We investigate the uptake of a nontargeted contrast agent by breast tumors using a continuous wave diffuse optical tomography apparatus. The instrument operates in the near-infrared spectral window and employs 16 sources and 16 detectors to collect light in parallel on the surface of the tumor-bearing breast (coronal geometry). In our protocol an extrinsic contrast agent, Indocyanine Green (ICG), was injected by bolus. Three clinical scenarios with three different pathologies were investigated. A two-compartment model was used to analyze the pharmacokinetics of ICG and preprocess the data, and diffuse optical tomography was used for imaging. Localization and delineation of the tumor was achieved in good agreement with a priori information. Moreover, different dynamical features were observed for differing pathologies. The malignant cases exhibited slower rate constants (uptake and outflow) compared to healthy tissue. These results provide further evidence that in vivo pharmacokinetics of ICG in breast tumors may be a useful diagnostic tool for differentiation of benign and malignant pathologies.
Developing methods that provide adequate vascular perfusion is an important step toward engineering large functional tissues. Meanwhile, an imaging modality to assess the three-dimensional (3-D) structures and functions of the vascular channels is lacking for thick matrices (>2~3mm). Herein, we report on an original approach to construct and image 3-D dynamically perfused vascular structures in thick hydrogel scaffolds. In this work, we integrated a robotic 3-D cell-printing technology with a mesoscopic fluorescence molecular tomography imaging system, and demonstrated the capability of the platform to construct perfused collagen scaffolds with endothelial lining and to image both the fluid flow and fluorescent-labeled living endothelial cells at high-frame rates, with high sensitivity and accuracy. These results establish the potential of integrating both 3-D cell-printing and fluorescence mesoscopic imaging for functional and molecular studies in complex tissue engineered tissues.
Fluorescence lifetime imaging (FLI) provides unique quantitative information in biomedical and molecular biology studies but relies on complex data-fitting techniques to derive the quantities of interest. Herein, we propose a fit-free approach in FLI image formation that is based on deep learning (DL) to quantify fluorescence decays simultaneously over a whole image and at fast speeds. We report on a deep neural network (DNN) architecture, named fluorescence lifetime imaging network (FLI-Net) that is designed and trained for different classes of experiments, including visible FLI and near-infrared (NIR) FLI microscopy (FLIM) and NIR gated macroscopy FLI (MFLI). FLI-Net outputs quantitatively the spatially resolved lifetime-based parameters that are typically employed in the field. We validate the utility of the FLI-Net framework by performing quantitative microscopic and preclinical lifetime-based studies across the visible and NIR spectra, as well as across the 2 main data acquisition technologies. These results demonstrate that FLI-Net is well suited to accurately quantify complex fluorescence lifetimes in cells and, in real time, in intact animals without any parameter settings. Hence, FLI-Net paves the way to reproducible and quantitative lifetime studies at unprecedented speeds, for improved dissemination and impact of FLI in many important biomedical applications ranging from fundamental discoveries in molecular and cellular biology to clinical translation.
The conjugation of anti-cancer drugs to endogenous ligands has proven to be an effective strategy to enhance their pharmacological selectivity and delivery towards neoplasic tissues. Since cell proliferation has a strong requirement for iron, cancer cells express high levels of transferrin receptors (TfnR), making its ligand, transferrin (Tfn), of great interest as a delivery agent for therapeutics. However, a critical gap exists in the ability to non-invasively determine whether drugs conjugated to Tfn are internalized into target cells in vivo. Due to the enhanced permeability and retention (EPR) effect, it remains unknown whether these Tfn-conjugated drugs are specifically internalized into cancer cells or are localized non-specifically as a result of a generalized accumulation of macromolecules near tumors. By exploiting the dimeric nature of the TfnR that binds two molecules of Tfn in close proximity, we utilized a Förster Resonance Energy Transfer (FRET) based technique that can discriminate bound and internalized Tfn from free, soluble Tfn. In order to non-invasively visualize intracellular amounts of Tfn in tumors through live animal tissues, we developed a novel near infrared (NIR) fluorescence lifetime FRET imaging technique that uses an active wide-field time gated illumination platform. In summary, we report that the NIR fluorescence lifetime FRET technique is capable of non-invasively detecting bound and internalized forms of Tfn in cancer cells and tumors within a live small animal model, and that our results are quantitatively consistent when compared to well-established intensity-based FRET microscopy methods used in in vitro experiments.
Diffuse optical imaging is an emerging modality that uses Near Infrared (NIR) light to reveal structural and functional information of deep biological tissue. It provides contrast mechanisms for molecular, chemical, and anatomical imaging that is not available from other imaging modalities. Diffuse Optical Tomography (DOT) deals with 3D reconstruction of optical properties of tissue given the measurements and a forward model of photon propagation. DOT has inherently low spatial resolution due to diffuse nature of photons. In this work, we focus to improve the spatial resolution and the quantitative accuracy of DOT by using a priori anatomical information specific to unknown image. Such specific a priori information can be obtained from a secondary high-resolution imaging modality such as Magnetic Resonance (MR) or X-ray. Image reconstruction is formulated within a Bayesian framework to determine the spatial distribution of the absorption coefficients of the medium. A spatially varying a priori probability density function is designed based on the segmented anatomical information. Conjugate gradient method is utilized to solve the resulting optimization problem. Proposed method is evaluated using simulation and phantom measurements collected with a novel timeresolved optical imaging system. Results demonstrate that the proposed method leads to improved spatial resolution, quantitative accuracy and faster convergence than standard least squares approach.
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