Objectives: Multimodality reporter gene imaging provides valuable, noninvasive information on the fate of engineered cell populations. To complement magnetic resonance imaging (MRI) measures of tumor volume and 2-dimensional reporter-based optical measures of cell viability, reporter-based MRI may offer 3-dimensional information on the distribution of viable cancer cells in deep tissues. Materials and Methods: Here, we engineered human and murine triple-negative breast cancer cells with lentivirus encoding tdTomato and firefly luciferase for fluorescence imaging and bioluminescence imaging (BLI). A subset of these cells was additionally engineered with lentivirus encoding organic anion transporting polypeptide 1a1 (Oatp1a1) for MRI. Oatp1a1 operates by transporting gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) into cells, and it concomitantly improves BLI substrate uptake. After orthotopic implantation of engineered cells expressing or not expressing Oatp1a1, longitudinal fluorescence imaging, BLI, and 3-Tesla MRI were performed. Results: Oatp1a1-expressing tumors displayed significantly increased BLI signals relative to control tumors at all time points (P < 0.05). On MRI, post-Gd-EOB-DTPA T 1 -weighted images of Oatp1a1-expressing tumors exhibited significantly increased contrast-to-noise ratios compared with control tumors and precontrast images (P < 0.05). At endpoint, tumors expressing Oatp1a1 displayed intratumoral MR signal heterogeneity not present at earlier time points. Pixel-based analysis of matched in vivo MR and ex vivo fluorescence microscopy images revealed a strong, positive correlation between MR intensity and tdTomato intensity for Oatp1a1-expressing tumors (P < 0.05), but not control tumors. Conclusions: These results characterize Oatp1a1 as a sensitive, quantitative, positive contrast MRI reporter gene for 3-dimensional assessment of viable cancer cell intratumoral distribution and concomitant BLI enhancement. This multimodality reporter gene system can provide new insights into the influence of viable cancer cell intratumoral distribution on tumor progression and metastasis, as well as improved assessments of anticancer therapies.
Stereoscopic endoscopes have been used increasingly in minimally invasive surgery to visualise the organ surface and manipulate various surgical tools. However, insufficient and irregular light sources become major challenges for endoscopic surgery. Not only do these conditions hinder image processing algorithms, sometimes surgical tools are barely visible when operating within low-light regions. In addition, low-light regions have low signal-to-noise ratio and metrication artefacts due to quantisation errors. As a result, present image enhancement methods usually suffer from heavy noise amplification in low-light regions. In this Letter, the authors propose an effective method for endoscopic image enhancement by identifying different illumination regions and designing the enhancement design criteria for desired image quality. Compared with existing image enhancement methods, the proposed method is able to enhance the low-light region while preventing noise amplification during image enhancement process. The proposed method is tested with 200 images acquired by endoscopic surgeries. Computed results show that the proposed algorithm can outperform state-of-the-art algorithms for image enhancement, in terms of naturalness image quality evaluator and illumination index.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.