Most current imaging systems developed for tomographic investigations of intact tissues using diffuse photons suffer from a limited number of sources and detectors. In this paper we describe the construction and evaluation of a large dataset, low noise tomographic system for fluorescence imaging in small animals. The system consists of a parallel plate-imaging chamber and a lens coupled CCD camera, which enables conventional planar imaging as well as fluorescence tomography. The planar imaging data are used to guide the acquisition of a Fluorescence Molecular Tomography (FMT) dataset containing more than 106 measurements, and to superimpose anatomical features with tomographic results for improved visual representation. Experimental measurements exhibited good agreement with the diffusion theory models used to predict light propagation within the chamber. Tests of the instrument's capacity to quantitatively reconstruct fluorochrome distributions in three dimensions showed less than 5% errors between actual fluorochrome concentrations and FMT findings, and suggested a detection threshold of approximately 100 femptomoles for small localized objects. Experiments to assess the instrument's spatial resolution demonstrated the ability of the system to resolve objects placed at clear distances of less than 1 mm. This is a significant resolution increase over previously developed systems for animal imaging, and is primarily due to the large dataset employed and the use of inversion methods. Finally, the in vivo imaging capacity is showcased. It is expected that the large dataset collected can enable superior imaging of molecular probes in vivo and improve quantification of fluorescence signatures.
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http://dx.doi.org/10.1118/1.1568977 | DOI Listing |
Ann Bot
January 2025
Division of BioInvasions, Global Change & Macroecology, University of Vienna, Austria.
Background And Aims: Despite accelerating interest in island evolution, the general evolutionary trajectories of island flowers remain poorly understood. In particular the island rule, which posits that small organisms become larger and large organisms to become smaller after island colonization, while tested in various plant traits, has never been tested in flower size. Here, we provide the first test for the island rule in flower size for animal- and wind-pollinated flowers, and the first evidence for generalized in-situ evolution of flower size on islands.
View Article and Find Full Text PDFSci Rep
January 2025
Fischell Department of Bioengineering, University of Maryland, College Park, USA.
The development of optical sensors for label-free quantification of cell parameters has numerous uses in the biomedical arena. However, using current optical probes requires the laborious collection of sufficiently large datasets that can be used to calibrate optical probe signals to true metabolite concentrations. Further, most practitioners find it difficult to confidently adapt black box chemometric models that are difficult to troubleshoot in high-stakes applications such as biopharmaceutical manufacturing.
View Article and Find Full Text PDFPhys Med Biol
January 2025
Department of Trauma and Reconstructive Surgery, BG Hospital Bergmanntrost, Merseburger Straße 165 06112 Halle, Halle, Sachsen-Anhalt, 06112, GERMANY.
The purpose of this study was to develop a robust deep learning approach trained with a small in-vivo MRI dataset for multi-label segmentation of all eight carpal bones for therapy planning and wrist dynamic analysis. Approach: A small dataset of 15 3.0-T MRI scans from five health subjects was employed within this study.
View Article and Find Full Text PDFJ Am Med Inform Assoc
January 2025
Department of Biomedical Informatics, Columbia University, New York, NY 10032, United States.
Objective: Extracting PICO elements-Participants, Intervention, Comparison, and Outcomes-from clinical trial literature is essential for clinical evidence retrieval, appraisal, and synthesis. Existing approaches do not distinguish the attributes of PICO entities. This study aims to develop a named entity recognition (NER) model to extract PICO entities with fine granularities.
View Article and Find Full Text PDFLearn Health Syst
January 2025
Department of Biostatistics, Epidemiology and Informatics University of Pennsylvania Perelman School of Medicine Philadelphia Pennsylvania USA.
Introduction: The rapid adoption of electronic health record (EHR) systems has resulted in extensive archives of data relevant to clinical research, hospital operations, and the development of learning health systems. However, EHR data are not frequently available, cleaned, standardized, validated, and ready for use by stakeholders. We describe an in-progress effort to overcome these challenges with cooperative, systematic data extraction and validation.
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