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

“1)磁共振成像（MRI）在许多儿童疾病的诊断和治疗方面取得了重大进展。然而，在儿科人群中，MRI是具有挑战性的，因为它耗时（约1小时），需要患者的配合。因此，对于8岁以下的儿童，通常需要使用全身麻醉（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的速度，这样孩子们就不必保持静止或屏住呼吸。最简单的方法是获取更少的数据（数据欠采样），但是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，这阻碍了它们的临床应用。一种新的方法是机器学习，旨在“学习”如何去除欠采样和运动伪影。2)在本项目结束时，学生将对机器学习算法，特别是MRI数据重建有很好的理解。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。这是一个转化性很强的项目，需要与临床合作伙伴密切合作。所有工作包将整合到标准的临床工作流程中，以进行临床验证研究，并简单地转化为常规临床实践。3)本研究旨在开发新的加速磁共振成像（MRI）技术，使儿童腹部疾病的扫描时间从1小时减少到10分钟。这将通过开发优化的MR采集策略并结合机器学习（ML）重建技术来实现。”

"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. "