Iterative Methods in Image Reconstruction

图像重建中的迭代方法

• 批准号: 0075239
• 负责人: James Nagy
• 金额: $ 13万
• 依托单位: Emory University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2001
• 资助国家: 美国
• 起止时间: 2001-04-01 至 2004-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0075239&HistoricalAwards=false
• 关键词: Iterative; Methods; Image; Reconstruction

The investigator develops efficient and reliable iterativemethods for reconstructing an image from recorded, noisy data.To obtain better resolved images, he develops methods that caneffectively handle complicated operators (e.g., spatially variantkernels) and methods that enforce a nonnegativity constraint. The emergence of increasingly sophisticated imaging deviceshas produced a new generation of very difficult computationalproblems in which an image is to be reconstructed from recordeddata. Such problems arise, for example, in breast cancerdetection, where there is a tradeoff between the needs to obtainhigh resolution images and to limit the radiation dose to thepatient. New devices have also been recently proposed for3-dimensional imaging, where high dimensionality and resolutionrequirements result in nontrivial computational complexity. Yetanother example occurs in new ground-based telescopes, which useadaptive optics techniques. To extract detailed information andtake advantage of the increase in resolution that can be obtainedfrom new imaging devices, it is important to develop methods thattake into account spatial changes of properties in the imagingmechanisms, which are not amenable to standard fast Fouriertransform based methods. New fully 3-dimensional medical imagingdevices require a new generation of computational methods toefficiently reconstruct images from recorded data, and in orderfor these new computational methods to find their way to clinicaluse, it is important to provide software packages that allow foreasy implementation and experimentation. Methods that enforcenonnegativity in the computed solution, and that can efficientlyhandle spatially variant kernels, are developed. Many of thecomputational tools developed in this project should beapplicable to a wide class of iterative image reconstructionmethods, including linear, nonlinear and statistical basedmethods.

研究人员开发了有效和可靠的迭代方法，用于从记录的噪声数据中重建图像。为了获得更好的分辨率图像，他开发了能够有效处理复杂运算符的方法（例如，空间变化核）和强制非负约束的方法。 日益复杂的成像设备的出现产生了新一代非常困难的计算问题，其中图像是从记录的数据重建。 例如，在乳腺癌检测中，需要在获得高分辨率图像和限制对患者的辐射剂量之间进行权衡。 最近还提出了用于三维成像的新设备，其中高维度和分辨率要求导致非平凡的计算复杂性。 另一个例子出现在新的地面望远镜，它使用自适应光学技术。 为了提取详细的信息，并利用可以从新的成像设备中获得的分辨率的增加，重要的是开发考虑成像机制中的属性的空间变化的方法，这不适合于基于标准快速傅立叶变换的方法。 新的全三维医学成像设备需要新一代的计算方法来有效地从记录的数据中重建图像，并且为了使这些新的计算方法找到它们的临床用途，重要的是提供允许进行简单实施和实验的软件包。 方法，enforcenonnegativity在计算的解决方案，并能有效地处理空间变化的内核，开发。 在这个项目中开发的许多计算工具应该适用于广泛的迭代图像重建方法，包括线性，非线性和基于统计的方法。