基于新颖的隐函数学习技术的稳健且高效的功能磁共振成像。拟议的研究将整合并推进机器学习和医学成像领域的最先进研究。在本研究中，我们将特别考虑婴儿运动轨迹的运动校正，因为婴儿运动会导致功能磁共振成像中的大量数据丢失，并且代表了最必要和最紧迫的需求。然而，由于可用的低运动婴儿数据并不多，而且它们也不能轻易提供用于验证的无运动控制，我们建议使用成人功能磁共振成像进行初步可行性研究，其中更多的低运动数据和更好的“地面实况” ' 可以获得控制权。该项目将创建一种新颖的隐函数学习方法，以学习与分辨率无关的无运动 fMRI 的先验空间，研究数据驱动先验与特定于实例的切片到体积配准的集成，以实现运动中快速且适应性强的运动校正模拟婴儿运动的场景，并评估和验证所创建的方法，该方法针对有或没有类似婴儿运动的成人大脑功能磁共振成像扫描数据。这将有助于创建一种新颖的方法，通过与分辨率无关的体积重建来实现高精度、内存效率高和鲁棒的功能磁共振成像运动校正。鉴于该项目取得了良好的成果，未来的研究将扩展到婴儿功能磁共振成像。该项目将通过推进基础研究和应用研究的最先进水平，为机器学习、医学成像和计算机视觉方面的知识做出贡献。多学科领域。该项目还将使临床医生和医学图像处理研究人员受益，特别是功能磁共振成像和婴儿方面，为他们提供快速可靠的运动校正工具，解决当前技术的缺点。患者、医疗行业和社会也将受益于医学影像技术的发展，从而改善医疗体系和经济。