Damon Hyde

Free-space fluorescence molecular tomography utilizing 360° geometry projections

Fluorescence tomography of diffuse media can yield optimal three-dimensional imaging when multiple projections over 360° geometries are captured, compared with limited projection angle systems such as implementations in the slab geometry. We demonstrate how it is possible to perform noncontact, 360° projection fluorescence tomography of mice using CCD-camera-based detection in free space, i.e., in the absence of matching fluids. This approach achieves high spatial sampling of photons propagating through tissue and yields a superior information content data set compared with fiber-based 360° implementations. Reconstruction feasibility using 36 projections in 10° steps is demonstrated in mice.

Hybrid FMT-CT imaging of amyloid-β plaques in a murine Alzheimer's disease model

The need to study molecular and functional parameters of Alzheimers disease progression in animal models has led to the development of disease-specific fluorescent markers. However, curved optical interfaces and a highly heterogeneous internal structure make quantitative fluorescence imaging of the murine brain a particularly challenging tomographic problem. We investigated the integration of X-ray computed tomography (CT) information into a state-of-the-art fluorescence molecular tomography (FMT) scheme and establish that the dual-modality approach is essential for high fidelity reconstructions of distributed fluorescence within the murine brain, as compared to conventional fluorescence tomography. We employ this method in vivo using a fluorescent oxazine dye to quantify amyloid-beta plaque burden in transgenic APP23 mice modeling Alzheimers disease. Multi-modal imaging allows for accurate signal localization and correlation of in vivo findings to ex vivo studies. The results point to FMT-CT as an essential tool for in vivo study of neurodegenerative disease in animal models and potentially humans.

Performance dependence of hybrid x-ray computed tomography/fluorescence molecular tomography on the optical forward problem

Hybrid imaging systems combining x-ray computed tomography (CT) and fluorescence tomography can improve fluorescence imaging performance by incorporating anatomical x-ray CT information into the optical inversion problem. While the use of image priors has been investigated in the past, little is known about the optimal use of forward photon propagation models in hybrid optical systems. In this paper, we explore the impact on reconstruction accuracy of the use of propagation models of varying complexity, specifically in the context of these hybrid imaging systems where significant structural information is known a priori. Our results demonstrate that the use of generically known parameters provides near optimal performance, even when parameter mismatch remains.

Visualization of pulmonary inflammation using noninvasive fluorescence molecular imaging

The ability to visualize molecular processes and cellular regulators of complex pulmonary diseases such as asthma, chronic obstructive pulmonary disease (COPD), or adult respiratory distress syndrome (ARDS), would aid in the diagnosis, differentiation, therapy assessment and in small animal-based drug-discovery processes. Herein we report the application of normalized transillumination and fluorescence molecular tomography (FMT) for the noninvasive quantitative imaging of the mouse lung in vivo. We demonstrate the ability to visualize and quantitate pulmonary response in a murine model of LPS-induced airway inflammation. Twenty-four hours prior to imaging, BALB/c female mice were injected via tail vein with 2 nmol of a cathepsin-sensitive activatable fluorescent probe (excitation: 750 nm; emission: 780 nm) and 2 nmol of accompanying intravascular agent (excitation: 674 nm; emission: 694 nm). Six hours later, the mice were anesthetized with isoflurane and administered intranasal LPS in sterile 0.9% saline in 25 microl aliquots (one per nostril). Fluorescence molecular imaging revealed the in vivo profile of cysteine protease activation and vascular distribution within the lung typifying the inflammatory response to LPS insult. Results were correlated with standard in vitro laboratory tests (Western blot, bronchoalveolar lavage or BAL analysis, immunohistochemistry) and revealed good correlation with the underlying activity. We demonstrated the capacity of fluorescence tomography to noninvasively and longitudinally characterize physiological, cellular, and subcellular processes associated with inflammatory disease burden in the lung. The data presented herein serve to further evince fluorescence molecular imaging as a technology highly appropriate for the biomedical laboratory.

Data Specific Spatially Varying Regularization for Multimodal Fluorescence Molecular Tomography

Fluorescence molecular tomography (FMT) allows in vivo localization and quantification of fluorescence biodistributions in whole animals. The ill-posed nature of the tomographic reconstruction problem, however, limits the attainable resolution. Improvements in resolution and overall imaging performance can be achieved by forming image priors from geometric information obtained by a secondary anatomical or functional high-resolution imaging modality such as X-ray computed tomography or magnetic resonance imaging. A particular challenge in using image priors is to avoid the use of assumptions that may bias the solution and reduced the accuracy of the inverse problem. This is particularly relevant in FMT inversions where there is not an evident link between secondary geometric information and the underlying fluorescence biodistribution. We present here a new, two step approach to incorporating structural priors into the FMT inverse problem. By using the anatomic information to define a low dimensional inverse problem, we obtain a solution which we then use to determine the parameters defining a spatially varying regularization matrix for the full resolution problem. The regularization term is thus customized for each data set and is guided by the data rather than depending only on user defined a priori assumptions. Results are presented for both simulated and experimental data sets, and show significant improvements in image quality as compared to traditional regularization techniques.

A Statistical Approach to Inverting the Born Ratio

We examine the problem of fluorescence molecular tomography using the normalized Born approximation, termed herein the Born ratio, from a statistical perspective. Experimentally verified noise models for received signals at the excitation and emission wavelengths are combined to generate a stochastic model for the Born ratio. This model is then utilized within a maximum likelihood framework to obtain an inverse solution based on a fixed point iteration. Results are presented for three experimental scenarios: phantom data with a homogeneous background, phantoms implanted within a small animal, and in vivo data using an exogenous probe.

Anisotropic Partial Volume CSF Modeling for EEG Source Localization

Electromagnetic source localization (ESL) provides non-invasive evaluation of brain electrical activity for neurology research and clinical evaluation of neurological disorders such as epilepsy. Accurate ESL results are dependent upon the use of patient specific models of bioelectric conductivity. While the effects of anisotropic conductivities in the skull and white matter have been previously studied, little attention has been paid to the accurate modeling of the highly conductive cerebrospinal fluid (CSF) region. This study examines the effect that partial volume errors in CSF segmentations have upon the ESL bioelectric model. These errors arise when segmenting sulcal channels whose widths are similar to the resolution of the magnetic resonance (MR) images used for segmentation, as some voxels containing both CSF and gray matter cannot be definitively assigned a single label. These problems, particularly prevalent in pediatric populations, make voxelwise segmentation of CSF compartments a difficult problem. Given the high conductivity of CSF, errors in modeling this region may result in large errors in the bioelectric model. We introduce here a new approach for using estimates of partial volume fractions in the construction of patient specific bioelectric models. In regions where partial volume errors are expected, we use a layered gray matter-CSF model to construct equivalent anisotropic conductivity tensors. This allows us to account for the inhomogeneity of the tissue within each voxel. Using this approach, we are able to reduce the error in the resulting bioelectric models, as evaluated against a known high resolution model. Additionally, this model permits us to evaluate the effects of sulci modeling errors and quantify the mean error as a function of the change in sulci width. Our results suggest that both under and over-estimation of the CSF region leads to significant errors in the bioelectric model. While a model with fixed partial volume fraction is able to reduce this error, we see the largest improvement when using voxel specific partial volume estimates. Our cross-model analyses suggest that an approximately linear relationship exists between sulci error and the error in the resulting bioelectric model. Given the difficulty of accurately segmenting narrow sulcal channels, this suggests that our approach may be capable of improving the accuracy of patient specific bioelectric models by several percent, while introducing only minimal additional computational requirements.

New techniques for data fusion in multimodal FMT-CT imaging

We examine approaches to the incorporation of anatomic structural information into the inverse problem of fluorescence molecular tomography (FMT). Using an appropriate relationship between anatomic and reconstruction image resolution, we build an inverse problem parameterized along the anatomical segmentation. These values serve as the basis for two new regularization techniques. The first regularizes individual voxels in proportion to the importance of the underlying segments in reducing the residual error. The second is based on a well known statistical interpretation of Tikhonov-type regularization in which the statistical prior is defined implicitly as the solution to a PDE whose structure is based on the anatomical segmentation. Results are shown using both techniques for a simulated experiment within the chest cavity of a mouse.

Extensions to a manifold learning framework for time-series analysis on dynamic manifolds in bioelectric signals.

This paper addresses the challenge of extracting meaningful information from measured bioelectric signals generated by complex, large scale physiological systems such as the brain or the heart. We focus on a combination of the well-known Laplacian eigenmaps machine learning approach with dynamical systems ideas to analyze emergent dynamic behaviors. The method reconstructs the abstract dynamical system phase-space geometry of the embedded measurements and tracks changes in physiological conditions or activities through changes in that geometry. It is geared to extract information from the joint behavior of time traces obtained from large sensor arrays, such as those used in multiple-electrode ECG and EEG, and explore the geometrical structure of the low dimensional embedding of moving time windows of those joint snapshots. Our main contribution is a method for mapping vectors from the phase space to the data domain. We present cases to evaluate the methods, including a synthetic example using the chaotic Lorenz system, several sets of cardiac measurements from both canine and human hearts, and measurements from a human brain.

Combined delay and graph embedding of epileptic discharges in EEG reveals complex and recurrent nonlinear dynamics

The dynamical structure of the brains electrical signals contains valuable information about its physiology. Here we combine techniques for nonlinear dynamical analysis and manifold identification to reveal complex and recurrent dynamics in interictal epileptiform discharges (IEDs). Our results suggest that recurrent IEDs exhibit some consistent dynamics, which may only last briefly, and so individual IED dynamics may need to be considered in order to understand their genesis. This could potentially serve to constrain the dynamics of the inverse source localization problem.