Intelligent Imaging: Motion, Form and Function Across Scale

智能成像：跨尺度的运动、形式和功能

• 批准号: EP/H046410/1
• 负责人: David Hawkes
• 金额: $ 771.34万
• 依托单位: University College London
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2010
• 资助国家: 英国
• 起止时间: 2010 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FH046410%2F1
• 关键词: Intelligent; Imaging; Motion; Form; Function

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.

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某些方面的垫脚石，我们称之为目标，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。图像只是一个中间的可视化，供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告知诊断和指导治疗的信息。我们提出了一种新的范式，即提取临床相关信息驱动整个成像过程。所有医学成像设备都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。这些物理属性可以通过结构或代谢功能的变化来调节。从磁共振和CT扫描仪等设备获取医学图像通常需要几十秒到几分钟的时间。未出生的孩子，年幼的，年老的或病得很重的人不能在这段时间内保持静止，处理运动的方法效率低下，不能应用于所有类型的成像。通常采用触发和门控策略，导致采集效率低（因为大多数数据被拒绝），并且经常由于不规则运动而失败。结果，图像被明显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进行精确的计算建模表明，通过成像设备进行的细致测量可能会使临床医生或医学科学家推断出健康状况、特定疾病和治疗过程中发生的情况。不幸的是，由于上述运动伪影，进行这些精确的测量是非常困难的。成像系统可以为治疗师、干预医生或外科医生提供3D导航地图，显示治疗的地点和效果。不幸的是，在胸部和腹部的运动和变形组织中进行图像引导干预是非常困难的，因为图像经常被运动破坏，并且随着手术的进行，图像通常与干预医师或外科医生正在治疗的局部解剖结构偏离。我们的课程汇集了三组不同的人：构建解剖学、生理学、药理学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；临床医生也在努力为病人提供更好的治疗。有了这三个小组的共同努力，我们将设计新的方法来纠正运动伪影，以产生最佳质量的图像，这些图像直接与临床相关的组织组成、微观结构和新陈代谢的测量有关。我们将运用这些方法来提高对疾病进展的理解；指导治疗和评估肺癌和肝癌的治疗反应；缺血性心脏病；胎儿在子宫内的临床处理；照顾早产儿和幼儿。

项目成果

Combining morphological information in a manifold learning framework: application to neonatal MRI.

在流形学习框架中结合形态信息：在新生儿 MRI 中的应用。

• DOI: 10.1007/978-3-642-15711-0_1
• 发表时间: 2010
• 期刊: MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
• 作者: Aljabar P

Material Decomposition in Spectral CT Using Deep Learning: A Sim2Real Transfer Approach

• DOI: 10.1109/access.2021.3056150
• 发表时间: 2021-01-01
• 期刊: IEEE ACCESS
• 影响因子: 3.9
• 作者: Abascal, Juan F. P. J.;Ducros, Nicolas;Peyrin, Francoise
• 通讯作者: Peyrin, Francoise

Atrophy rates in asymptomatic amyloidosis: implications for Alzheimer prevention trials.

• DOI: 10.1371/journal.pone.0058816
• 发表时间: 2013
• 期刊: PloS one
• 影响因子: 3.7
• 作者: Andrews KA;Modat M;Macdonald KE;Yeatman T;Cardoso MJ;Leung KK;Barnes J;Villemagne VL;Rowe CC;Fox NC;Ourselin S;Schott JM;Australian Imaging Biomarkers, Lifestyle Flagship Study of Ageing
• 通讯作者: Australian Imaging Biomarkers, Lifestyle Flagship Study of Ageing

Re-localisation of a biopsy site in endoscopic images and characterisation of its uncertainty

• DOI: 10.1016/j.media.2011.11.005
• 发表时间: 2012-02-01
• 期刊: MEDICAL IMAGE ANALYSIS
• 影响因子: 10.9
• 作者: Allain, Baptiste;Hu, Mingxing;Hawkes, David J.
• 通讯作者: Hawkes, David J.