Paper
16 July 2015 Monitoring the injured brain: registered, patient specific atlas models to improve accuracy of recovered brain saturation values
Michael Clancy, Antonio Belli, David Davies, Samuel J. E. Lucas, Zhangjie Su, Hamid Dehghani
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Abstract
The subject of superficial contamination and signal origins remains a widely debated topic in the field of Near Infrared Spectroscopy (NIRS), yet the concept of using the technology to monitor an injured brain, in a clinical setting, poses additional challenges concerning the quantitative accuracy of recovered parameters.

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.
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Michael Clancy, Antonio Belli, David Davies, Samuel J. E. Lucas, Zhangjie Su, and Hamid Dehghani "Monitoring the injured brain: registered, patient specific atlas models to improve accuracy of recovered brain saturation values", Proc. SPIE 9538, Diffuse Optical Imaging V, 95381C (16 July 2015); https://doi.org/10.1117/12.2183783
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KEYWORDS
Brain

Bone

Skin

Near infrared spectroscopy

Data modeling

Head

Tissues

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