Synergistic Inverse Problems Omni-Solver for Expeditious High Quality Multimodal Cardiovascular MRI via Deep Compressive Sensing and Data Coalescing

协同逆问题 Omni-Solver 通过深度压缩传感和数据合并实现快速高质量多模态心血管 MRI

基本信息

批准号：
MR/V023799/1
负责人：
Guang Yang
金额：
$ 152.13万
依托单位：
Imperial College London
依托单位国家：
英国
项目类别：
Fellowship
财政年份：
2021
资助国家：
英国
起止时间：
2021 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=MR%2FV023799%2F1
关键词：
Synergistic Inverse Problems Omni Solver

项目摘要

Cardiovascular disease is the most common cause of illness and death worldwide. In the UK, it accounts for about 25% of all deaths, and it costs the NHS roughly 9 billion pounds each year. Medical imaging is used to screen and diagnose for disease and to plan and monitor treatment. Cardiovascular magnetic resonance (CMR) is a safe technique that allows detailed non-invasive imaging of the structure and function of the heart without using X-rays, which may increase the risk of cancer slightly. How the images are acquired - using different 'sequences' (or multimodal CMR) - is very flexible and can change the information content of the images to highlight 'biomarkers' of disease. These image biomarkers can allow earlier diagnosis of disease and better treatment planning.A typical CMR study lasts about an hour and can take several more hours to analyse, often requiring the reporting clinician to manually identify and outline structures of interest using a computer mouse. While many types of image can be acquired in a short period of breath-holding, highly detailed images with greater coverage can take 5-10 minutes to complete and image quality can be reduced by poor respiratory motion control which affects the reliability and usefulness of the image biomarkers. Increasing the speed of acquisition while maintaining or improving image quality together with a rapid, reproducible and fully automatic analysis of the resulting images will lead to the development of new biomarkers and improve the reliability of existing ones.In this fellowship proposal, I will work on methods to speed up multimodal CMR imaging by factors up to 12 (depending on the imaging sequence) by using an advanced 'deep learning' based signal processing approach. Deep learning is a new technique that teaches computers to do what comes naturally to humans: to learn by example. This is achieved by using fast computers working in parallel with 'big data' and has produced impressive results in different applications. I will then improve the quality of the CMR images obtained with a new 'transfer learning' technique that teaches a computer to accomplish a given task by using the model it used to do a similar previous one. A 'data coalescing technique' will be devised to take advantage of available 'big data'. In addition, I will develop and implement a fully automatic software pipeline to analyse the resulting images, extracting the cardiac anatomy and image biomarkers in ~2 minutes (speed-up factor of ~100). The methods will be developed using existing CMR images and will be implemented on our commercial CMR scanners for prospective testing in healthy volunteers and patients including those with myocardial infarction (heart attack), atrial fibrillation (fast irregular heart rhythm) and congenital heart disease (birth defect).I. Goals of Researchi. To speed up CMR imaging by acceleration factors up to 12 using new 'deep learning' methods.ii. To develop innovative 'transfer learning' and 'data coalescing' techniques to boost image quality by increasing the image resolution, suppressing the noise and correcting the blurry and other artefacts.iii. To investigate and implement a fully automated detection and analysis software package/pipeline for the assessment of new and reliable image biomarkers.II. Potential Benefit of Researchi. The combination of faster imaging and better image quality will allow automated analysis of image biomarkers extracted from CMR images. This will reduce the processing time by a factor of ~100, reduce clinician variability and allow better guidance and assessment of patients with cardiovascular disease.ii. Faster imaging will reduce study duration and improve the patient experience. It will also increase patient throughput, reduce waiting lists and reduce cost per study.iii. The techniques developed will be widely applicable to all types of CMR image, to other patient groups and also to other types of medical imaging.

心血管疾病是全球疾病和死亡的最常见原因。在英国，它约占所有死亡人数的25％，而NHS每年花费约90亿磅。医学成像用于筛查和诊断疾病以及计划和监测治疗。心血管磁共振（CMR）是一种安全的技术，可以详细介绍心脏的结构和功能而无需使用X射线，这可能会稍微增加癌症的风险。如何获取图像 - 使用不同的“序列”（或多模式CMR）非常灵活，并且可以更改图像的信息内容以突出疾病的“生物标志物”。这些图像生物标志物可以允许早期诊断疾病和更好的治疗计划。一项典型的CMR研究持续大约一个小时，并且可能需要几个小时的分析，通常需要报告临床医生使用计算机鼠标手动识别和概述感兴趣的结构。虽然可以在短时间的呼吸呼吸范围内获取许多类型的图像，但覆盖范围更高的高度详细图像可能需要5-10分钟才能完成，并且可以通过不良的呼吸运动控制来降低图像质量，从而影响图像生物标志物的可靠性和实用性。在保持或提高图像质量的同时提高获取速度，以及对所得图像的快速，可重复和全自动分析的速度，将导致新的生物标志物的发展并提高现有图像的可靠性，并在此奖学金提案中努力，我将努力通过最大程度地依靠进度进行进一步的``进一步''来加速多模态CMR成像的方法（根据进一步的效果）进行了进一步的研究。深度学习是一种新技术，它教会计算机做人类自然而然的事情：以身作则。这是通过使用与“大数据”并行工作的快速计算机来实现的，并在不同的应用程序中产生了令人印象深刻的结果。然后，我将通过一种新的“转移学习”技术提高CMR图像的质量，该技术通过使用用于执行类似先前的模型来教会计算机来完成给定任务。将设计一种“数据合并技术”，以利用可用的“大数据”。此外，我将开发并实施一条全自动软件管道，以分析所得图像，在〜2分钟内提取心脏解剖结构和图像生物标志物（加速系数〜100）。该方法将使用现有的CMR图像开发，并将在我们的商业CMR扫描仪上实施，用于健康志愿者和包括心肌梗塞（心脏病发作），心房颤动（快速不规则心脏节奏）和先天性心脏病（先天性心脏病）和先天性心脏病（先天性心脏病）（生育缺陷）的患者的前瞻性测试。研究目标。通过加速因子加速CMR成像，使用新的“深度学习”方法。II。开发创新的“转移学习”和“数据合并”技术来通过增加图像分辨率，抑制噪声并纠正模糊和其他伪像，以提高图像质量。调查和实施全自动检测和分析软件包/管道，以评估新的可靠图像生物标志物。研究的潜在好处。更快的成像和更好的图像质量的组合将允许对从CMR图像提取的图像生物标志物进行自动分析。这将使处理时间减少约100倍，减少临床医生的可变性，并可以更好地指导和评估心血管疾病患者。更快的成像将减少学习持续时间并改善患者的体验。它还将增加患者吞吐量，减少等待名单并降低每个研究成本。开发的技术将广泛适用于所有类型的CMR图像，其他患者组以及其他类型的医学成像。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Aortic Annulus Detection Based on Deep Learning for Transcatheter Aortic Valve Replacement Using Cardiac Computed Tomography.

DOI：
10.3346/jkms.2023.38.e306
发表时间：
2023-09-18
期刊：
JOURNAL OF KOREAN MEDICAL SCIENCE
影响因子：
4.5
作者：
Yongwon, Cho;Soojung, Park;Ho, Hwang Sung;Minseok, Ko;Do-Sun, Lim;Woong, Yu Cheol;Seong-Mi, Park;Mi-Na, Kim;Yu-Whan, Oh;Guang, Yang
通讯作者：
Guang, Yang

CHAIMELEON Project: Creation of a Pan-European Repository of Health Imaging Data for the Development of AI-Powered Cancer Management Tools.

DOI：
10.3389/fonc.2022.742701
发表时间：
2022
期刊：
Frontiers in oncology
影响因子：
4.7
作者：
Bonmatí LM;Miguel A;Suárez A;Aznar M;Beregi JP;Fournier L;Neri E;Laghi A;França M;Sardanelli F;Penzkofer T;Lambin P;Blanquer I;Menzel MI;Seymour K;Figueiras S;Krischak K;Martínez R;Mirsky Y;Yang G;Alberich-Bayarri Á
通讯作者：
Alberich-Bayarri Á

Multiparameter Synchronous Measurement With IVUS Images for Intelligently Diagnosing Coronary Cardiac Disease

IVUS图像多参数同步测量智能诊断冠心病