Estimation of tissue deformation in medical images

医学图像中组织变形的估计

• 批准号: RGPIN-2015-04136
• 负责人: Rivaz, Hassan
• 金额: $ 1.97万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2016
• 资助国家: 加拿大
• 起止时间: 2016-01-01 至 2017-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=612792
• 关键词: Estimation; tissue; deformation; medical; images

We have seen a significant increase in the use of computers in medicine in the past decade. Extensive use of medical image analysis techniques has helped clinicians transcend many limitations of conventional medicine. The focus of my research lies in the same paradigm: to develop novel medical image analysis techniques that are reliable for clinical use. More specifically, my research program involves the development of novel computing techniques for ultrasound (US) elastography and fusion/registration of US images to magnetic resonance (MR) and computed tomography (CT). US is an easy-to-use, inexpensive, real-time and safe imaging modality. This relatively young medical imaging modality is also one of the most frequently used, and is rapidly growing. In fact, numerous new research projects have recently been translated into commercial products. This research proposal has two thrusts that further improve the capabilities of US. First, we will develop novel image processing techniques that accurately track tissue deformation with US to create new images that show tissue mechanical properties. This is of significant clinical impact since pathologic changes in tissue, such as cancer tumours, are highly correlated with tissue mechanical properties. Second, we will develop robust and accurate techniques for automatic deformable registration of US to MR and CT. Image registration involve aligning two images that are obtained from the same tissue, so that they can be compared or combined. Registration of US with MR and CT has numerous applications in both diagnosis and therapy/interventions, and is an active field of research. Our approach in both thrusts is to cast the problem as optimization of a cost function that incorporates image similarity and prior information. To achieve widespread clinical use, a critical requirement in both elastography and registration is real-time performance. The proposed research program will achieve this goal by developing novel computationally efficient image analysis techniques. The proposed interdisciplinary research program will train two PhD and four MASc students in an exciting environment at the Department of Electrical and Computer Engineering and the PERFORM Centre at Concordia University.

在过去的十年中，我们看到计算机在医学中的使用显着增加。医学图像分析技术的广泛使用帮助临床医生超越了传统医学的许多局限性。我的研究重点在于同一范式：开发临床使用可靠的新型医学图像分析技术。更具体地说，我的研究项目涉及开发用于超声 (US) 弹性成像的新型计算技术以及 US 图像与磁共振 (MR) 和计算机断层扫描 (CT) 的融合/配准。 US 是一种易于使用、廉价、实时且安全的成像方式。这种相对年轻的医学成像方式也是最常用的方式之一，并且正在快速增长。事实上，许多新的研究项目最近已转化为商业产品。这项研究提案有两个主旨，进一步提高美国的能力。首先，我们将开发新颖的图像处理技术，通过超声精确跟踪组织变形，以创建显示组织机械特性的新图像。这具有重大的临床影响，因为组织的病理变化（例如癌症肿瘤）与组织的机械性能高度相关。其次，我们将开发强大而准确的技术，用于 US 到 MR 和 CT 的自动变形配准。图像配准涉及对齐从同一组织获得的两个图像，以便可以比较或组合它们。 US 与 MR 和 CT 的配准在诊断和治疗/干预方面都有大量应用，并且是一个活跃的研究领域。我们在这两个方面的方法是将问题转化为包含图像相似性和先验信息的成本函数的优化。为了实现广泛的临床应用，弹性成像和配准的关键要求是实时性能。拟议的研究计划将通过开发新颖的计算高效的图像分析技术来实现这一目标。 拟议的跨学科研究计划将在康考迪亚大学电气与计算机工程系和 PERFORM 中心的令人兴奋的环境中培训两名博士生和四名硕士生。