Computational methods in medical imaging

医学成像中的计算方法

• 批准号: 36901-2009
• 负责人: Trummer, Manfred
• 金额: $ 1.38万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2012
• 资助国家: 加拿大
• 起止时间: 2012-01-01 至 2013-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=503349
• 关键词: Computational; methods; medical; imaging

We propose to investigate computational and mathematical methods in medical imaging. The research is driven by applications from nuclear medicine, in particular SPECT (single photon emission computed tomography), although many of our results have applications to other image modalities such as PET and MRI. Our main mathematical interest lies in dynamic SPECT imaging. In static imaging, one single image is reconstructed from the data obtained during a patient scan; in the dynamic case, the same data are used to reconstruct a sequence of 3-D images (i.e., a 3D movie) to show the dynamic behaviour of uptake and wash-out of substances, indicating to the physician functional information about the organs. This is an ill-posed inverse problem, with the challenge of not having enough data to determine the unknowns. Hence, additional information must be incorporated into the solution algorithms to exclude possible solutions which are not physically meaningful. Our approaches are either of a stochastic nature (Kalman filter) or will be based on large scale constrained optimization methods.

我们建议研究计算和数学方法在医学成像。这项研究是由核医学的应用驱动的，特别是SPECT（单光子发射计算机断层扫描），尽管我们的许多结果也应用于其他图像模式，如PET和MRI。我们的主要数学兴趣在于动态SPECT成像。在静态成像中，从患者扫描期间获得的数据重建单个图像；在动态情况下，相同的数据被用来重建一系列3D图像（即3D电影），以显示物质摄取和冲洗的动态行为，向医生指示有关器官的功能信息。这是一个不适定逆问题，面临的挑战是没有足够的数据来确定未知数。因此，必须在解算法中加入附加信息，以排除可能的没有物理意义的解。我们的方法要么是随机性质的（卡尔曼滤波），要么是基于大规模约束优化方法。