Deep compressive quantitative MRI imaging

深度压缩定量 MRI 成像

• 批准号: EP/X001091/1
• 负责人: Mohammad Golbabaee
• 金额: $ 34.27万
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX001091%2F1
• 关键词: Deep; compressive; quantitative; MRI; imaging

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.

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，常规MRI扫描不产生“定量”测量，即标准化测量，因此难以比较在不同医院或在不同时间点获取的MRI图像，限制了这种成像技术用于高级诊断和监测精度的潜力。定量MRI（qMRI）旨在通过产生量化组织生物特性的可再现测量来克服这个问题，与扫描仪和扫描时间无关。这可以将现有的扫描仪从拍照机器转变为科学测量仪器，从而实现跨临床站点，个人和不同时间点的客观比较。但不幸的是，qMRI具有过长的采集时间，这目前对其在临床常规中的广泛采用造成了主要障碍。因此，该项目的主要目标是开发基于压缩采样和机器学习的新计算方法，这将大大减少qMRI的扫描时间。压缩采样技术使得能够从严格约束的传感器/成像系统有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，以便在该应用所需的更高加速（压缩）速率下消除图像压缩伪影。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它们来解决这一差距。该研究有两个受益者：（i）英国和国际临床学者的大型社区，他们使用qMRI技术进行癌症，心脏或神经退行性疾病等疾病的精确成像和评估研究，每种疾病都具有重大的社会经济影响。这一项目的成果将使这些研究更容易获得，在经济上更可行。(ii)一个由英国和国际非临床学者/专业人士组成的大型社区，他们致力于压缩采样逆问题技术，受到各种其他传感/成像应用的激励，这些应用可以从本项目开发的方法中受益。这些活动包括与临床学者和医疗保健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项目进展并分享开源软件和演示者，以及与医疗技术领域的领域专家和国家学术和非学术利益相关者举行的研讨会。