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Measurements of dynamic near-infrared (NIR) light attenuation across the human head together with model-based image reconstruction algorithms allow the recovery of three-dimensional spatial brain activation maps. Previous studies using high-density diffuse optical tomography (HD-DOT) systems have reported improved image quality over sparse arrays. These HD-DOT systems incorporated multidistance overlapping continuous wave measurements that only recover differential intensity attenuation. We investigate the potential improvement in reconstructed image quality due to the additional incorporation of phase shift measurements, which reflect the time-of-flight of the measured NIR light, within the tomographic reconstruction from high-density measurements. To evaluate image reconstruction with and without the additional phase information, we simulated point spread functions across a whole-scalp field of view in 24 subject-specific anatomical models using an experimentally derived noise model. The addition of phase information improves the image quality by reducing localization error by up to 59% and effective resolution by up to 21% as compared to using the intensity attenuation measurements alone. Furthermore, we demonstrate that the phase data enable images to be resolved at deeper brain regions where intensity data fail, which is further supported by utilizing experimental data from a single subject measurement during a retinotopic experiment.
Using high density diffuse optical tomography probes, quantitatively accurate parameters from different layers (skin, bone and brain) can be recovered from subject specific reconstruction models. This study assesses the use of registered atlas models for situations where subject specific models are not available. Data simulated from subject specific models were reconstructed using the 8 registered atlas models implementing a regional (layered) parameter recovery in NIRFAST. A 3-region recovery based on the atlas model yielded recovered brain saturation values which were accurate to within 4.6% (percentage error) of the simulated values, validating the technique. The recovered saturations in the superficial regions were not quantitatively accurate. These findings highlight differences in superficial (skin and bone) layer thickness between the subject and atlas models. This layer thickness mismatch was propagated through the reconstruction process decreasing the parameter accuracy.
Receiver operating characteristic and location analysis of simulated near-infrared tomography images
Contrast-detail analysis characterizing diffuse optical fluorescence tomography image reconstruction
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This course will teach near-infrared light propagation modeling and image reconstruction in tissue using the freely distributed NIRFAST software package. NIRFAST is a widely-used, user-friendly package for modeling NIR light propagation in tissue and recovering images of optical parameters in arbitrarily-shaped tissue volumes. This course will use a combination of instructor lecturing and hands-on exercises to teach both conceptual and practical aspects of NIR imaging using the software. Attendees will be running and visualizing light propagation models within minutes and will also practice using image reconstruction algorithms for volumetric imaging of functional parameters such as hemoglobin concentration, oxygen saturation, water content, scattering parameters, as well as fluorescence and bioluminescence activity. The class will review the basic physics and biology of the approach, step through how the software works, and train attendees how to use the software through user exercises. More information about NIRFAST can be found at <a href="http://www.nirfast.org">http://www.nirfast.org</a>
This course teaches how to model light propagation with finite element programming, utilizing the easy-to-use style of MATLAB. The NIRFAST shareware software package developed at Dartmouth College (freely distributed for academic research) is used as the backbone to start modeling within the first few minutes of the course. The software incorporates image reconstruction algorithms which work for most diffuse tomography applications, and geometries. The class will review the basic physics of the approach, step through how the software works, and the visualization capabilities of the package will be explored for a number of geometries. Image reconstruction from multispectral data is demonstrated and image reconstruction from luminescent sources is also demonstrated.
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