Towards 10-minute Magnetic Resonance Scanning in Children - Developing Accelerated Imaging Using Machine Learning

迈向 10 分钟儿童磁共振扫描 - 使用机器学习开发加速成像

• 批准号: MR/S032290/1
• 负责人: Jennifer Steeden
• 金额: $ 126.15万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=MR%2FS032290%2F1
• 关键词: Towards; 10; minute; Magnetic; Resonance

Magnetic Resonance Imaging (MRI) scans play a vital role in helping many ill children, by finding out what the problem is and helping plan their treatment. MRI is safe because it does not use radiation. MRI scans produce good-quality pictures or images of many parts of the body, including the brain, heart, spine, joints and other organs. The main problem is they take a long time - often over an hour. During the scan, the child has to keep very still and may even need to hold their breath many times. This is especially hard for children and unwell patients. Hence, younger children under 8 years old need a general anaesthetic, to put them to sleep during the scan. In many childhood diseases, for example in cancer, children may need many MRI scans to follow up disease progression and treatment. Being put to sleep for all of these scans is not pleasant for the child and may occasionally cause problems. It also puts a lot of pressure on hospitals who need to find the doctors, beds, equipment and funds for this. One way of overcoming these problems would be to speed up the MRI scans so the 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, but this causes so much distortion in the images that they cannot be used. There are some ways of converting these into useful images, but these are complicated and take too long to use in a hospital.Machine Learning is an upcoming way of teaching computers to find complicated patterns in large amounts of information. Recent advances mean that computers are now so powerful that they can learn effectively. Machine Learning has been successfully used for analysing many types of images, for example to perform de-noising, interpolation, image classification and border identification. Despite its popularity, only a few recent studies have shown its potential for reconstruction of MRI images. This is partly due to the greater complexity of the problem and importantly, the large amounts of data required to 'learn' the solution. At Great Ormond Street Hospital, we have MRI images from over 100,000 children and scan an additional 10,000 children each year, all of which we could use to help train and test Machine Learning technologies.I have already shown that basic Machine Learning techniques can remove distortions from MRI scans of the heart, so I am well placed to develop Machine Learning techniques to reconstruct MRI images from other children's diseases, as well as developing more advanced Machine Learning techniques. I showed Machine Learning to be faster than existing reconstruction methods and the images were of better quality than more conventional state-of-the-art techniques. However, much more work is needed to get Machine Learning working reliably in children's scans and to make the most of the possible benefits.If we can use fast scanning with Machine Learning we could shorten scan times from 1 hour to about 10 minutes for children having MRI scans. They would not have to keep completely still for the scan and would not have to hold their breath, therefore reducing the need to put patients to sleep. This would make MRI scanning far less difficult and daunting for children, and would eliminate the cost and side effects from the anaesthetic. Quicker scans would help reduce waiting lists and costs for the NHS. It would also mean that MRI scanning would be used far more often, so it could help many more children. Additionally, these techniques could enable MRI scans to become affordable in some countries for the first time.

磁共振成像(MRI)扫描在帮助许多患病儿童方面发挥着至关重要的作用，因为它可以找出问题所在，并帮助计划他们的治疗。核磁共振是安全的，因为它不使用辐射。核磁共振扫描可以生成身体许多部位的高质量图片或图像，包括大脑、心脏、脊柱、关节和其他器官。主要的问题是它们需要很长的时间--通常超过一个小时。在扫描过程中，孩子必须保持非常静止，甚至可能需要多次屏气。对于儿童和身体不适的病人来说，这尤其困难。因此，8岁以下的儿童需要全身麻醉剂，以便在扫描过程中进入睡眠状态。在许多儿童疾病中，例如癌症，儿童可能需要进行多次核磁共振扫描，以跟踪疾病进展和治疗。所有这些扫描都让孩子睡着，对孩子来说不是一件愉快的事情，偶尔可能会引发问题。这也给医院带来了很大的压力，他们需要为此找到医生、床位、设备和资金。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，但这会导致图像中的严重失真，以至于无法使用它们。有一些方法可以将这些图像转换成有用的图像，但这些方法很复杂，在医院使用太长时间。机器学习是一种即将到来的教计算机从大量信息中找到复杂模式的方法。最近的进步意味着计算机现在是如此强大，以至于它们可以有效地学习。机器学习已被成功地用于分析许多类型的图像，例如执行去噪、内插、图像分类和边界识别。尽管它很受欢迎，但最近只有几项研究显示出它在重建MRI图像方面的潜力。这在一定程度上是因为问题的复杂性更大，更重要的是，需要大量的数据来“学习”解决方案。在大奥蒙德街医院，我们有超过10万名儿童的核磁共振图像，每年另外扫描1万名儿童，所有这些我们都可以用来帮助培训和测试机器学习技术。我已经证明了基本的机器学习技术可以消除心脏核磁共振扫描的失真，所以我处于有利地位，可以开发机器学习技术来重建其他儿童疾病的核磁共振图像，以及开发更先进的机器学习技术。我展示了机器学习比现有的重建方法更快，图像的质量比更传统的最先进技术更好。然而，要让机器学习在儿童扫描中可靠地工作，并最大限度地发挥可能的好处，还需要做更多的工作。如果我们可以将快速扫描与机器学习结合使用，我们可以将儿童进行核磁共振扫描的扫描时间从1小时缩短到大约10分钟。他们不需要在扫描时完全保持静止，也不需要屏住呼吸，因此减少了让患者入睡的需要。这将使核磁共振扫描对儿童来说不那么困难和令人望而生畏，并将消除麻醉剂的成本和副作用。更快的扫描将有助于减少NHS的等待名单和成本。这也将意味着核磁共振扫描将被更频繁地使用，因此它可以帮助更多的儿童。此外，这些技术可以使核磁共振扫描在一些国家首次变得负担得起。

项目成果

Automatic segmentation of the great arteries for computational hemodynamic assessment.

• DOI: 10.1186/s12968-022-00891-z
• 发表时间: 2022-11-07
• 期刊: Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance

2D sodium MRI of the human calf using half-sinc excitation pulses and compressed sensing

• DOI: 10.1002/mrm.29841
• 发表时间: 2023-10-05
• 期刊: MAGNETIC RESONANCE IN MEDICINE
• 影响因子: 3.3
• 作者: Baker，Rebecca R.;Muthurangu，Vivek;Steeden，Jennifer A.
• 通讯作者: Steeden，Jennifer A.

Editorial for "Automatic Time-Resolved Cardiovascular Segmentation of 4D Flow MRI Using Deep Learning"

“使用深度学习对 4D 流 MRI 进行自动时间分辨心血管分割”的社论

• DOI: 10.1002/jmri.28220
• 发表时间: 2022
• 期刊: Journal of Magnetic Resonance Imaging
• 影响因子: 4.4
• 作者: Montalt-Tordera J

FReSCO: Flow Reconstruction and Segmentation for low-latency Cardiac Output monitoring using deep artifact suppression and segmentation.

• DOI: 10.1002/mrm.29374
• 发表时间: 2022-11
• 期刊: MAGNETIC RESONANCE IN MEDICINE
• 影响因子: 3.3
• 作者: Jaubert, Olivier;Montalt-Tordera, Javier;Brown, James;Knight, Daniel;Arridge, Simon;Steeden, Jennifer;Muthurangu, Vivek
• 通讯作者: Muthurangu, Vivek

Real-time deep artifact suppression using recurrent U-Nets for low-latency cardiac MRI.

• DOI: 10.1002/mrm.28834
• 发表时间: 2021-10
• 期刊: Magnetic resonance in medicine
• 影响因子: 3.3
• 作者: Jaubert O;Montalt-Tordera J;Knight D;Coghlan GJ;Arridge S;Steeden JA;Muthurangu V
• 通讯作者: Muthurangu V