This work demonstrates the usefulness of 3D printing for optical imaging applications. Progress in developing optical imaging for biomedical applications requires customizable and often complex objects for testing and evaluation. There is therefore high demand for what have become known as tissue-simulating “phantoms.” We present a new optical phantom fabricated using inexpensive 3D printing methods with multiple materials, allowing for the placement of complex inhomogeneities in complex or anatomically realistic geometries, as opposed to previous phantoms, which were limited to simple shapes formed by molds or machining. We use diffuse optical imaging to reconstruct optical parameters in 3D space within a printed mouse to show the applicability of the phantoms for developing whole animal optical imaging methods. This phantom fabrication approach is versatile, can be applied to optical imaging methods besides diffusive imaging, and can be used in the calibration of live animal imaging data.

Three-dimensional (3D) printing allows for complex or physiologically realistic phantoms, useful, for example, in developing biomedical imaging methods and for calibrating measured data. However, available 3D printing materials provide a limited range of static optical properties. We overcome this limitation with a new method using stereolithography that allows tuning of the printed phantom’s optical properties to match that of target tissues, accomplished by printing a mixture of polystyrene microspheres and clear photopolymer resin. We show that Mie theory can be used to design the optical properties, and demonstrate the method by fabricating a mouse phantom and imaging it using fluorescence optical diffusion tomography.

The multiple scattering of light presents major challenges in realizing useful in vivo imaging at tissue depths of more than about one millimeter where many answers to health questions lie.Visible through near-infrared photons can be readily and safely detected through centimeters of tissue, however limited information is available for image formation. One strategy for obtaining images is to model the photon transport, and a simple incoherent model is the diffusion equation approximation to the Boltzmann transport equation. Such an approach provides a prediction of the mean intensity of heavily scattered light and hence provides a forward model for optimizationbased computational imaging. While diffuse optical imaging methods have received substantial attention, they remain restricted in terms of resolution because of the loss of high spatial frequency information that is associated with the multiple scattering of photons. Consequently, only relatively large inhomogeneities, such as tumors or organs in small animals, can be effectively resolved.Here, we introduce a super-resolution imaging approach based on point localization in a diffusion framework that enables over two orders of magnitude improvement in the spatial resolution of diffuse optical imaging. The method is demonstrated experimentally by localizing a fluorescent inhomogeneity in a highly scattering slab and characterizing the localization uncertainty. The approach allows imaging through centimeters of tissue with a resolution of tens of microns, thereby enabling cells or cell clusters to be resolved. More generally, this high-resolution imaging approach could be applied with any physical transport or wave model and hence to a broad class of physical problems. Paired with a suitable optical contrast mechanism, as can be realized with targeted fluorescent molecules or genetically-modified animals, super-resolution diffuse imaging should open new dimensions for in vivo applications. * webb@purdue.edu

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