Machine learning approaches to enabling ultra-fast diagnostic MRI protocols for neurology

1. 简要描述研究背景，包括潜在影响磁共振成像（MRI）已被证明是广泛神经系统疾病的诊断成像方法选择。然而，由于其费用和获取图像所需的时间较长，与CT等竞争方式相比，它的使用频率较低。此外，已安装的核磁共振成像仪的数量低于CT扫描仪的数量，许多医疗机构的旧机器无法提供最新的高质量成像。尽管在许多情况下，MRI是更合适的选择，具有更高的诊断敏感性和特异性，但MRI的使用频率低于CT。这对阿尔茨海默病（AD）的诊断有重要影响，NICE指南规定使用结构成像来排除认知能力下降的可逆原因并协助亚型诊断。理想情况下，这意味着安排MRI扫描，因为它缺乏电离辐射，具有良好的软组织对比，并且在识别血管性痴呆或亚型不确定时优于其他成像技术。与CT不同，这些信息有助于鉴别诊断，从而改变治疗方法并增强预后。如果MRI扫描的扫描时间、可用性和成本与CT扫描相当，那么它的好处意味着几乎所有病例都可以使用MRI。解决这些问题的关键是大幅减少阿尔茨海默病诊断扫描的持续时间。在整个诊断路径中，更短的扫描时间更容易安排，从而提高患者和提供者获得适当成像的可用性。更短的扫描也更便宜，因为成本在很大程度上是由扫描时间决定的。随着焦虑的减少，患者在扫描仪上的时间也会减少，工作人员可以有更多的时间。然而，到目前为止，实现更短的时间一直是个问题，因为所需的扫描时间减少会导致不可接受的图像质量下降。此外，如果旧的核磁共振成像机采集的质量可以提高，那么合适扫描的总体可用性也会增加。这个博士项目的一个关键科学挑战是开发一种新的超快速MRI和机器学习方法的组合，用于重建和分析，可以提供与传统诊断MRI相同的诊断信息。2. 目的和目标具体目标是：评估现有的机器学习方法以加速MRI采集开发和评估图像质量转移（IQT）方法应用于MR图像重建和质量改进应用和评估IQT方法用于检测阿尔茨海默病的超快速获得MRI扫描应用和评估IQT方法用于检测阿尔茨海默病的超低场获得MRI扫描开发和维护工具在转化研究环境中有效应用IQT和相关方法的工作流程。研究方法的新颖性到目前为止，还没有使用图像质量转移方法来加速痴呆症扫描的尝试。从严重退化的快速扫描中创建标准护理图像（T1, T2， SWI）的挑战将需要开发新的机器学习方法，在临床图像上实施，并随后进行评估。4. 与EPSRC的战略和研究领域保持一致与EPSRC的人工智能和医疗保健技术主题保持一致有可能与核磁共振扫描仪制造商合作，但这还有待证实。

1. Brief description of the context of the research including potential impactMagnetic resonance imaging (MRI) is proven as the diagnostic imaging method of choice for a wide range of neurological conditions. However, it is used less often than competing modalities such as CT due to its expense and the longer time taken to acquire images. Additionally, the number of installed MRI machines is lower than the number of CT scanners, with many healthcare organisations having older machines not capable of the latest high-quality imaging. This mixture of challenges around the use of MRI means that it is used less often than CT, even though in many cases it is the more appropriate choice, providing greater diagnostic sensitivity and specificity.An important area where this has impact is in the diagnosis of Alzheimer's disease (AD) where NICE guidelines specify using structural imaging to rule out reversible causes of cognitive decline and to assist with subtype diagnosis. Ideally, this means scheduling an MRI scan, due to its lack of ionising radiation, excellent soft-tissue contrast, and superiority over other imaging techniques in identifying vascular dementia or when the subtype is uncertain. This information allows differential diagnosis, which may alter management and enhance prognostication, unlike CT.If the scan time, availability, and cost for an MRI scan were comparable to a CT scan, its benefits mean that MRI would be used in almost all cases. The key to solving each of these problems is a substantial reduction in the duration of a diagnostic scan for Alzheimer's disease. Shorter scans would be easier to schedule in the overall diagnostic patient pathway, thereby improving availability of appropriate imaging to patients and providers. Shorter scans are also less expensive, as cost is driven to a large degree by the scan time. The patient experience would also be improved by less time in the scanner as anxiety is reduced, and more time is available for staff. However, achieving shorter times has been problematic to date because the required scan time reduction leads to unacceptable image quality degradation. Additionally, if older MRI machines acquisitions could be improved in quality, then the overall availability of suitable scanning would also be increased. A key scientific challenge of this PhD project is the development of a combination of new ultra-fast MRI and machine learning methods for reconstruction and analysis that can provide equivalent diagnostic information to conventional diagnostic MRI. 2. Aims and Objectives The specific objectives are to:Evaluate existing machine learning methods to accelerate MRI acquisitionDevelop and evaluate the use of Image Quality Transfer (IQT) methods as applied to MR image reconstruction and quality improvementApply and assess IQT methods on ultra-fast acquired MRI scans for the detection of Alzheimer's diseaseApply and assess IQT methods on ultra-low field acquired MRI scans for the detection of Alzheimer's diseaseDevelop and maintain tools and workflows for efficient application of IQT and related methods in a translational research environment.3. Novelty of Research MethodologyUntil now there has been no previous attempt of using Image Quality Transfer methods to enable accelerated scans for dementia. The challenge of creating standard of care images (T1, T2, SWI) from significantly degraded rapid scans will require new machine learning methods to be developed, implemented on clinical images, and subsequently evaluated. 4. Alignment to EPSRC's strategies and research areasAligned with the EPSRC themes on Artificial Intelligence and Healthcare Technologies5. Any companies or collaborators involvedThere is a possibility that there may be collaboration with MRI scanner manufactures, but this is yet to be confirmed.