Machine learning approaches to enabling ultra-fast diagnostic MRI protocols for neurology
机器学习方法为神经病学提供超快速诊断 MRI 协议
基本信息
- 批准号:2599861
- 负责人:
- 金额:--
- 依托单位:
- 依托单位国家:英国
- 项目类别:Studentship
- 财政年份:2021
- 资助国家:英国
- 起止时间:2021 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
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.
1.研究背景的简要描述,包括潜在的影响磁共振成像(MRI)被证明是广泛的神经系统疾病的诊断成像方法的选择。然而,由于其费用和采集图像所需的时间较长,它的使用频率低于竞争模式(如CT)。此外,已安装的MRI机器的数量低于CT扫描仪的数量,许多医疗机构的旧机器无法进行最新的高质量成像。MRI的使用面临着诸多挑战,这意味着它的使用频率低于CT,尽管在许多情况下它是更合适的选择,提供了更高的诊断灵敏度和特异性。这一影响的一个重要领域是阿尔茨海默病(AD)的诊断,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,良好的软组织对比度,以及在识别血管性痴呆或亚型不确定时优于其他成像技术。这些信息可以进行鉴别诊断,这可能会改变管理和增强诊断,不像CT。如果MRI扫描的扫描时间,可用性和成本与CT扫描相当,它的好处意味着MRI将在几乎所有的情况下使用。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。更短的扫描将更容易在整个诊断患者路径中安排,从而提高患者和提供者适当成像的可用性。较短的扫描也较便宜,因为成本在很大程度上由扫描时间驱动。患者体验也将得到改善,因为焦虑减少,扫描仪中的时间减少,工作人员有更多的时间可用。然而,迄今为止,实现更短的时间是有问题的,因为所需的扫描时间减少导致不可接受的图像质量劣化。此外,如果旧的MRI机器采集的质量可以得到改善,那么适当扫描的整体可用性也将增加。该博士项目的一个关键科学挑战是开发新的超快速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等效的诊断信息。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)方法在MR图像重建和质量改进中的应用在超快速采集的MRI扫描中应用和评估IQT方法以检测阿尔茨海默病在超低场采集的MRI扫描中应用和评估IQT方法以检测阿尔茨海默病开发和维护工具和工作流程,以便在转化研究环境中有效应用IQT和相关方法。3.研究方法的新奇到目前为止,还没有人尝试使用图像质量转移方法来加速痴呆症的扫描。从严重退化的快速扫描创建标准护理图像(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关于人工智能和医疗保健技术的主题保持一致5。任何公司或合作者参与有可能有可能与MRI扫描仪制造商合作,但这还有待证实。
项目成果
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其他文献
吉治仁志 他: "トランスジェニックマウスによるTIMP-1の線維化促進機序"最新医学. 55. 1781-1787 (2000)
Hitoshi Yoshiji 等:“转基因小鼠中 TIMP-1 的促纤维化机制”现代医学 55. 1781-1787 (2000)。
- DOI:
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LiDAR Implementations for Autonomous Vehicle Applications
- DOI:
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2021 - 期刊:
- 影响因子:0
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吉治仁志 他: "イラスト医学&サイエンスシリーズ血管の分子医学"羊土社(渋谷正史編). 125 (2000)
Hitoshi Yoshiji 等人:“血管医学与科学系列分子医学图解”Yodosha(涉谷正志编辑)125(2000)。
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Effect of manidipine hydrochloride,a calcium antagonist,on isoproterenol-induced left ventricular hypertrophy: "Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,K.,Teragaki,M.,Iwao,H.and Yoshikawa,J." Jpn Circ J. 62(1). 47-52 (1998)
钙拮抗剂盐酸马尼地平对异丙肾上腺素引起的左心室肥厚的影响:“Yoshiyama,M.,Takeuchi,K.,Kim,S.,Hanatani,A.,Omura,T.,Toda,I.,Akioka,
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