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

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。目前的心血管MR成像（cMRI）方法基本上是连续的：图像采集后进行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。

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.