Towards 10 Minute Magnetic Resonance Imaging scans in children with machine learning

通过机器学习实现儿童 10 分钟磁共振成像扫描

• 批准号: 2407623
• 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: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2407623
• 关键词: Towards; 10; Minute; Magnetic; Resonance

1. 1. BackgroundMagnetic Resonance Imaging (MRI) has enabled significant advances in the diagnosis and management of many 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 collect less data for each image, 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 (ML) that aims to 'learn' how to remove undersampling and motion artefacts. The purpose of this project is to develop fast MRI acquisitions, with rapid Machine Learning reconstruction technologies for use in childhood diseases of the abdomen.Building on work in Super Resolution and Deep Artefact Suppression, this project aims to reframe the reconstruction of MRI data, to remove aliases caused by data undersampling and motion corruption, as an image de-noising problem that can be initially performed by a CNN. This strategy requires specific sampling patterns that produce noise-like aliasing and large amounts of high quality, application-specific training data. Thus, this work will leverage the extremely large amounts image data that is available at Great Ormond Street Hospital (GOSH). 2. 2. Research Aims and ObjectivesThis study aims to develop novel accelerated 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 ML reconstruction techniques. Specific work packages include development of techniques to correct for artefacts caused by respiratory motion, development of fast 3D imaging for assessment of small bowel motility in Chron's disease, as well as development of real-time imaging to assess the filling and emptying function of the stomach. The resulting networks will be integrated into standard clinical workflow to enable clinical validation studies, as well as simple translation into routine clinical practise.3. 3. Novelty of Research MethodologyA few recent papers have shown the benefit of using ML methods for reconstruction of MR images. These have mostly been in knee or cardiac applications, and very few have been in abdominal MR imaging or in paediatrics. 4. 4. Alignment to EPSRC's strategies and research areasThis research aligns with the EPSRC Healthcare Technologies theme. The work falls within the "Optimising Treatment" grand challenge and is thoroughly aligned with the "Novel imaging technologies" cross-cutting research capabilities area. The proposed technologies satisfy the key fields; techniques for image reconstruction, lower cost image acquisition technologies, high throughput, and automated image interpretation. The work is also aligned with the EPSRC Research area, "Medical imaging". In particular, it addresses the high priority areas of this delivery plan; enabling earlier and more effective diagnosis, and novel imaging technologies that offer a significant benefit over current technologies. 5. 5. Any companies or collaborators involvedThe National Institute for Health Research Biomedical Research Centre (BRC) is partly funding this PhD studentship. Siemens Healthcare have pledged their support, as Project Partner, for Jennifer Steeden's UKRI grant which is closely related to this work.Great Ormond Street Hospital (GOSH) will be close collaborators in this project, providing access to MRI data and MRI scanning facilities.

1. 1.背景磁共振成像（MRI）在许多疾病的诊断和治疗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。这样做的最简单的方法是为每个图像收集较少的数据，然而，这会导致使图像不可用的伪影。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（ML），旨在“学习”如何去除欠采样和运动伪影。该项目的目的是开发快速MRI采集，并使用快速机器学习重建技术用于腹部儿童疾病。该项目旨在基于超分辨率和深度伪影抑制的工作，重新构建MRI数据的重建，以消除数据欠采样和运动损坏引起的别名，作为一个图像去噪问题，可以首先由CNN执行。这种策略需要特定的采样模式，产生类似噪声的混叠和大量高质量的特定于应用程序的训练数据。因此，这项工作将利用大奥蒙德街医院（GOSH）提供的大量图像数据。2. 2.研究目的和目标本研究旨在开发新的加速MRI技术，使腹部疾病儿童的扫描时间从约1小时减少到约10分钟。这将通过结合ML重建技术开发优化的MR采集策略来实现。具体的工作包包括开发技术来纠正由呼吸运动引起的伪影，开发快速3D成像以评估Chron病的小肠运动，以及开发实时成像以评估胃的充盈和排空功能。由此产生的网络将被集成到标准的临床工作流程中，以实现临床验证研究，以及简单的转换为常规临床流程。3. 3.研究方法的新奇最近的一些论文已经显示了使用ML方法重建MR图像的好处。这些主要应用于膝关节或心脏，很少用于腹部MR成像或儿科。4. 4.与EPSRC的战略和研究领域保持一致这项研究与EPSRC医疗保健技术主题保持一致。这项工作福尔斯属于“优化治疗”的重大挑战，并与“新型成像技术”的交叉研究能力领域完全一致。所提出的技术满足关键领域;图像重建技术、低成本图像采集技术、高通量和自动图像解释。这项工作也与EPSRC研究领域“医学成像”保持一致。特别是，它解决了该交付计划的高优先级领域;使早期和更有效的诊断，以及新的成像技术，提供了一个显着的好处比目前的技术。5. 5.国家健康研究所生物医学研究中心（BRC）部分资助了这个博士生项目。作为项目合作伙伴，西门子医疗已承诺支持Jennifer Steeden的UKRI赠款，该赠款与这项工作密切相关。大奥蒙德街医院（GOSH）将是该项目的密切合作者，提供MRI数据和MRI扫描设备。