Multigrid Optical Diffusion Tomography

最近，与基于超声、X射线计算机断层扫描（CT）或磁共振（MRI）的更常规的模态相反，对基于光的医学成像模态的开发的兴趣越来越大。基于光的成像具有非侵入性、安全且仅需要廉价仪器的优点。传统观点认为光不能穿过组织，但事实上，近红外光确实穿过组织，因此原则上可以用于形成组织内部的图像。然而，穿过组织的光是高度散射的，使得不能使用常规方法计算图像。这项研究关注的是新的计算算法，可用于形成三维图像的测量散射光的发展。这些方法，统称为光学扩散层析成像，有可能提供安全，廉价，便携式imaging instruments.Practical实现的光学扩散imaging需要一个困难的非线性逆问题，以计算易处理的方式来解决，以产生准确的图像。在这项研究中，新的多重网格优化方法的开发，有可能显着加快重建。这些新方法应用在贝叶斯框架中，并使用新技术来模拟包含空隙，不透明组织或荧光成像剂的区域。此外，光学扩散层析成像是一个更广泛的一类重要的逆问题的代表，使在这项研究中开发的方法有应用范围从图像配准和运动估计的问题，环境传感和非破坏性评估。

Recently, there has been growing interest in the development of medical imaging modalities based on light, as opposed to more conventional modalities based on ultrasound, X-ray computed tomography (CT), or magnetic resonance (MRI). Imaging based on light has the advantages of being non-invasive, safe, and requiring only inexpensive instrumentation. Conventional wisdom holds that light can not pass through tissue, but in fact, near infra-red light does pass through tissue and can therefore, in principle, be used to form images of the interior of the tissue. However, light passing through tissue is highly scattered, so that an image can not be calculated using conventional methods. This research is concerned with the development of new computational algorithms that can be used to form three-dimensional images from measurements of scattered light. These methods, known collectively as optical diffusion tomography, have the potential to provide safe, inexpensive, and portable imaging instruments.Practical realization of optical diffusion imaging requires that a difficult non-linear inverse problem be solved in a computationally tractable manner to yield accurate images. In this research, new multigrid optimization methods are developed which have the potential to dramatically speed reconstruction. These new methods are applied in a Bayesian framework and use novel techniques to model the regions containing voids, opaque tissue, or fluorescence imaging agents. Moreover, optical diffusion tomography is representative of a broader class of important inverse problems; so that the methods developed in this research have application in problems ranging from image registration and motion estimation to environmental sensing and non-destructive evaluation.