Optical tomography is a new medical imaging modality that is at the threshold of realization. A large amount of clinical work has shown the very real benefits that such a method could provide. At the same time a considerable effort has been put into theoretical studies of its probable success. At present there exist gaps between these two realms. In this paper we review some general approaches to inverse problems to set the context for optical tomography, defining both the terms
forward problem
and
inverse problem
. An essential requirement is to treat the problem in a nonlinear fashion, by using an iterative method. This in turn requires a convenient method of evaluating the forward problem, and its derivatives and variance. Photon transport models are described and methods for obtaining analytical and numerical solutions for the most commonly used ones are reviewed. The inverse problem is approached by classical gradient–based solution methods. In order to develop practical implementations of these methods, we discuss the important topic of
photon measurement density functions
, which represent the derivative of the forward problem. We show some results that represent the most complex and realistic simulations of optical tomography yet developed. We suggest, in particular, that both time–resolved, and intensity–modulated systems can reconstruct variations in both optical absorption and scattering, but that unmodulated, non–time–resolved systems are prone to severe artefact. We believe that optical tomography reconstruction methods can now be reliably applied to a wide variety of real clinical data. The expected resolution of the method is poor, meaning that it is unlikely that the type of high–resolution images seen in computed tomography or medical resonance imaging can ever be obtained. Nevertheless we strongly expect the functional nature of these images to have a high degree of clinical significance.
Synchronized multiartifact reduction with tomographic reconstruction (SMART-RECON): A statistical model based iterative image reconstruction method to eliminate limited-view artifacts and to mitigate the temporal-average artifacts in time-resolved CT