Towards 10-Minute Magnetic Resonance Imaging Scans in Children with Machine Learning

利用机器学习对儿童进行 10 分钟磁共振成像扫描

• 批准号: 2713593
• 金额: --
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2713593
• 关键词: Towards; 10; Minute; Magnetic; Resonance

"1) Magnetic Resonance Imaging (MRI) has enabled significant advances in the diagnosis and management of many childhood diseases. However, MRI is challenging in the pediatric population as it is time consuming (~1 hour to perform) and requires patient cooperation. Hence it is often necessary to use general anesthesia (GA) in children below 8 years of age, which is both costly and carries some risk. One way of overcoming these problems would be to speed up the MRI scans so children do not have to keep still or hold their breath. The simplest way of doing this is to acquire less data (data undersampling), however this results in artefacts that make the images unusable. Current reconstruction methods for removing these artefacts, allow limited acceleration, or use time consuming algorithms which hamper their clinical uptake. A new approach is Machine Learning that aims to 'learn' how to remove undersampling, as well as motion artefacts. 2) By the end of this project, the student will have an excellent understanding of machine learning algorithms particularly for reconstruction of MRI data. The student will also be able to use an MRI scanner, understand MRI sequence design, as well as traditional and state-of-the-art MRI reconstruction algorithms. This is a very translational project and will include working closely with clinical partners. All work packages will be integrated into standard clinical workflow to enable clinical validation studies, and simple translation into routine clinical practise.3) This study aims to develop novel accelerated magnetic resonance imaging (MRI) technologies which will allow scan times to be reduced from ~1 hour to ~10 minutes in children with diseases within the abdomen. This will be achieved this through development of optimised MR acquisition strategies combined with Machine Learning (ML) reconstruction techniques. "

“1）磁共振成像（MRI）在许多儿童疾病的诊断和管理方面取得了重大进展。然而，MRI在儿科人群中具有挑战性，因为它耗时（约1小时执行），并且需要患者配合。因此，通常需要对8岁以下的儿童使用全身麻醉（GA），这既昂贵又有一定的风险。克服这些问题的一种方法是加快核磁共振扫描速度，这样孩子们就不必保持静止或屏住呼吸。最简单的方法是采集较少的数据（数据欠采样），但这会导致图像无法使用的伪影。目前用于去除这些伪影的重建方法允许有限的加速，或者使用耗时的算法，这妨碍了它们的临床应用。一种新的方法是机器学习，旨在“学习”如何去除欠采样以及运动伪影。2)通过这个项目的结束，学生将有一个很好的机器学习算法的理解，特别是用于重建MRI数据。学生还将能够使用MRI扫描仪，了解MRI序列设计，以及传统和最先进的MRI重建算法。这是一个非常转化的项目，将包括与临床合作伙伴密切合作。所有工作包将被整合到标准的临床工作流程中，以实现临床验证研究，并简单地转化为常规临床检查。3）本研究旨在开发新型加速磁共振成像（MRI）技术，使腹部疾病儿童的扫描时间从约1小时缩短至约10分钟。这将通过结合机器学习（ML）重建技术开发优化的MR采集策略来实现。"