Current Time-Resolved Near-Infrared Spectroscopy (trNIRS) fitting methods require the inclusion of an amplitude term, to account for the unknown gain in the measurements. This increases the number of fitting parameters and the potential for crosstalk between them. We propose a method that eliminates the need for the amplitude term by fitting the temporal derivative of the natural log of the distributions of time-of-flight. The new approach was tested on a large in silico dataset and the accuracy of the estimated cerebral oxygenation and total hemoglobin were 2.2±2.4% and 1.9±0.8%, respectively. Future work will include further validation with in vivo data.
Time-resolved Near-Infrared Spectroscopy (trNIRS) methods typically use multiple wavelengths and source-detector distances in conjunction with a solution of the diffusion approximation to quantify tissue blood content and oxygenation. This approach can be both computationally intensive and costly, as multiple detectors are required. We propose a novel two-layer fitting approach for multi-wavelength trNIRS, which uses a single detector while providing accurate estimates of cerebral oxygen saturation (ScO2) and hemoglobin content. The method uses a multi-step fitting algorithm to establish rough estimates of the absorption and scattering coefficients in the extracerebral layer and the brain, and subsequently refine those estimates, to improve accuracy while reducing crosstalk and complexity. Validation was conducted using Monte Carlo simulations in a realistic adult head model with appropriate optical properties at 680nm, 750nm, 800nm, and 830nm. The detector was located 30 mm anteriorly from the source, which was placed 50 mm above the right temple. Scalp oxygen saturation (SO2) (50%, 60%, and 70%) and ScO2 (40%-80%, 2% increments) were varied independently. The recovered ScO2 had a difference (mean±standard deviation) of 2.31±2.93% from inputted values, and cerebral total hemoglobin was recovered with a difference of 2.94±3.47%. Such high accuracy demonstrates the robustness of this computationally efficient two-layer fitting approach for analyzing multi-wavelength trNIRS measurements acquired with a single detector. Future work will involve validating the technique in tissue mimicking phantoms and animal studies.
Optical tomography imaging (OTI) of joints is challenging since light is highly scattered by tissue, leading to poor spatial resolution. Image quality can be improved by early-photons imaging; however, the image reconstruction will require more advanced forward models than the classic methods based on the diffusion approximation. We aim to use Monte-Carlo simulations as forward model for early-photons OTI. To test the feasibility, DICOM images of the hand of an adult male, obtained from an MRI, were imported into 3DSlicer where the bones and soft tissue were segmented. The MATLAB-based toolbox Iso2Mesh was used to generate a 3D volumetric mesh of the segmented image. Typical optical properties (i.e., absorption coefficient, scattering coefficient, anisotropic index, and refractive index) of bone and soft tissue were assigned to each node of the 3D mesh and light propagation was simulated using the Mesh-based MonteCarlo (MMC) toolbox. Our results show that our approach can reliable model propagation of early-photons in the highly heterogeneous human wrist.
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