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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

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Propagation of Uncertainty from Random Error00:59

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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...

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Born Normalization for Fluorescence Optical Projection Tomography for Whole Heart Imaging
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Hierarchical Bayesian regularization of reconstructions for diffuse optical tomography using multiple priors.

Farras Abdelnour, Christopher Genovese, Theodore Huppert

    Biomedical Optics Express
    |January 25, 2011
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    This summary is machine-generated.

    This study introduces a new method for diffuse optical tomography (DOT) brain imaging. The parametric restricted maximum likelihood (ReML) method improves spatial accuracy in optical imaging by incorporating statistical priors.

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    Area of Science:

    • Neuroimaging
    • Biomedical Optics
    • Medical Physics

    Background:

    • Diffuse optical tomography (DOT) is a non-invasive brain imaging method using near-infrared light.
    • DOT measures optical absorption changes related to regional blood flow and oxygenation.
    • Accurate spatial reconstruction of hemoglobin changes from DOT data is challenging due to the ill-posed nature of the inverse problem.

    Purpose of the Study:

    • To describe and demonstrate a parametric restricted maximum likelihood (ReML) method for DOT image reconstruction.
    • To incorporate multiple statistical priors into the recovery of optical images.
    • To adapt methods used in magnetoencephalography (MEG) for DOT applications.

    Main Methods:

    • Application of a parametric restricted maximum likelihood (ReML) method.
    • Incorporation of multiple statistical priors for model regularization.
    • Adaptation of established inverse problem solutions from MEG to DOT.

    Main Results:

    • Demonstration of the ReML method's ability to improve optical image recovery.
    • Objective incorporation of reconstruction constraints.
    • Validation through simulated numerical examples.

    Conclusions:

    • The ReML method offers a robust approach for enhancing spatial accuracy in DOT.
    • This technique provides an objective means to integrate prior information into image reconstruction.
    • The adapted model shows promise for advancing functional brain imaging with DOT.