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

推动该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区提供诊断/预后质量的床旁图像数据。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。我们提供了一个概念证明，使用低功率扫描仪在LMIC，特别是尼日利亚，我们通过传播信息的图像数据库中的信息，从国家的最先进的MRI扫描仪在英国可用。我们专注于儿童癫痫的应用，以证明早期临床获益。儿童癫痫在中低收入国家中具有迫切的临床需求，因为广泛使用的0.36T扫描仪的MRI不足以支持在英国常规使用1.5T或3 T图像进行的治疗性手术的临床决策。这使得许多患者得不到治疗，患有严重的癫痫并导致身体残疾和精神障碍，无法有效工作，并消耗了稀缺的医疗和社会护理资源。我们利用机器学习的最新进展，将英国可用的MRI与尼日利亚伊巴丹UCH儿科神经科诊所的MRI进行近似。机器学习在过去几年中取得了重大进展。特别是，它展示了人工智能在计算机视觉等数据丰富的应用领域的非凡成就，例如，计算机现在在物体识别方面超过了人类。这些进步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）与许多非医学计算机视觉任务相比，可用的数据较少，B）决策往往更关键，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医学图像传播到低质量医学图像。它显示了令人信服的早期结果，例如揭示薄白色物质路径，通常只能从专业的高分辨率数据集，从临床扫描仪上获得的标准分辨率图像。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信心措施，并定制解决方案，专门用于增强伊巴丹儿科诊所的图像与英国类似队列的图像。我们获取和整理的数据集足以支持学习所需的图像到图像的映射。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切实际的，因此我们采用无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。我们完善有前途的实现，并评估其对临床决策的影响，在伊巴丹使用当地商定的指标进行试点研究。我们打算将该项目作为一个跳板，用于更广泛和长期的计划，探索这些想法，以实现成像的范式转变，部署专门用于获取数据的廉价即时设备，这些设备由最先进或定制设备上获取的高质量图像数据库增强。

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.