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

• 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

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.

心血管疾病是全世界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%，每年给 NHS 造成约 90 亿英镑的损失。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) 是一种安全技术，无需使用 X 射线即可对心脏的结构和功能进行详细的非侵入性成像，而 X 射线可能会稍微增加患癌症的风险。图像的获取方式——使用不同的“序列”（或多模态 CMR）——非常灵活，可以改变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实现疾病的早期诊断和更好的治疗计划。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时间来分析，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。虽然可以在短时间内屏气获取多种类型的图像，但具有更大覆盖范围的高度详细的图像可能需要 5-10 分钟才能完成，并且呼吸运动控制不良可能会降低图像质量，从而影响图像的可靠性和实用性。图像生物标志物。提高采集速度，同时保持或提高图像质量，并对所得图像进行快速、可重复和全自动分析，将导致新生物标记物的开发并提高现有生物标记物的可靠性。在本奖学金提案中，我将致力于通过使用基于“深度学习”的先进信号处理方法，可将多模态 CMR 成像速度提高高达 12 倍（取决于成像序列）。深度学习是一种新技术，它教会计算机做人类自然会做的事情：通过例子学习。这是通过使用与“大数据”并行工作的快速计算机来实现的，并在不同的应用中产生了令人印象深刻的结果。然后，我将提高通过新的“迁移学习”技术获得的 CMR 图像的质量，该技术教会计算机使用用于执行类似的先前任务的模型来完成给定的任务。将设计“数据合并技术”来利用可用的“大数据”。此外，我将开发并实现一个全自动软件管道来分析生成的图像，在约 2 分钟内提取心脏解剖结构和图像生物标记物（加速系数约 100）。这些方法将使用现有的 CMR 图像进行开发，并将在我们的商用 CMR 扫描仪上实施，以便对健康志愿者和患者进行前瞻性测试，包括患有心肌梗塞（心脏病发作）、心房颤动（快速不规则心律）和先天性心脏病（出生）的患者缺陷）。研究目标。使用新的“深度学习”方法将 CMR 成像速度提高至 12 倍。开发创新的“转移学习”和“数据合并”技术，通过提高图像分辨率、抑制噪声并纠正模糊和其他伪影来提高图像质量。研究并实施全自动检测和分析软件包/流程，以评估新的可靠的图像生物标志物。研究的潜在好处。更快的成像和更好的图像质量的结合将允许对从 CMR 图像中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，并更好地指导和评估心血管疾病患者。更快的成像将减少研究持续时间并改善患者体验。它还将增加患者吞吐量、减少等待名单并降低每项研究的成本。所开发的技术将广泛适用于所有类型的 CMR 图像、其他患者群体以及其他类型的医学成像。

项目成果

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

• DOI: 10.1109/tim.2020.3036067
• 发表时间: 2021
• 期刊: IEEE Transactions on Instrumentation and Measurement
• 影响因子: 5.6
• 作者: Cao Y

Deep learning based synthesis of MRI, CT and PET: Review and analysis

• DOI: 10.1016/j.media.2023.103046
• 发表时间: 2023-12-04
• 期刊: MEDICAL IMAGE ANALYSIS
• 影响因子: 10.9
• 作者: Dayarathna，Sanuwani;Islam，Kh Tohidul;Chen，Zhaolin
• 通讯作者: Chen，Zhaolin

AI-Based Reconstruction for Fast MRI-A Systematic Review and Meta-Analysis

• DOI: 10.1109/jproc.2022.3141367
• 发表时间: 2022-02-01
• 期刊: PROCEEDINGS OF THE IEEE
• 影响因子: 20.6
• 作者: Chen, Yutong;Schonlieb, Carola-Bibiane;Yang, Guang
• 通讯作者: Yang, Guang