Adaptive information processing in hybrid imaging on the cloud

云端混合成像中的自适应信息处理

• 批准号: RGPIN-2014-05037
• 负责人: Li, Shuo
• 金额: $ 1.46万
• 依托单位: University of Western Ontario
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=634265
• 关键词: Adaptive; information; processing; hybrid; imaging

Background: Hybrid imaging is defined as the fusion of two or more imaging technologies into a single form of imaging. Ideally, this new form is synergistic-that is, more powerful than the sum of its parts. Hybrid imaging has grown from simply fusing two modalities into an independent research area. In the last decade, very wide ranges of hybrid imaging modalities have been becoming routinely available. Typical example would include PET (Positron Emission Tomography)-MRI (Magnetic Resonance Imaging), PET-CT (Computed Tomography), and multiple source radar-sonar images.To realize the full potential of hybrid imaging, diverse kinds of expertise must be brought together. Taking PET-MR-CT for an example: Traditionally PET is interpreted by human expert from nuclear medicine; CT and MRI are interpreted by different human experts from radiology. Hybrid PET-MRI brings significant challenges for human perceptions. This is especially true when dynamic hybrid imaging such as cardiac PET-MR and real time sonar-Radar. While the images provide complementary information, there is lack of computer based intelligence tools to handle this huge amount of data efficiently. This brings significant research opportunities for adaptive information processing platform.Motivation: Our research is motivated by: i) Within the last decade, there has been a growing increase of hybrid image acquisition, which is very challenging for human perception. The specialty of the hybrid images has required that the researcher apply particular methods to analysis rather than extend existing image processing methods. ii) Information extracted from the mutual modality contains valuable insight, which often requires further analysis. As an example, Cardiac CT and MRI images can output important information that can be used to medically diagnose disease, as well as track and monitor the progression of disease and therapy and PET can output muscle on molecular level, which can be used to track metabolism within muscle; and iii) the integrated registration and segmentation methods is motivated by the observation of the reciprocity between registration and segmentation and reciprocity between different modalities.OBJECTIVES: The main long term objective is to develop and evaluate an comprehensive adaptive information processing computer software platform for hybrid imaging. The system is able greatly reduce the current workload in the practice, further improve the accuracy; The secondary objective is to establish a publicly accessible hybrid imaging database for benchmarking to help the research community and to improve the current state-of-art of research in hybrid image analysis. Short term objectives: 1) Research and develop an intelligent PET-MRI-CT image processing software system, which covers image fusion, integrated segmentation and registration and information extraction system; 2) Research and develop multiple information based focus-of-attention mechanisms for generating region of interest (ROI) from the hybrid images segmentation and registration for further analysis; 3) Collect and share 50 cases of hybrid image database.SIGNIFICANCE: To the best of our knowledge, this is the first attempt to create a comprehensive information processing system for hybrid imaging. The proposed short term objective: PET-MR-CT information processing system leverage the existing leading facility in the institute, combined with the technical strengths of the group, fused with the user knowledge inherited in the group. The proposed methodology leverages the cutting edge techniques currently being developed in the group and fused with other leading technologies in the field, move towards a multidisciplinary, integrated research program.

背景：混合成像的定义是将两种或两种以上的成像技术融合成一种成像形式。理想情况下，这种新形式是协同的--也就是说，比各部分之和更强大。混合成像已经从简单地将两种模式融合为一个独立的研究领域。在过去的十年里，非常广泛的混合成像模式已经变得司空见惯。典型的例子包括PET(正电子发射断层扫描)-MRI(磁共振成像)，PET-CT(计算机断层扫描)，以及多源雷达-声纳图像。为了充分发挥混合成像的潜力，必须将各种专业知识结合在一起。以PET-MR-CT为例：传统上，PET是由核医学的人类专家解释的；CT和MRI是由放射学的不同人类专家解释的。混合PET-MRI给人类的感知带来了巨大的挑战。当动态混合成像，如心脏PET-MR和实时声纳-雷达时，尤其如此。虽然这些图像提供了互补的信息，但缺乏基于计算机的智能工具来有效处理这些海量数据。这为自适应信息处理平台带来了重要的研究机会。动机：我们的研究动机是：i)近十年来，混合图像采集越来越多，这对人类的感知非常具有挑战性。混合图像的特殊性要求研究人员将特定的方法应用于分析，而不是扩展现有的图像处理方法。Ii)从相互情态中提取的信息包含有价值的见解，这往往需要进一步分析。例如，心脏CT和MRI图像可以输出可用于医学诊断的重要信息，以及跟踪和监测疾病和治疗的进展，PET可以在分子水平上输出肌肉，用于跟踪肌肉内的代谢；iii)综合配准和分割方法的动机是观察到配准和分割之间的相互作用以及不同模式之间的相互作用。目的：主要的长期目标是开发和评估一个综合的自适应信息处理计算机软件平台，用于混合成像。该系统能够在实践中大大减少目前的工作量，进一步提高准确率；次要目标是建立一个可公开访问的混合图像数据库，用于标杆，以帮助研究界，提高目前混合图像分析的研究水平。短期目标：1)研究和开发智能PET-MRI-CT图像处理软件系统，包括图像融合、综合分割和配准以及信息提取系统；2)研究和开发基于多种信息的从混合图像分割和配准生成关注区域(ROI)以供进一步分析的机制；3)收集和共享50例混合图像数据库。拟议的短期目标：PET-MR-CT信息处理系统利用研究所现有的领先设施，结合集团的技术优势，与集团继承的用户知识相融合。建议的方法利用了该集团目前正在开发的尖端技术，并与该领域的其他领先技术融合在一起，走向一个多学科的综合研究计划。