Intelligent Imaging: Motion, Form and Function Across Scale

该计划旨在改变目前在需要对疾病进展进行定量评估或指导治疗的应用中使用医学成像的方式。成像技术传统上将重建图像视为最终目标，但实际上它是评估患者状态的某个方面的垫脚石，我们将其称为目标，例如特定疾病的存在、位置、程度和特征、心脏功能、对治疗的反应等。图像仅是一种中间可视化，用于由人类专家或基于计算机的分析进行后续解释和处理。我们的目标是直接从成像设备的测量中提取可用于通知诊断和指导治疗的信息。我们提出了一种新的范式，即临床相关信息的提取驱动了整个成像过程。所有的医学成像设备都会测量患者身体的某些物理属性，如CT中的X射线衰减、超声中的声阻抗变化或MRI中的质子迁移率。这些物理属性可能会受到结构或代谢功能变化的影响。来自MR和CT扫描仪等设备的医学图像通常需要10秒到许多分钟才能获得。未出生的婴儿、年幼的、年老的或病得很重的人此时不能静止不动，处理运动的方法效率低下，不能应用于所有类型的成像。通常采用触发和选通策略，这导致采集效率较低(因为大多数数据被拒绝)，并且经常由于不规则运动而失败。因此，图像被显著的运动伪影或模糊所破坏。对不同空间尺度上的生理和病理过程的精确计算建模表明，来自成像设备的仔细测量可以如何允许临床医生或医学科学家推断在健康、特定疾病和治疗期间发生的事情。不幸的是，由于上面描述的运动伪影，进行这些准确的测量是非常困难的。成像系统可以为治疗师、干预师或外科医生提供3D导航地图，显示应该在哪里进行治疗，并测量治疗的效果。不幸的是，在图像引导下对胸部和腹部的运动和变形组织进行干预是非常困难的，因为图像通常会受到运动的破坏，随着手术的进行，图像通常会偏离干预者或外科医生正在治疗的局部解剖结构。我们的计划汇集了三个不同的群体：构建解剖学、生理学、药理过程和组织运动动力学计算机模型的计算机科学家；开发新方法重建人体图像的成像科学家；以及致力于为患者提供更好治疗的临床医生。在这三个小组的共同努力下，我们将设计出新的方法来校正运动伪影，以产生与组织成分、显微结构和新陈代谢的临床相关测量直接相关的最佳质量的图像。我们将应用这些方法来加强对疾病进展的了解；指导治疗并评估对肺癌和肝脏癌症的治疗反应；对缺血性心脏病；对仍在子宫中的胎儿的临床管理；以及对早产儿和幼儿的护理。

This programme aims to change the way medical imaging is currently used in applications where quantitative assessment of disease progression or guidance of treatment is required. Imaging technology traditionally sees the reconstructed image as the end goal, but in reality it is a stepping stone to evaluate some aspect of the state of the patient, which we term the target, e.g. the presence, location, extent and characteristics of a particular disease, function of the heart, response to treatment etc. The image is merely an intermediate visualization, for subsequent interpretation and processing either by the human expert or computer based analysis. Our objectives are to extract information which can be used to inform diagnosis and guide therapy directly from the measurements of the imaging device. We propose a new paradigm whereby the extraction of clinically-relevant information drives the entire imaging process. All medical imaging devices measure some physical attribute of the patient's body, such as the X-ray attenuation in CT, changes acoustic impedance in ultrasound, or the mobility of protons in MRI. These physical attributes may be modulated by changes in structure or metabolic function. Medical images from devices such as MR and CT scanners often take 10s of seconds to many minutes to acquire. The unborn child, the very young, the very old or very ill cannot stay still for this time and methods of addressing motion are inefficient and cannot be applied to all types of imaging. Usually triggering and gating strategies are applied, which result in a low acquisition efficiency (since most of the data is rejected) and often fail due to irregular motion. As a result the images are corrupted by significant motion artifact or blurring.Accurate computational modeling of physiology and pathological processes at different spatial scales has shown how careful measurements from imaging devices might allow the clinician or the medical scientist to infer what is happening in health, in specific diseases and during therapy. Unfortunately, making these accurate measurements is very difficult due to the movement artifacts described above. Imaging systems can provide the therapist, interventionist or surgeon with a 3D navigational map showing where therapy should be delivered and measuring how effective it is. Unfortunately image guided interventions in the moving and deforming tissues of the chest and abdomen is very difficult as the images are often corrupted by motion and as the procedure progresses the images generally diverge from the local anatomy that the interventionist or surgeon is treating.Our programme brings together three different groups of people: computer scientists who construct computer models of anatomy, physiology, pharmacological processes and the dynamics of tissue motion; imaging scientists who develop new ways to reconstruct images of the human body; and clinicians working to provide better treatment for their patients. With these three groups working together we will devise new ways to correct for motion artifact, to produce images of optimal quality that are related directly to clinically relevant measures of tissue composition, microscopic structure and metabolism. We will apply these methods to improve understanding of disease progression; guide therapies and assess response to treatment in cancer arising in the lung and liver; to ischaemic heart disease; to the clinical management of the foetus while still in the womb; and to caring for premature babies and young children.