Multigrid Optical Diffusion Tomography

Purdue Research Foundation, West Lafayette IN

Investigators

Abstract

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.