Statistical approach for detection and localization of a fluorescing mouse tumor in Intralipid

Incorporating the effects of objects in an approximate model of light transport in scattering media

A computationally efficient radiative transport model is presented that predicts a camera measurement and accounts for the light reflected and blocked by an object in a scattering medium. The model is in good agreement with experimental data acquired at the Sandia National Laboratory Fog Chamber Facility (SNLFC). The model is applicable in computational imaging to detect, localize, and image objects hidden in scattering media. Here, a statistical approach was implemented to study object detection limits in fog.

Localization of Fluorescent Targets in Deep Tissue With Expanded Beam Illumination for Studies of Cancer and the Brain

Imaging fluorescence through millimeters or centimeters of tissue has important in vivo applications, such as guiding surgery and studying the brain. Often, the important information is the location of one of more optical reporters, rather than the specifics of the local geometry, motivating the need for a localization method that provides this information. We present an optimization approach based on a diffusion model for the fast localization of fluorescent inhomogeneities in deep tissue with expanded beam illumination that simplifies the experiment and the reconstruction. We show that the position of a fluorescent inhomogeneity can be estimated while assuming homogeneous tissue parameters and without having to model the excitation profile, reducing the computational burden and improving the utility of the method. We perform two experiments as a demonstration. First, a tumor in a mouse is localized using a near infrared folate-targeted fluorescent agent (OTL38). This result shows that localization can quickly provide tumor depth information, which could reduce damage to healthy tissue during fluorescence-guided surgery. Second, another near infrared fluorescent agent (ATTO647N) is injected into the brain of a rat, and localized through the intact skull and surface tissue. This result will enable studies of protein aggregation and neuron signaling.

Multiresolution Localization with Temporal Scanning for Super-Resolution Diffuse Optical Imaging of Fluorescence

A super-resolution optical imaging method is presented that relies on the distinct temporal information associated with each fluorescent optical reporter to determine its spatial position to high precision with measurements of heavily scattered light. This multiple-emitter localization approach uses a diffusion equation forward model in a cost function, and has the potential to achieve micron-scale spatial resolution through centimeters of tissue. Utilizing some degree of temporal separation for the reporter emissions, position and emission strength are determined using a computationally efficient time stripping multiresolution algorithm. The approach circumvents the spatial resolution challenges faced by earlier optical imaging approaches using a diffusion equation forward model, and is promising for in vivo applications. For example, in principle, the method could be used to localize individual neurons firing throughout a rodent brain, enabling direct imaging of neural network activity.

Diffuse optical localization of blood vessels and 3D printing for guiding oral surgery

Diffuse optical imaging through centimeters of tissue has emerged as a powerful tool in biomedical research. However, applications in the operating theater have been limited in part due to data set requirements and computational burden. We present an approach that uses a small number of optical source-detector pairs that allows for the fast localization of arteries in the roof of the mouth and has the potential to reduce complications during oral surgery. The arteries are modeled as multiple-point absorbers, allowing localization of their complex shapes. The method is demonstrated using a printed tissue-simulating mouth phantom. Furthermore, we use the extracted position information to fabricate a custom surgical guide using 3D printing that could protect the arteries during surgery.

Accessing Structurally Diverse Near-Infrared Cyanine Dyes for Folate Receptor-Targeted Cancer Cell Staining

Folate receptor (FR) targeting is one of the most promising strategies for the development of small-molecule-based cancer imaging agents considering that the FR is highly overexpressed on the surface of many cancer cell types. FR-targeted conjugates of near-infrared (NIR) emissive cyanine dyes are in advanced clinical trials for fluorescence-guided surgery and are valuable research tools for optical molecular imaging in animal models. Only a small number of promising conjugates has been evaluated so far. Analysis of structure-performance relations to identify critical factors modulating the performance of targeted conjugates is essential for successful further optimization. This contribution addresses the need for convenient synthetic access to structurally diverse NIR-emissive cyanine dyes for conjugation with folic acid. Structural variations were introduced to readily available cyanine precursors in particular via C-C-coupling reactions including Suzuki and (for the first time with these types of dyes) Sonogashira cross-couplings. Photophysical properties such as absorbance maxima, brightness, and photostability are highly dependent on the molecular structure. Selected modified cyanines were conjugated to folic acid for cancer cell targeting. Several conjugates display a favorable combination of high fluorescence brightness and photostability with high affinity to FR-positive cancer cells, and enable the selective imaging of these cells with low background.

On the use of the Cramér-Rao lower bound for diffuse optical imaging system design

We evaluated the potential of the Cramér-Rao lower bound (CRLB) to serve as a design metric for diffuse optical imaging systems. The CRLB defines the best achievable precision of any estimator for a given data model; it is often used in the statistical signal processing community for feasibility studies and system design. Computing the CRLB requires inverting the Fisher information matrix (FIM), however, which is usually ill-conditioned (and often underdetermined) in the case of diffuse optical tomography (DOT). We regularized the FIM by assuming that the inhomogeneity to be imaged was a point target and assessed the ability of point-target CRLBs to predict system performance in a typical DOT setting in silico. Our reconstructions, obtained with a common iterative algebraic technique, revealed that these bounds are not good predictors of imaging performance across different system configurations, even in a relative sense. This study demonstrates that agreement between the trends predicted by the CRLBs and imaging performance obtained with reconstruction algorithms that rely on a different regularization approach cannot be assumed a priori. Moreover, it underscores the importance of taking into account the intended regularization method when attempting to optimize source-detector configurations.

Fluorescent molecular imaging: technical progress and current preclinical and clinical applications in urogynecologic diseases

Many molecular imaging probes have been developed in recent years that hold great promise for both diagnostic and therapeutic functions in urogynecologic disease. Historically, optical probe designs were based on either endogenous or exogenous fluorophores. More recently, organic fluorophore probes have been engineered to target specific tissues and emit fluorescence only upon binding to targets. Several different photochemical mechanisms of activation exist. This review presents a discussion of the history and development of molecular imaging probe designs and provides an overview of successful preclinical and clinical models employing molecular probes for in vivo imaging of urogynecologic cancers.

Maximum likelihood tomographic reconstruction of extremely sparse solutions in diffuse fluorescence flow cytometry

We apply reparameterization and the maximum likelihood method to a specific fluorescence-mediated tomography problem where the solution is known a priori to be extremely sparse (i.e., all image values are zero except for one). Our algorithm performs significantly better than a standard image reconstruction method, particularly for deep-seated targets, and achieves close to 150 μm accuracy in a 3 mm diameter cross-sectional area with only 12 measurements. Moreover, results do not depend on the selection of a regularization parameter or other ad hoc values, and since reconstructions can be computed very quickly, the algorithm is also suitable for real-time implementation.

Cramer-Rao analysis of steady-state and time-domain fluorescence diffuse optical imaging

Using a Cramer-Rao analysis, we study the theoretical performances of a time and spatially resolved fDOT imaging system for jointly estimating the position and the concentration of a point-wide fluorescent volume in a diffusive sample. We show that the fluorescence lifetime is a critical parameter for the precision of the technique. A time resolved fDOT system that does not use spatial information is also considered. In certain cases, a simple steady-state configuration may be as efficient as this time resolved fDOT system.

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.

Tumor detection using folate receptor-targeted imaging agents

Folate receptors are up-regulated on a variety of human cancers, including cancers of the breast, ovaries, endometrium, lungs, kidneys, colon, brain, and myeloid cells of hematopoietic origin. This over-expression of folate receptors (FR) on cancer tissues can be exploited to target folate-linked imaging and therapeutic agents specifically to FR-expressing tumors, thereby avoiding uptake by most healthy tissues that express few if any FR. Four folate-targeted therapeutic drugs are currently undergoing clinical trials, and several folate-linked chemotherapeutic agents are in late stage preclinical development. However, because not all cancers express FR, and because only FR-expressing cancers respond to FR-targeted therapies, FR-targeted imaging agents have been required to select patients with FR-expressing tumors likely to respond to folate-targeted therapies. This review focuses on recent advances in the use of the vitamin folic acid to target PET agents, gamma-emitters, MRI contrast agents and fluorescent dyes to FR(+) cancers for the purpose of diagnosing and imaging malignant masses with improved specificity and sensitivity.

Polarized temporal impulse response for scattering media from third-order frequency correlations of speckle intensity patterns

Second- and third-order frequency correlations of speckle intensity patterns are used to characterize scattering media for multiple polarization states. The polarized temporal responses thus obtained are sensitive to the degree of scatter, with results being predictable by a diffusion model with sufficiently strong scatter. Experimental data are used to reconstruct various transfer functions.

Fluorescence molecular tomography in the presence of background fluorescence

Fluorescence molecular tomography is an emerging imaging technique that resolves the bio-distribution of engineered fluorescent probes developed for in vivo reporting of specific cellular and sub-cellular targets. The method can detect fluorochromes in picomole amounts or less, imaged through entire animals, but the detection sensitivity and imaging performance drop in the presence of background, non-specific fluorescence. In this study, we carried out a theoretical and an experimental investigation on the effect of background fluorescence on the measured signal and on the tomographic reconstruction. We further examined the performance of three subtraction methods based on physical models of photon propagation, using experimental data on phantoms and small animals. We show that the data pre-processing with subtraction schemes can improve image quality and quantification when non-specific background florescence is present.

Estimation of kinetic model parameters in fluorescence optical diffusion tomography

We present a technique for reconstructing the spatially dependent dynamics of a fluorescent contrast agent in turbid media. The dynamic behavior is described by linear and nonlinear parameters of a compartmental model or some other model with a deterministic functional form. The method extends our previous work in fluorescence optical diffusion tomography by parametrically reconstructing the time-dependent fluorescent yield. The reconstruction uses a Bayesian framework and parametric iterative coordinate descent optimization, which is closely related to Gauss-Seidel methods. We demonstrate the method with a simulation study.