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

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，磁共振成像在儿科人群中具有挑战性，因为它很耗时(~1小时完成)，并且需要患者合作。因此，经常有必要对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。解决这些问题的一种方法是加快核磁共振扫描，这样孩子们就不必保持不动或屏住呼吸。要做到这一点，最简单的方法是为每个图像收集较少的数据，然而，这会导致图像无法使用的伪影。目前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，阻碍了它们的临床应用。一种新的方法是机器学习(ML)，其目标是‘学习’如何去除欠采样和运动伪影。这个项目的目的是开发快速MRI采集，使用快速机器学习重建技术用于儿童腹部疾病。该项目建立在超分辨率和深度伪影抑制工作的基础上，旨在重新构建MRI数据的重建框架，消除数据采样不足和运动损坏造成的混叠，作为一个最初可以由CNN执行的图像去噪问题。这一策略需要特定的采样模式，以产生类似噪声的混叠和大量高质量的、特定于应用的训练数据。因此，这项工作将利用大奥蒙德街医院(GOSH)提供的极大量的图像数据。2.研究目的和目的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~1小时减少到~10分钟。这将通过开发优化的MR获取策略与ML重建技术相结合来实现。具体的工作包包括开发纠正呼吸运动引起的伪影的技术，开发用于评估Chron病的小肠运动的快速3D成像，以及开发用于评估胃的充盈和排空功能的实时成像。由此产生的网络将被整合到标准的临床工作流程中，以实现临床验证研究，以及将其简单地转化为常规临床实践。3.研究方法的新颖性最近的几篇论文显示了使用ML方法重建MR图像的好处。它们大多用于膝盖或心脏应用，很少用于腹部磁共振成像或儿科。4.与EPSRC的战略和研究领域保持一致本研究与EPSRC医疗保健技术主题保持一致。这项工作属于“优化治疗”的重大挑战，与“新成像技术”交叉研究能力领域完全一致。所提出的技术满足了图像重建技术、低成本图像采集技术、高吞吐量和自动图像解释等关键领域的要求。这项工作也与EPSRC研究领域“医学成像”保持一致。特别是，它解决了这一交付计划的高优先级领域；实现了更早和更有效的诊断，以及与当前技术相比具有显著优势的新型成像技术。5.任何参与的公司或合作者国家健康研究所生物医学研究中心(BRC)将为该博士生项目提供部分资金。西门子医疗保健已承诺作为项目合作伙伴，支持Jennifer Steeden与这项工作密切相关的UKRI赠款。大奥蒙德街医院(GOSH)将成为该项目的密切合作伙伴，提供对MRI数据和MRI扫描设施的访问。

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.