Optimising Neuroimaging Biomarkers for Dementia Using Deep Learning

1)简要描述研究背景，包括潜在影响痴呆症是全球发病率和死亡率的主要原因，但成功的治疗方法很少，机制理解不完整。神经影像学在我们对痴呆症的理解和治疗中起着关键作用，并且已经提出了许多反映大脑健康的生物标志物。然而，这些神经成像生物标志物目前受到广泛性差、对不同类型和质量数据的鲁棒性不足、计算速度慢和任意处理方法的限制。尽管神经成像生物标志物有许多潜在的好处，但这些局限性阻碍了它们在临床试验和临床实践中的应用。本项目旨在利用新兴的深度学习技术来克服上述限制。它将涉及核磁共振成像（MRI）生物标志物的分析，包括全脑体积、海马体积、脑室大小、皮质厚度和白质病变。将开发基于深度学习的新图像分割管道来生成这些生物标记物。目的是证明这些生物标志物在痴呆症研究和临床试验中的可靠性、有效性和稳健性。该项目涉及开发新的神经网络方法，例如使用生成网络对t1加权和t2加权MRI扫描进行合成图像增强，以增强对不同类型和质量数据的鲁棒性。本研究还将探索新兴的网络架构，特别是视觉转换器，它应用注意力机制来区分神经成像数据各部分的重要性。这些新的图像分析管道将使用各种本地和公共痴呆症MRI数据集进行验证，以确定所产生的生物标志物是否会增加对疾病影响的敏感性，更好地预测疾病进展和治疗反应。4)与EPSRC的战略和研究领域保持一致EPSRC的目标是“改变健康和医疗保健”，发展“人工智能（AI）、数字化和数据”。人口老龄化使更多的人面临患痴呆症的风险。该项目可以帮助临床医生更好地了解痴呆症，让患者得到更及时和准确的诊断，并为痴呆症治疗的发展提供更多的支持性证据。该项目将探索MRI分析中不同的神经网络架构和方法，这与EPSRC的两个研究领域——医学成像和人工智能——很好地结合在一起，作为医疗技术主题的一部分。5)涉及任何公司或合作者

1) Brief description of the context of the research including potential impactDementia is a leading cause of global morbidity and mortality, yet successful treatments are scarce and mechanistic understanding is incomplete. Neuroimaging plays a key role in our understanding and treatment of dementia, and many biomarkers reflecting the brain's health have been proposed. However, these neuroimaging biomarkers are currently limited by poor generalisability, inadequate robustness to varying types and quality of data, slow computation speeds and arbitrary processing methods. Despite the many potential benefits of neuroimaging biomarkers, these limitations have hindered their use in clinical trials and clinical practice.2) Aims and Objectives This project aims to use emerging deep-learning techniques to overcome the above limitations. It will involve the analysis of magnetic resonance imaging (MRI) biomarkers, including whole-brain volume, hippocampal volume, ventricle size, cortical thickness, and white matter lesions. New image segmentation pipelines, based on deep learning, will be developed to generate these biomarkers. The goal is to demonstrate increased reliability, validity, and robustness of these biomarkers for use in dementia research and clinical trials.3) Novelty of Research Methodology The project involves developing novel neural-network methods, such as synthetic image augmentation on T1-weighted and T2-weighted MRI scans using generative networks, to enhance the robustness to varying types and quality of data. This research will also explore emerging network architectures, especially vision transformers which applies attention mechanism to differentially weighting the significance of each part of the neuroimaging data. These new image analysis pipelines will be validated using various local and public dementia MRI datasets to establish whether the resulting biomarkers increase sensitivity to disease effects, better predict disease progression and treatment response.4) Alignment to EPSRC's strategies and research areas EPSRC aims to "Transform Health and Healthcare" and develop "Artificial Intelligence (AI), Digitalisation and Data". Population ageing exposes more people to the risk of dementia. This project could help clinicians have a better understanding of dementia, allow patients to have a more timely and accurate diagnosis, and provide more supportive evidence in the development of dementia treatments. The project will explore different neural network architectures and methods in MRI analysis, which align well with two research areas in EPSRC - medical imaging and artificial intelligence, as part of the Healthcare Technologies theme.5) Any companies or collaborators involved None