SmartHeart: Next-generation cardiovascular healthcare via integrated image acquisition, reconstruction, analysis and learning

SmartHeart：通过集成图像采集、重建、分析和学习实现下一代心血管保健

• 批准号: EP/P001009/1
• 负责人: Daniel Rueckert
• 金额: $ 669.43万
• 依托单位: Imperial College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2016
• 资助国家: 英国
• 起止时间: 2016 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FP001009%2F1
• 关键词: SmartHeart; Next; generation; cardiovascular; healthcare

The vision for our research programme is to pave the way for a fundamentally different approach in which cardiovascular diseases (CVD) are diagnosed, monitored and treated: We propose to develop a diagnosis-driven "smart Magnetic Resonance (MR) scanner" that it is no longer a mere imaging device but instead becomes a highly sophisticated diagnostic tool. The output of a patient scan with the proposed smart MR scanner will not be just an image, but instead a comprehensive diagnostic assessment and interpretation of the patient's cardiovascular health/disease, enabling optimal treatment decisions for best patient outcome. The current approach to cardiovascular MR imaging (cMRI) is essentially serial: image acquisition is followed by image analysis and clinical interpretation. In addition, cardiac/respiratory motion is currently resulting in long scanning times for cMRI, with only a small fraction of the data (10-20%) being used for image reconstruction. This leads to breath-holds that are difficult to tolerate by sick patients. Furthermore, the characterization of clinically relevant tissue parameters requires the acquisition of multiple images which is inefficient. The absolute quantification of tissue parameters also remains a major technical challenge, leading to difficulties in interpreting tissue contrast parameters across scanners, clinical centres and patient populations. Finally, the objective interpretation of comprehensive, multi-parametric cMRI in the context of other complex non-imaging data is highly challenging for clinicians. We propose a transformative approach in which acquisition, analysis and interpretation are tightly coupled, with feedback between the different stages in order to optimize the overall objective: Extracting clinically useful information. Developing such an integrated approach to cardiac imaging will enable rapid, continuous and comprehensive imaging that is both simpler and more efficient than current practice, eliminating "dead time" between separate specialized acquisitions and allowing extraction of multiple dynamic as well as tissue contrast parameters simultaneously.

我们研究计划的愿景是为诊断、监测和治疗心血管疾病（CVD）的根本不同方法铺平道路：我们建议开发一种诊断驱动的“智能磁共振（MR）扫描仪”，它不再是一种单纯的成像设备，而是成为一种高度复杂的诊断工具。使用所提出的智能MR扫描仪进行患者扫描的输出将不仅仅是图像，而是对患者的心血管健康/疾病进行全面的诊断评估和解释，从而实现最佳治疗决策，以获得最佳患者结局。当前的心血管MR成像（cMRI）方法基本上是连续的：图像采集之后是图像分析和临床解释。此外，心脏/呼吸运动目前导致cMRI的扫描时间较长，只有一小部分数据（10-20%）用于图像重建。这导致病人难以忍受的屏气。此外，临床相关组织参数的表征需要采集多个图像，这是低效的。组织参数的绝对量化也仍然是一个主要的技术挑战，导致难以跨扫描仪、临床中心和患者群体解释组织对比度参数。最后，在其他复杂的非成像数据的背景下，对全面的多参数cMRI进行客观解释对临床医生来说是极具挑战性的。我们提出了一种变革性的方法，在这种方法中，采集、分析和解释紧密耦合，不同阶段之间有反馈，以优化总体目标：提取临床有用的信息。开发这种心脏成像的集成方法将实现快速、连续和全面的成像，这比当前的实践更简单和更有效，消除了单独的专门采集之间的“死区时间”，并允许同时提取多个动态和组织对比度参数。

项目成果

Predicting post-contrast information from contrast agent free cardiac MRI using machine learning: Challenges and methods.

• DOI: 10.3389/fcvm.2022.894503
• 发表时间: 2022
• 期刊: FRONTIERS IN CARDIOVASCULAR MEDICINE
• 影响因子: 3.6
• 作者: Abdulkareem, Musa;Kenawy, Asmaa A.;Rauseo, Elisa;Lee, Aaron M.;Sojoudi, Alireza;Amir-Khalili, Alborz;Lekadir, Karim;Young, Alistair A.;Barnes, Michael R.;Barckow, Philipp;Khanji, Mohammed Y.;Aung, Nay;Petersen, Steffen E.
• 通讯作者: Petersen, Steffen E.

Generalizable Framework for Atrial Volume Estimation for Cardiac CT Images Using Deep Learning With Quality Control Assessment.

使用深度学习和质量控制评估的深度学习对心脏CT图像进行心房体积估算的可通用框架。

• DOI: 10.3389/fcvm.2022.822269
• 发表时间: 2022
• 期刊: Frontiers in cardiovascular medicine
• 影响因子: 3.6
• 作者: Abdulkareem M;Brahier MS;Zou F;Taylor A;Thomaides A;Bergquist PJ;Srichai MB;Lee AM;Vargas JD;Petersen SE
• 通讯作者: Petersen SE

Evaluating reinforcement learning agents for anatomical landmark detection.

• DOI: 10.1016/j.media.2019.02.007
• 发表时间: 2019-04
• 期刊: Medical image analysis
• 影响因子: 10.9
• 作者: Alansary A;Oktay O;Li Y;Folgoc LL;Hou B;Vaillant G;Kamnitsas K;Vlontzos A;Glocker B;Kainz B;Rueckert D
• 通讯作者: Rueckert D

Statin Prescribing and Dosing-Failure Has Become an Option-Reply.

他汀类药物处方和剂量失败已成为一种选择-答复。

• DOI: 10.1001/jamacardio.2021.0838
• 发表时间: 2021
• 期刊: JAMA cardiology
• 影响因子: 24
• 作者: Adusumalli S

The Role of AI in Characterizing the DCM Phenotype.

• DOI: 10.3389/fcvm.2021.787614
• 发表时间: 2021
• 期刊: Frontiers in cardiovascular medicine
• 影响因子: 3.6
• 作者: Asher C;Puyol-Antón E;Rizvi M;Ruijsink B;Chiribiri A;Razavi R;Carr-White G
• 通讯作者: Carr-White G