A simulation study of the variability of indocyanine green kinetics and using structural a priori information in dynamic contrast enhanced diffuse optical tomography (DCE-DOT)

Automated in vivo Assessment of Vascular Response to Radiation using a Hybrid Theranostic X-ray Irradiator/Fluorescence Molecular Imaging System

Hypofractionated stereotactic body radiotherapy treatments (SBRT) have demonstrated impressive results for the treatment of a variety of solid tumors. The role of tumor supporting vasculature damage in treatment outcome for SBRT has been intensely debated and studied. Fast, non-invasive, longitudinal assessments of tumor vasculature would allow for thorough investigations of vascular changes correlated with SBRT treatment response. In this paper, we present a novel theranostic system which incorporates a fluorescence molecular imager into a commercial, preclinical, microCT-guided, irradiator and was designed to quantify tumor vascular response (TVR) to targeted radiotherapy. This system overcomes the limitations of single-timepoint imaging modalities by longitudinally assessing spatiotemporal differences in intravenously-injected ICG kinetics in tumors before and after high-dose radiation. Changes in ICG kinetics were rapidly quantified by principle component (PC) analysis before and two days after 10 Gy targeted tumor irradiation. A classifier algorithm based on PC data clustering identified pixels with TVR. Results show that two days after treatment, a significant delay in ICG clearance as measured by exponential decay (40.5±16.1% P=0.0405 Paired t-test n=4) was observed. Changes in the mean normalized first and second PC feature pixel values (PC1 & PC2) were found (P=0.0559, 0.0432 paired t-test), suggesting PC based analysis accurately detects changes in ICG kinetics. The PC based classification algorithm yielded spatially-resolved TVR maps. Our first-of-its-kind theranostic system, allowing automated assessment of TVR to SBRT, will be used to better understand the role of tumor perfusion in metastasis and local control.

An extended analytical approach for diffuse optical imaging

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.

Bayesian Framework Based Direct Reconstruction of Fluorescence Parametric Images

Fluorescence imaging has been successfully used in the study of pharmacokinetic analysis, while dynamic fluorescence molecular tomography (FMT) is an attractive imaging technique for three-dimensionally resolving the metabolic process of fluorescent biomarkers in small animals in vivo. Parametric images obtained by combining dynamic FMT with compartmental modeling can provide quantitative physiological information for biological studies and drug development. However, images obtained with conventional indirect methods suffer from poor image quality because of failure in utilizing the temporal correlations of boundary measurements. Besides, FMT suffers from low spatial resolution due to its ill-posed nature, which further reduces the image quality. In this paper, we propose a novel method to directly reconstruct parametric images from boundary measurements based on maximum a posteriori (MAP) estimation with structural priors in a Bayesian framework. The proposed method can utilize structural priors obtained from an X-ray computed tomography system to mitigate the ill-posedness of dynamic FMT inverse problem, and use direct reconstruction strategy to make full use of temporal correlations of boundary measurements. The results of numerical simulations and in vivo mouse experiments demonstrate that the proposed method leads to significant improvements in the reconstruction quality of parametric images as compared with the conventional indirect method and a previously developed direct method.

Virtual source method for diffuse optical imaging

The Green's function for diffusive wave propagation can be obtained by utilizing the representation theorems of the convolution type and the correlation type. In this work, the Green's function is retrieved by making use of the Robin boundary condition and the representation theorems for diffusive media. The diffusive Green's function between two detectors for photon flux is calculated by combining detector readings due to point light sources and utilizing virtual light sources at the detector positions in optical tomography. Two dimensional simulations for a circular region with eight sources and eight detectors located on the boundary are performed using a finite element method to demonstrate the feasibility of virtual sources. The most important potential application would be the replacement of noisy measurements with synthetic measurements that are provided by the virtual sources. This becomes an important issue in small animal and human studies. In addition, the same method may also be used to reduce the imaging time.

Implicit and explicit prior information in near-infrared spectral imaging: accuracy, quantification and diagnostic value

Near-infrared spectroscopy (NIRS) of tissue provides quantification of absorbers, scattering and luminescent agents in bulk tissue through the use of measurement data and assumptions. Prior knowledge can be critical about things such as (i) the tissue shape and/or structure, (ii) spectral constituents, (iii) limits on parameters, (iv) demographic or biomarker data, and (v) biophysical models of the temporal signal shapes. A general framework of NIRS imaging with prior information is presented, showing that prior information datasets could be incorporated at any step in the NIRS process, with the general workflow being: (i) data acquisition, (ii) pre-processing, (iii) forward model, (iv) inversion/reconstruction, (v) post-processing, and (vi) interpretation/diagnosis. Most of the development in NIRS has used ad hoc or empirical implementations of prior information such as pre-measured absorber or fluorophore spectra, or tissue shapes as estimated by additional imaging tools. A comprehensive analysis would examine what prior information maximizes the accuracy in recovery and value for medical diagnosis, when implemented at separate stages of the NIRS sequence. Individual applications of prior information can show increases in accuracy or improved ability to estimate biochemical features of tissue, while other approaches may not. Most beneficial inclusion of prior information has been in the inversion/reconstruction process, because it solves the mathematical intractability. However, it is not clear that this is always the most beneficial stage.

Dynamic contrast-enhanced diffuse optical tomography (DCE-DOT): experimental validation with a dynamic phantom

Dynamic contrast-enhanced diffuse optical tomography (DCE-DOT) can provide spatially resolved enhancement kinetics of an optical contrast agent. We undertook a systematic phantom study to evaluate the effects of the geometrical parameters such as the depth and size of the inclusion as well as the optical parameters of the background on the recovered enhancement kinetics of the most commonly used optical contrast agent, indocyanine green (ICG). For this purpose a computer-controlled dynamic phantom was constructed. An ICG-intralipid-water mixture was circulated through the inclusions while the DCE-DOT measurements were acquired with a temporal resolution of 16 s. The same dynamic study was repeated using inclusions of different sizes located at different depths. In addition to this, the effect of non-scattering regions was investigated by placing a second inclusion filled with water in the background. The phantom studies confirmed that although the peak enhancement varied substantially for each case, the recovered injection and dilution rates obtained from the percentage enhancement maps agreed within 15% independent of not only the depth and the size of the inclusion but also the presence of a non-scattering region in the background. Although no internal structural information was used in these phantom studies, it may be necessary to use it for small objects buried deep in tissue. However, the different contrast mechanisms of optical and other imaging modalities as well as imperfect co-registration between both modalities may lead to potential errors in the structural a priori. Therefore, the effect of erroneous selection of structural priors was investigated as the final step. Again, the injection and dilution rates obtained from the percentage enhancement maps were also immune to the systematic errors introduced by erroneous selection of the structural priors, e.g. choosing the diameter of the inclusion 20% smaller increased the peak enhancement 60% but changed the injection and dilution rates only less than 10%.

A higher order diffusion model for three-dimensional photon migration and image reconstruction in optical tomography

In this work, we derived three-dimensional simplified spherical harmonics approximated higher order diffusion equations. We also solved the higher order diffusion equations using a finite element method and compared the solutions from the first-order diffusion equation and Monte Carlo simulations. We found that the conducted model is able to improve the first-order diffusion solution in a transport-like homogeneous or heterogeneous medium. Reconstructed images based on the higher order diffusion model are also presented. We conclude that the developed higher order diffusion model is able to accurately describe light propagation in biological tissues and to offer improved image reconstruction.