Enabling Clinical Decisions From Low-power MRI In Developing Nations Through Image Quality Transfer

推动这一项目的长期愿景是软件解决方案，使低功耗、廉价和可持续的成像设备能够在资源匮乏的地点提供高质量的诊断/预测质量的护理点图像数据。我们通过传播来自高质量图像数据库的信息来实现这一点。我们使用来自LMIC，特别是尼日利亚的低功率扫描仪的MRI提供概念证明，我们通过传播来自英国最先进的MRI扫描仪的图像数据库的信息来增强这一概念。我们将重点放在儿童癫痫的应用上，以展示早期的临床益处。儿童癫痫是LMICs的迫切临床需求，因为广泛可用的0.36T扫描仪的MRI不足以支持英国常规使用1.5T或3T图像进行的根治性手术的临床决策。这使得许多患者得不到治疗，患有严重的癫痫和由此导致的身体残疾和精神障碍，无法有效地工作，并耗尽稀少的医疗和社会护理资源。我们借鉴了机器学习的最新进展，以近似英国可用的核磁共振成像，这些磁共振成像可在尼日利亚UCH Ibadan的儿科神经科诊所获得-尼日利亚UCH Ibadan的一家典型的撒哈拉以南城市医院。在过去的几年里，机器学习取得了重大进展。特别是，它展示了人工智能在数据丰富的应用领域的非凡壮举，例如，在计算机视觉等领域，计算机在物体识别方面的表现现在超过了人类。这些进展刚刚开始对医学成像产生影响，这带来了独特的挑战，因为a)可用数据比许多非医疗计算机视觉任务少，b)决策通常更关键，因为它们直接影响患者的结果。我们最近的图像质量传输(IQT)框架将信息从高质量的医学图像传播到低质量的医学图像。它显示了令人信服的早期结果，例如从临床扫描仪获取的标准分辨率图像中揭示了薄薄的白质路径，通常只有从专业的高分辨率数据集才能获得。在这里，我们推进IQT，以利用最新的机器学习技术，增强这些技术，以提供对医疗决策有价值的置信度测量，并专门定制解决方案，以增强来自Ibadan儿科诊所的图像，以及来自英国类似队列的图像。我们获取并整理足以支持学习所需图像到图像映射的数据集。来自英国和尼日利亚扫描仪的相同主题的匹配图像对是不现实的，因此我们使用非监督和半监督学习来构建图像到图像的映射，而不直接匹配训练数据。在伊巴丹的一项试点研究中，我们使用当地商定的指标改进了有希望的实现，并评估了它们对临床决策的影响。我们打算将这个项目作为一个跳板，作为探索这些想法的更广泛和长期计划的跳板，以实现成像的范式转变，即部署廉价的护理点设备，专门为获取通过最先进或定制设备获取的高质量图像数据库而增强的数据。

The long-term vision motivating this project is of software solutions that enable low-power cheap-and-sustainable imaging devices able to provide point-of-care image data in resource-poor locations at diagnostic/prognostic quality. We achieve this by propagating information from databases of high quality images. We provide a proof of concept using MRI from lower-power scanners available in LMICs, specifically Nigeria, that we enhance by propagating information from databases of images from state-of-the-art MRI scanners available in the UK. We focus on an application to childhood epilepsy to demonstrate early clinical benefit. Childhood epilepsy presents an immediate clinical need in LMICs, as MRI from widely available 0.36T scanners is insufficient to support clinical decisions on curative surgery that are routinely made in the UK using 1.5T or 3T images. This leaves many patients untreated, living with severe epilepsy and resulting physical disabilities and mental disorders, unable to work effectively, and draining sparse medical and social-care resources.We draw on the latest advances in machine learning to approximate the MRIs available in the UK from those accessible in the paediatric neurology clinic in UCH Ibadan, Nigeria - a typical sub-Saharan city hospital. Machine learning has made major advances over the last few years. In particular, it shows remarkable feats of artificial intelligence in data-rich application areas such as computer vision where, for example, computers now outperform humans in object recognition. The advances are just starting to make an impact in medical imaging, which presents unique challenges because a) less data is available than many non-medical computer vision tasks, b) decisions are often more critical as they impact directly on patient outcome. Our recent image quality transfer (IQT) framework propagates information from high quality to low quality medical images. It shows compelling early results, such as revealing thin white matter pathways, usually only accessible from specialist high resolution data sets, from standard resolution images acquired on a clinical scanner. Here we advance IQT to exploit the latest machine learning techniques, enhance those techniques to provide confidence measures valuable for medical decision-making, and tailor solutions specifically to enhance images from the Ibadan paediatric clinic with those from similar cohorts in the UK. We acquire and collate the data sets sufficient to support learning the required image-to-image mappings. Matched pairs of images from the same subjects from UK and Nigerian scanners are not practical to obtain, so we employ unsupervised and semi-supervised learning to construct image-to-image mappings without directly matching training data. We refine promising implementations and assess their impact on clinical decision making in a pilot study in Ibadan using locally agreed metrics. We intend this project as a springboard for a much wider and long term program exploring these ideas to bring about a paradigm shift in imaging that deploys cheap point-of-care devices built specifically to acquire data enhanced by databases of high quality images acquired on state of the art or bespoke devices.