Deep compressive quantitative MRI imaging

磁共振成像(MRI)通过在高分辨率图像中提供精致的软组织对比度，非侵入性地改变了我们观察人体的方式。这使得MRI成为诊断和监测许多疾病的金标准成像技术。然而，传统的MRI扫描并不产生“定量”测量，即标准化测量，因此很难比较在不同医院或不同时间点获得的MRI图像，这限制了这种成像技术在高级诊断和监测精度方面的潜力。量化MRI(QMRI)旨在通过产生可重复的测量来量化组织的生物属性，而不依赖于扫描仪和扫描时间。这可能会将现有的扫描仪从照相机转变为科学测量仪器，从而能够进行跨临床地点、个人和不同时间点的客观比较。但不幸的是，定量磁共振成像的获取时间过长，这目前为其在临床上的广泛采用制造了主要障碍。因此，本项目的主要目标是开发基于压缩采样和机器学习的新的计算方法，从而大大减少qMRI的扫描次数。压缩采样技术能够从严格受限的传感器/成像系统中高效地采集信号和图像。它们最近已被应用于解决qMRI中的扫描时间问题，但这些技术需要更好的计算方法来在此应用所需的更高加速(压缩)速率下去除图像压缩伪影。该项目旨在通过先进的基于机器学习的模型和适当选择的数据集来训练这些模型来解决这一差距。这项研究有两个受益者：(I)一个由英国和国际临床学者组成的大型社区，他们使用qMRI技术对癌症、心脏或神经退行性疾病等疾病进行精确成像和评估，每一项都具有重大的社会经济影响。该项目的成果将使这些研究更容易获得，在经济上也更可行。(Ii)致力于压缩采样反问题技术的英国和国际非临床学者/专业人员的大型社区，其动机是各种其他传感/成像应用程序，这些应用程序可以在他们的研究中受益于本项目开发的方法。这些活动包括与临床学者和医疗行业作为我们的项目合作伙伴共同制作和验证知识，在领先的学术期刊/会议上发布结果，建立一个项目网站来宣传最新的项目进展并分享开源软件和演示，以及与医疗技术领域的现场专家和国家学术和非学术利益相关者举办研讨会。

Magnetic resonance imaging (MRI) has transformed the way we look through the human body by offering exquisite soft-tissue contrast in high-resolution images, noninvasively. This has made MRI the gold-standard imaging technique for diagnosis and monitoring of many diseases. However, conventional MRI scans do not produce "quantitative" measurements, i.e. standardised measures, and therefore it is difficult to compare MRI images acquired at different hospitals, or at different points in time, limiting the potential of this imaging technology for advanced diagnostic and monitoring precision.Quantitative MRI (qMRI) aims to overcome this problem by yielding reproducible measurements that quantify tissue bio-properties, independent of the scanner and scanning times. This could transform the existing scanners from picture-taking machines to scientific measuring instruments, enabling objective comparisons across clinical sites, individuals and different time-points. But unfortunately qMRIs have excessively long acquisition times which currently create a major obstacle for their wide adoption in clinical routines. Therefore, the main goal of this project is to develop new computational methodologies based on compressed sampling and machine learning that will substantially reduce the scan times of qMRI. Compressed sampling techniques enable efficient acquisition of signals and images from tightly constrained sensor/imaging systems. They have been recently applied to address the issue of scan time in qMRI, but these techniques require much better computational methods for removing image compression artefacts at higher acceleration (compression) rates needed for this application. The project aims to address this gap through advanced machine learning-based models and appropriately chosen datasets to train them.The research has two streams of beneficiaries: (i) A large community of UK and international clinical academics that use qMRI techniques for their research on precision imaging and evaluation of diseases such as cancer, cardiac or neurodegenerative disorders, each with significant socioeconomic impact. The outcomes of this project would allow these studies to become more available and more economically feasible. (ii) A large community of UK and international non-clinical academics/professionals who work on compressed sampling inverse problem techniques, motivated by variety of other sensing/imaging applications that could benefit in their studies from methodologies developed by this project.A number of activities have been carefully designed to effectively engage with beneficiaries of this research. These activities include co-production and validation of knowledge with clinical academics and healthcare industry as our project partners, publishing of the results in leading academic journals/conferences, a project website to publicize up-to-date project advances and share open-source software and demonstrators, and a workshop with field specialists and national academic and non-academic stakeholders in medical technologies.