We present experimental results that validate our imaging technique termed photomagnetic imaging (PMI). PMI illuminates the medium under investigation with a near-infrared light and measures the induced temperature increase using magnetic resonance imaging. A multiphysics solver combining light and heat propagation is used to model spatiotemporal distribution of temperature increase. Furthermore, a dedicated PMI reconstruction algorithm has been developed to reveal high-resolution optical absorption maps from temperature measurements. Being able to perform measurements at any point within the medium, PMI overcomes the limitations of conventional diffuse optical imaging. We present experimental results obtained on agarose phantoms mimicking biological tissue with inclusions having either different sizes or absorption contrasts, located at various depths. The reconstructed images show that PMI can successfully resolve these inclusions with high resolution and recover their absorption coefficient with high-quantitative accuracy. Even a 1-mm inclusion located 6-mm deep is recovered successfully and its absorption coefficient is underestimated by only 32%. The improved PMI system presented here successfully operates under the maximum skin exposure limits defined by the American National Standards Institute, which opens up the exciting possibility of its future clinical use for diagnostic purposes.

We present a new method allowing the reconstruction of 3D time-domain diffuse optical tomography images, based on the time-dependent diffusion equation and an iterative algorithm (ART) using specific points on the temporal profiles. The first advantage of our method versus the full time-resolved scheme consists in considerably reducing the inverse problem resolution time. Secondly, in the presence of scattering heterogeneities, our method provides images of better quality comparatively to classical methods using full-time data or the first moments of the profiles.

We present the feasibility of structured-light-based diffuse optical tomography (DOT) to quantify the breast density with an extensive simulation study. This study is performed on multiple numerical breast phantoms built from magnetic resonance imaging (MRI) images. These phantoms represent realistic tissue morphologies and are given typical breast optical properties. First, synthetic data are simulated at five wavelengths using our structured-light-based DOT forward problem. Afterwards, the inverse problem is solved to obtain the absorption images and subsequently the chromophore concentration maps. Parameters, such as segmented volumes and mean concentrations, are extracted from these maps and used in a regression model to estimate the percent breast densities. These estimations are correlated with the true values from MRI, r=0.97, showing that our new technique is promising in measuring breast density.

In this work, we introduce an analytical method to solve the diffusion equation in a cylindrical geometry. This method is based on an integral approach to derive the Green’s function for specific boundary conditions. Using our approach, we obtain comprehensive analytical solutions with the Robin boundary condition for diffuse optical imaging in both two and three dimensions. The solutions are expressed in terms of the optical properties of tissue and the amplitude and position of the light source. Our method not only works well inside the tissue but provides very accurate results near the tissue boundaries as well. The results obtained by our method are first compared with those obtained by a conventional analytical method then validated using numerical simulations. Our new analytical method allows not only implementation of any boundary condition for a specific problem but also fast simulation of light propagation making it very suitable for iterative image reconstruction algorithms.

In this work, we present a new analytical approach to model continuous wave laser induced temperature in highly homogeneous turbid media. First, the diffusion equation is used to model light transport and a comprehensive solution is derived analytically by obtaining a special Greens' function. Next, the time-dependent bio-heat equation is used to describe the induced heat increase and propagation within the medium. The bio-heat equation is solved analytically utilizing the separation of variables technique. Our theoretical model is successfully validated using numerical simulations and experimental studies with agarose phantoms and ex-vivo chicken breast samples. The encouraging results show that our method can be implemented as a simulation tool to determine important laser parameters that govern the magnitude of temperature rise within homogenous biological tissue or organs.